Figure 1.
The landmarks for DPA. (1) Posterior tip of the greater trochanter. (2) Posterior superior iliac spine. (3) Midpoint between (1) and (2).
The need for accurate solar energy forecasting is paramount as the global push towards renewable energy intensifies. We aimed to provide a comprehensive analysis of the latest advancements in solar energy forecasting, focusing on Machine Learning (ML) and Deep Learning (DL) techniques. The novelty of this review lies in its detailed examination of ML and DL models, highlighting their ability to handle complex and nonlinear patterns in Solar Irradiance (SI) data. We systematically explored the evolution from traditional empirical, including machine learning (ML), and physical approaches to these advanced models, and delved into their real-world applications, discussing economic and policy implications. Additionally, we covered a variety of forecasting models, including empirical, image-based, statistical, ML, DL, foundation, and hybrid models. Our analysis revealed that ML and DL models significantly enhance forecasting accuracy, operational efficiency, and grid reliability, contributing to economic benefits and supporting sustainable energy policies. By addressing challenges related to data quality and model interpretability, this review underscores the importance of continuous innovation in solar forecasting techniques to fully realize their potential. The findings suggest that integrating these advanced models with traditional approaches offers the most promising path forward for improving solar energy forecasting.
Citation: Ayesha Nadeem, Muhammad Farhan Hanif, Muhammad Sabir Naveed, Muhammad Tahir Hassan, Mustabshirha Gul, Naveed Husnain, Jianchun Mi. AI-Driven precision in solar forecasting: Breakthroughs in machine learning and deep learning[J]. AIMS Geosciences, 2024, 10(4): 684-734. doi: 10.3934/geosci.2024035
| [1] | Wenya Shi, Xinpeng Yan, Zhan Huan . Faster free pseudoinverse greedy block Kaczmarz method for image recovery. Electronic Research Archive, 2024, 32(6): 3973-3988. doi: 10.3934/era.2024178 |
| [2] | Fang Wang, Weiguo Li, Wendi Bao, Li Liu . Greedy randomized and maximal weighted residual Kaczmarz methods with oblique projection. Electronic Research Archive, 2022, 30(4): 1158-1186. doi: 10.3934/era.2022062 |
| [3] | Jia-Min Luo, Hou-Biao Li, Wei-Bo Wei . Block splitting preconditioner for time-space fractional diffusion equations. Electronic Research Archive, 2022, 30(3): 780-797. doi: 10.3934/era.2022041 |
| [4] | Jingshi Li, Jiachuan Zhang, Guoliang Ju, Juntao You . A multi-mode expansion method for boundary optimal control problems constrained by random Poisson equations. Electronic Research Archive, 2020, 28(2): 977-1000. doi: 10.3934/era.2020052 |
| [5] | Bin Wang, Lin Mu . Viscosity robust weak Galerkin finite element methods for Stokes problems. Electronic Research Archive, 2021, 29(1): 1881-1895. doi: 10.3934/era.2020096 |
| [6] | Huimin Qu, Haiyan Xie, Qianying Wang . Multi-convolutional neural network brain image denoising study based on feature distillation learning and dense residual attention. Electronic Research Archive, 2025, 33(3): 1231-1266. doi: 10.3934/era.2025055 |
| [7] | Jiange Liu, Yu Chen, Xin Dai, Li Cao, Qingwu Li . MFCEN: A lightweight multi-scale feature cooperative enhancement network for single-image super-resolution. Electronic Research Archive, 2024, 32(10): 5783-5803. doi: 10.3934/era.2024267 |
| [8] | Zengyu Cai, Liusen Xu, Jianwei Zhang, Yuan Feng, Liang Zhu, Fangmei Liu . ViT-DualAtt: An efficient pornographic image classification method based on Vision Transformer with dual attention. Electronic Research Archive, 2024, 32(12): 6698-6716. doi: 10.3934/era.2024313 |
| [9] | Yantong Guo, Quansheng Wu, Xiaofeng Wang . An extension of high-order Kou's method for solving nonlinear systems and its stability analysis. Electronic Research Archive, 2025, 33(3): 1566-1588. doi: 10.3934/era.2025074 |
| [10] | Xuefei He, Kun Wang, Liwei Xu . Efficient finite difference methods for the nonlinear Helmholtz equation in Kerr medium. Electronic Research Archive, 2020, 28(4): 1503-1528. doi: 10.3934/era.2020079 |
The need for accurate solar energy forecasting is paramount as the global push towards renewable energy intensifies. We aimed to provide a comprehensive analysis of the latest advancements in solar energy forecasting, focusing on Machine Learning (ML) and Deep Learning (DL) techniques. The novelty of this review lies in its detailed examination of ML and DL models, highlighting their ability to handle complex and nonlinear patterns in Solar Irradiance (SI) data. We systematically explored the evolution from traditional empirical, including machine learning (ML), and physical approaches to these advanced models, and delved into their real-world applications, discussing economic and policy implications. Additionally, we covered a variety of forecasting models, including empirical, image-based, statistical, ML, DL, foundation, and hybrid models. Our analysis revealed that ML and DL models significantly enhance forecasting accuracy, operational efficiency, and grid reliability, contributing to economic benefits and supporting sustainable energy policies. By addressing challenges related to data quality and model interpretability, this review underscores the importance of continuous innovation in solar forecasting techniques to fully realize their potential. The findings suggest that integrating these advanced models with traditional approaches offers the most promising path forward for improving solar energy forecasting.
Posterior portion fractures of the acetabulum are the most frequent pattern of acetabular fractures, comprising approximately 19.4–35% of all acetabular fractures [1,2]. Based on the works of Judet and Letournel [3,4], accurate reduction and stable osteosynthesis with early mobilization have become the gold standard for the treatment of posterior acetabular fractures [5]. When faced with acetabular fractures involving the posterior portion, the Kocher-Langenbeck (K-L) approach is regarded as the classic approach for its direct visualization of the entire posterior column and posterior wall as well as indirect access to the true pelvis [6,7]. However, the extensive incision of the K-L approach results in high risks of medial femoral circumflex artery (MFCA) and sciatic nerve (SN) injury intraoperatively, especially for inexperienced surgeons [8,9]. In addition, the sharpness damage of muscles used in the K-L approach usually results in formation of heterotopic ossification (HO) and decreased muscle strength post-operatively [10,11].
Over recent decades, minimally-invasive surgery has become a clinical trend because of its expected benefits including less tissue trauma and early rehabilitation. In the standard K-L approach, the gluteus maximus is split to expose the posterior patterns of the acetabulum, and the short external rotators (SER) are dissected; moreover, the capsule of the hip would be dissected if necessary. As a result, numerous surgeons have focused their views on a minimally-invasive modification of the K-L approach [12,13,14,15,16].
In the present study, a novel minimally-invasive posterior approach, the DPA, based on the classic K-L approach, was designed to treat posterior acetabular portion fractures and the clinical outcomes were evaluated over a 1 year follow-up.
Inclusion criteria were: (1) acute acetabular fractures (<21 days); (2) acetabular fractures involving the posterior wall, posterior column, posterior wall and posterior column.
Exclusion criteria were: (1) open fractures; (2) acetabular fractures involving the anterior portion and quadrilateral plate which required an anterior approach; (3) follow-up for less than 1 year or loss to follow-up.
The study was approved by the Ethics Committee of the Third affiliated hospital of Southern Medical University, Guangzhou, China, and all procedures involving human participants were carried out in accordance with the ethical standards of the institutional and/or national research committee and the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
Between January 2016 and June 2017, 22 consecutive patients with acetabular posterior portion fractures treated surgically in our trauma center using the DPA were retrospectively analyzed. Finally, 10 patients were included in this study according to our inclusion and exclusion criteria. Before surgery, all patients underwent radiographic examinations including X-rays (anteroposterior (AP) view and Judet view) and computed tomography (CT) scans (slice thickness of 1 mm) with three-dimensional (3D) reconstructions.
All surgical procedures were performed by the same senior surgeon in this study.
The patients with hip dislocation underwent a closed reduction and supracondylar traction as soon as possible after admission. All patients received routine antibiotic prophylaxis 30 min prior to operation. Under general anesthesia, the patient was placed in a prone position on the radiolucent operating table, with hip extension and knee flexion to avoid excessive tension on the SN. The feasibility of obtaining AP and Judet oblique views captured by C-arm fluoroscopy was verified for each patient before surgical draping. As shown in Figure 1, the posterior superior iliac spine and the posterior margin of the tip of the greater trochanter were treated as the landmarks for incision.
Essentially, the skin incision of the DPA is similar to the proximal portion of the classical K-L incision [7]. A straight skin incision was initiated from the midpoint between the posterior superior iliac spine and the posterior tip of the greater trochanter, and extended towards the latter approximately 8–11 cm (Figure 2A, E). The gluteus maximus muscles were dissected bluntly along the muscle fibers. Neither the proximal part of the gluteus maximus attaching to the iliac crest nor the distal part inserting into the femur was detached. Holding the layers of the gluteus maximus apart using retractors, the piriformis and gluteus medius were exposed and the interval between these two muscles was identified (Figure 2B, F). The SN could be distinguished by its course anterior to the piriformis. With anterosuperior retraction of the gluteus medius muscle, the superior gluteal neurovascular bundle contiguous to the greater sciatic notch could be defined in the proximal direction (Figure 2C, G). Instead of dividing the SERs and abductors, the interval was further developed by retracting the gluteus medius anterosuperiorly and the piriformis posteroinferiorly. In this way, access to the entire acetabular posterior wall, partial hip capsule and the great mass of the posterior column (from the superior margin of the greater sciatic foramen to the sciatic spine) was achieved (Figure 2D, H). Nevertheless, in the region of the greater sciatic notch, the surgeon needed to manipulate carefully to avoid intraoperative damage to the superior gluteal neurovascular bundle and the SN. This procedure created a space between the undersurface of the piriformis muscle and the posterior wall of the acetabulum, allowing reduction of acetabular posterior portion fractures under direct visualization.
For posterior column fractures with or without posterior wall fractures, fractures of the posterior column should be reduced and fixed first. On the other hand, for posterior wall fractures, any hematoma and hip capsule debris could be cleaned up directly by traction of the fragments, and soft tissue attached to the displaced fragments could be preserved. Capsulotomy of the hip joint was no longer necessary in this approach (Figure 3A). Moreover, as a benefit of the extensive exposure of the intra-articular surface, a marginal compressive fracture of the hip joint could be treated by bone grafting if necessary. After that, anatomic reduction and temporary fixation with K-wires of the posterior wall fragments was performed (Figure 3B). Finally, the appropriate reconstruction plates were chosen to contour adequately to accommodate the shape of the posterior wall and inserted to fix the posterior rim of the acetabulum (Figure 3C). A thorough irrigation was performed and closed suction drainage was placed before wound closure.
The whole procedure was performed between the space of the posterior column through windows between the gluteus medius and the piriformis muscles superiorly and between the SERs and retro-acetabular surface without dividing the rotators and abductors. The fractures of the posterior wall were appropriately stabilized with reconstruction plates. The reduction was assessed from the interlocking of the ragged fracture ends perioperatively without joint incision and division of rotators. The safe insertion of the screws was further assessed by confirming the fixation under an image intensifier.
After surgery, the function of the SN was examined once the patient recovered from anesthesia. Routine antibiotic prophylaxis was administered for 24 h postoperatively. Low molecular weight heparin was administered daily for 2 weeks to prevent thrombosis. The suction drain was removed once the aspirated volume was less than 50 mL/d. X-ray examinations and CT scans with 3D reconstruction were performed in all patients postoperatively. Active and passive movement of the hip was started as soon as medically possible. Toe-touch weight bearing with the help of a walker or crutches was allowed at 4–6 weeks, and gradually full weight bearing was started 3–4 months after surgery.
Blood loss, operative time, time to surgery, reduction quality and complications were recorded. Patients received routine postoperative follow-up at 1, 3, 6, and 12 months, and annually thereafter. According to the Matta scoring system [17,18,19], the reduction of fractures was evaluated by measuring the postoperative residual displacements on postoperative radiographs, with reduction quality classified as anatomic (<1 mm), imperfect (1–3 mm), or poor (>3 mm). At the final follow-up the patients were evaluated clinically building on Merle d'Aubigne and Postel scoring which had been modified by Matta [17,18,20], ranging from excellent (18 points), good (15–17 points), and fair (14 or 13 points), to poor (<13 points).
Preoperative, intraoperative and postoperative data were recorded. Statistical analysis was mainly conducted using SPSS Statistics (SPSS Inc., Chicago, IL, USA). Patient characteristics and results are described by mean, standard deviation, and percentage. One sample T test was used to measure the significant of operative time and blood loss between our data and previous study of K-L approach.
Between June 2016 and June 2017, 22 consecutive patients with posterior portion fractures received surgical treatment through the DPA at our medical center. However, due to incomplete data, 12 patients were excluded from this study. Of the remaining 10 patients, seven were male and three were female, with a mean age of 37 years (range: 17–54 years; SD: 12.3 years). Six patients were injured in traffic accidents and four had a history of falling. According to the Judet and Letournel classification, six patients were diagnosed with isolated posterior wall fractures (60%), and four patients with posterior column and posterior wall fractures (40%). Additionally, in six (60%) patients, posterior wall fractures were associated with posterior hip dislocation. The dislocations were reduced as soon as possible. And preoperative supracondylar traction was performed, using weights up to 10 to 12 kg, until surgery. No abnormalities or deficits of the lower limbs were observed in any patients upon neurovascular examination. None of patients had associated injuries of the extremities or had fractures of the head of the femur. All patients were taken for operation as soon as their general medical condition permitted. The average time between injury and surgery was 6 days (range: 4–11 days; SD: 2.5 days) (Table 1).
| Variable | Value (mean ± SD) | Percent (%) |
| Gender | ||
| Male | 7 | 70 |
| Female | 3 | 30 |
| Mean age (years) | 37 ± 12.3 (range: 17–54) | - |
| Acetabular fracture type | ||
| Posterior wall | 6 | 60 |
| Posterior column and posterior wall | 4 | 40 |
| Mechanism of injury | ||
| Fall from a height | 4 | 40 |
| Traffic accident | 6 | 60 |
| Associated injury | ||
| Posterior hip dislocation | 6 | 60 |
| Extremity fractures | 0 | 0 |
| Mean time from injury to surgery (days) | 6 ± 2.5 (range: 4–11) | - |
DownLoad:
CSV
For all operations, the mean length of the incision was 9.6 cm (rang: 8–11 cm; SD: 1.4 cm) with a mean operative time of 50 min (range: 35–80 min; SD: 14.5 min). Mean blood loss during surgery was 310 mL (range: 200–440 mL; SD: 82.6 mL). As shown in Figure 4, the operative time and blood loss of DPA exhibited significant decline (p < 0.01) when compared that in previous report of traditional K-L approach [21]. All fractures exhibited radiological evidence of fracture union within 12 weeks after surgery (mean fracture union time, 9.3 weeks; range: 8–12 weeks; SD: 1.0 week). In the post-operative evaluations, the postoperative reduction quality was graded as anatomic in seven patients (70%), imperfect in three patients (30%), and poor in zero patients (0%) according to the Matta scoring system [17,18,19]. No patient exhibited loss of reduction at the end of the follow-up period (Figure 5). The clinical outcomes based on the modified Merle d'Aubigné scoring system [17,18,20] were as follows: excellent in six patients (60%), good in two patients (20%), fair in two patients (20%), and poor in zero patients (0%) (Table 2).
| Postoperative data | Value mean ± SD | Percent (%) |
| Length of incision(cm) | 9.6 ± 1.4 (range: 8–11) | - |
| Mean operative time (min) | 50 ± 14.5 (range: 35–80) | - |
| Mean blood loss (ml) | 310 ± 82.6 (range: 200–440) | - |
| Mean union time (weeks) | 9.3 ± 1.0 (range: 8–12) | - |
| Reduction quality (Matta) | ||
| Anatomic (<1 mm) | 7 | 70 |
| Imperfect (1–3 mm) | 3 | 30 |
| Poor (>3 mm) | 0 | 0 |
| The modified Merle d'Aubigné score | ||
| Excellent | 6 | 60 |
| Good | 2 | 20 |
| Fair | 2 | 20 |
| Poor | 0 | 0 |
| Complications | ||
| Heterotopic ossification | 1 | 10 |
| Femoral head necrosis | 1 | 10 |
DownLoad:
CSV
HO developed in one patient but the range of motion (ROM) of his hip recovered to a similar range as the uninjured hip by the end of the follow-up period. Femoral head necrosis occurred in one case at 9 months after surgery with the diagnosis of complicated posterior wall fractures associated with posterior hip dislocation. The patient received total hip arthroplasty at one year after surgery. Intraoperative radial measurements exhibited an accurate reduction in all patients and no screws were presented in the acetabular fossa. No iatrogenic damage to the SN or the superior gluteal neurovascular bundle and no posttraumatic arthritis was observed postoperatively. There were no instances of wound infection, implant loosening or breakage, recurrent dislocation, deep vein thrombosis, or revision fixation (Table 2).
Over recent decades, based on the contributions of Judet et al. [3], treatment strategies for acetabular fractures involving the posterior portion have changed from non-operative treatments to surgical treatments. However, for most surgeons, operating on posterior acetabular fractures can be very challenging due to the complex adjacent arteries and nerves [8,22]. Consequently, conventional surgical approaches required extensive exposure and could result in severe trauma and high risks of complications such as HO or iatrogenic nerve or artery injury [8,10,23]. In this study, inspired by the traditional Kocher approach, we designed a novel minimally-invasive posterior approach to treat fractures of the posterior portion of the acetabulum. Satisfactory clinical outcomes were obtained with this reduced approach, with less soft tissue trauma and reduced risks of injury to the iatrogenic SERs, MFCA and SN.
In classic treatment of posterior acetabular fractures, the K-L approach is considered the ideal approach, with the benefit of remarkable exposure. Nevertheless, a 15–20 cm incision should be performed, and splitting of the SERs, abductors, and even the hip capsule is essential for this approach, therefore, extensive intra-operative blood loss and lengthy operative time are always involved [24]. Collinge et al. reported a 644 mL mean blood loss and 258 min mean operative time in 33 operations performed by the classic K-L approach [25]. In addition, for the trochanteric flip osteotomy associated with the K-L approach, although this can improve visualization of the superior acetabulum and enhance the protective on the MFCA, it is associated with increased blood loss and operative time, according to a recent study [16,26]. Furthermore, a rate of trochanter nonunion ranging from 2.1–2.4% is reported after trochanteric flip osteotomy [27,28]. In our study, the DPA provided direct visualization of the entire posterior wall, part of the hip capsule and the posterior column between the space of the gluteus medius and the piriformis, and yet required an incision of only 8–11 cm. Additionally, the mean operative time of surgery via the DPA was 50 min and mean blood loss was 310 mL. These data suggest that the DPA could be considered a minimally-invasive modification of the traditional Kocher approach.
Physically, the SERs, abductors and gluteus muscles contribute to the strength of hip joints, and intraoperative preservation of these components is related positively with post-operative rehabilitation. Matta and Ceylan proposed that the reduced muscular split is associated with improvement of hip abduction in their analyses [29,30]. Simultaneously, Borelli et al. reported a hip-muscle strength deficit post-operatively in 15 operations performed using the classic K-L incision [11]. Consequently, as the DPA utilizes blunt division of the SERs, abductors and gluteus muscles, the DPA can provide opportunities for stable and fast post-operative rehabilitation of the hip joint.
For most surgeons, protection of the MFCA is one of the vital key points for the posterior approach of acetabular fracture, since iatrogenic injury of the MFCA may result in avascular necrosis of the femoral head [31]. Meanwhile, recent investigations suggested that the gluteal artery is involved in the blood supply to the femoral head [32]. Moreover, iatrogenic injury of the SN can catastrophically destroy the functions of the lower limbs. According to recent analyses, a rate of 3–18% of SN injury was associated with surgery via the K-L approach with or without trochanteric flip osteotomy [33,34,35]. In our design, the manipulation space of the DPA was created through blunt separation of the piriformis muscle, and the gluteus medius, the MFCA, SN, superior and inferior gluteal neurovascular bundles could be well protected synchronously by retractors when compared to both the K-L approach and the osteotomy assistant K-L approach according to the anatomical characteristics of the posterior acetabulum. As a result, there were no instances of iatrogenic injury in our study. Nevertheless, one of our patients presented with femoral head necrosis; however, since the patient experienced extensive dislocation time before transfer to our medical center (11 days), it is our view that the major cause of femoral head necrosis may be the result of dislocation rather than iatrogenic injury of the supplying arteries.
HO is one of the unfortunate possible complications in orthopedics which result in chronic pain and unsatisfactory hip function [36]. Negrin et al. reported an HO rate of 37.9% post-operatively, in the analysis of 167 cases treated by the K-L approach [37]. Based on past investigations, HO is believed to be strongly related to the incision and split division of muscles [15,38]. Although there was one patient who presented with HO at 3 months post-operatively, the DPA approach may provide a low risk of HO due to the blunt division performed during surgery.
In recent reports, some minimally-invasive approaches similar to DPA have been described. SER-sparing modifications of the K-L approach for selected posterior portion fractures have been reported by Magu [13], Josten [14] and Sarlak [15]. A mean operative time of 73.2 min and mean blood loss of 187 mL in 14 cases were described by Magu and the reduction quality measurements showed an excellent outcome in ten patients (71%), good in three (22%) and fair in one (7%) [13]. In 2011, Josten et al. applied the same approach to stabilize nine patients with a mean operative time of 94 min, the reduction quality was classified as anatomic in five patients, imperfect in three, and poor in one patient [14]. In our results, the DPA resulted in similar operative time and blood loss; in addition, the modified Merle d'Aubigné scoring measurements presented approximate results when compared to the recent reported minimally-invasive approaches for posterior acetabular fractures. Our data suggested that the DPA may be a safe and minimally-invasive technique for the treatment of posterior acetabular fractures. Although the DPA exhibited advantages and excellent follow-up results in this study, the limitations of small case volume and short follow up time remain. Simultaneously, there are no data comparing the DPA and the K-L approach. Therefore, in further studies, a larger number of cases and longer follow-up time is necessary, while comparison of the DPA with other classic approaches for treatment of posterior acetabular factures will also be necessary.
In this study, a novel minimally-invasive approach for the treatment of posterior acetabular fracture was described and evaluated by 1 year follow-up. In addition to the low blood loss, short operative time and low risk of iatrogenic injury, the patients who underwent the DPA exhibited positive functional recovery in follow-up. Therefore, we conclude that the DPA is an effective, safe and minimally-invasive technique for the treatment of posterior acetabular fractures.
This study was supported by the National Natural Science Foundation of China (81772428), Special Program of Guangdong Frontier and Key Technological Innovation (No. 2015B010125006) and the "Clinical Research Initiative" of Southern Medical University (No. LC2016ZD032).
All authors declare no potential conflicts of interest.
| [1] |
EI Hendouzi A, Bourouhou A (2020) Solar Photovoltaic Power Forecasting. J Electr Comput Eng 2020: 1–21. https://doi.org/10.1155/2020/8819925 doi: 10.1155/2020/8819925
|
| [2] | Ürkmez M, Kallesøe C, Dimon Bendtsen J, et al. (2022) Day-ahead pv power forecasting for control applications. IECON 2022, 48th Annual Conference of the IEEE Industrial Electronics Society, Brussels, Belgium, 1–6. https://doi.org/10.1109/IECON49645.2022.9968709 |
| [3] |
Cheng S, Prentice IC, Huang Y, et al. (2022) Data-driven surrogate model with latent data assimilation: Application to wildfire forecasting. J Comput Phys 464: 111302. https://doi.org/10.1016/J.JCP.2022.111302 doi: 10.1016/J.JCP.2022.111302
|
| [4] |
Cheng S, Jin Y, Harrison SP, et al. (2022) Parameter Flexible Wildfire Prediction Using Machine Learning Techniques: Forward and Inverse Modelling. Remote Sens 14: 3228. https://doi.org/10.3390/RS14133228 doi: 10.3390/RS14133228
|
| [5] |
Zhong C, Cheng S, Kasoar M, et al. (2023) Reduced-order digital twin and latent data assimilation for global wildfire prediction. Nat Hazard Earth Sys 23: 1755–1768. https://doi.org/10.5194/NHESS-23-1755-2023 doi: 10.5194/NHESS-23-1755-2023
|
| [6] |
Gupta P, Singh R (2021) PV power forecasting based on data-driven models: a review. Int J Sustain Eng 14: 1733–1755. https://doi.org/10.1080/19397038.2021.1986590 doi: 10.1080/19397038.2021.1986590
|
| [7] |
López Santos M, García-Santiago X, Echevarría Camarero F, et al. (2022) Application of Temporal Fusion Transformer for Day-Ahead PV Power Forecasting. Energies 15: 5232. https://doi.org/10.3390/EN15145232 doi: 10.3390/EN15145232
|
| [8] | Kanchana W, Sirisukprasert S (2020) PV Power Forecasting with Holt-Winters Method. 2020 8th International Electrical Engineering Congress (IEECON), 1–4. https://doi.org/10.1109/IEECON48109.2020.229517 |
| [9] | Dhingra S, Gruosso G, Gajani GS (2023) Solar PV Power Forecasting and Ageing Evaluation Using Machine Learning Techniques. IECON 2023 49th Annual Conference of the IEEE Industrial Electronics Society, 1–6. https://doi.org/10.1109/IECON51785.2023.10312446 |
| [10] |
Hanif MF, Naveed MS, Metwaly M, et al. (2021) Advancing solar energy forecasting with modified ANN and light GBM learning algorithms. AIMS Energy 12: 350–386. https://doi.org/10.3934/ENERGY.2024017 doi: 10.3934/ENERGY.2024017
|
| [11] |
Hanif MF, Siddique MU, Si J, et al. (2021) Enhancing Solar Forecasting Accuracy with Sequential Deep Artificial Neural Network and Hybrid Random Forest and Gradient Boosting Models across Varied Terrains. Adv Theory Simul 7: 2301289. https://doi.org/10.1002/ADTS.202301289 doi: 10.1002/ADTS.202301289
|
| [12] | Musafa A, Priyadi A, Lystianingrum V, et al. (2023) Stored Energy Forecasting of Small-Scale Photovoltaic-Pumped Hydro Storage System Based on Prediction of Solar Irradiance, Ambient Temperature, and Rainfall Using LSTM Method. IECON 2023 49th Annual Conference of the IEEE Industrial Electronics, 1–6. https://doi.org/10.1109/IECON51785.2023.10311982 |
| [13] |
Konstantinou M, Peratikou S, Charalambides AG (2021) Solar Photovoltaic Forecasting of Power Output Using LSTM Networks. Atmosphere 12: 124. https://doi.org/10.3390/ATMOS12010124 doi: 10.3390/ATMOS12010124
|
| [14] | Jasiński M, Leonowicz Z, Jasiński J, et al. (2023) PV Advancements & Challenges: Forecasting Techniques, Real Applications, and Grid Integration for a Sustainable Energy Future. 2023 IEEE International Conference on Environment and Electrical Engineering and 2023 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I & CPS Europe), Spain, 1–5. https://doi.org/10.1109/EEEIC/ICPSEUROPE57605.2023.10194796 |
| [15] |
Cantillo-Luna S, Moreno-Chuquen R, Celeita D, et al. (2023) Deep and Machine Learning Models to Forecast Photovoltaic Power Generation. Energies 16: 4097. https://doi.org/10.3390/EN16104097 doi: 10.3390/EN16104097
|
| [16] | Kaushik AR, Padmavathi S, Gurucharan KS, et al. (2023) Performance Analysis of Regression Models in Solar PV Forecasting. 2023 3rd International Conference on Artificial Intelligence and Signal Processing (AISP), India, 1–5. https://doi.org/10.1109/AISP57993.2023.10134943 |
| [17] |
Halabi LM, Mekhilef S, Hossain M (2018) Performance evaluation of hybrid adaptive neuro-fuzzy inference system models for predicting monthly global solar radiation. Appl Energy 213: 247–261. https://doi.org/10.1016/J.APENERGY.2018.01.035 doi: 10.1016/J.APENERGY.2018.01.035
|
| [18] |
Zhang G, Wang X, Du Z (2015) Research on the Prediction of Solar Energy Generation based on Measured Environmental Data. Int J U e-Service Sci Technol 8: 385–402. https://doi.org/10.14257/IJUNESST.2015.8.5.37 doi: 10.14257/IJUNESST.2015.8.5.37
|
| [19] | Peng Q, Zhou X, Zhu R, et al. (2023) A Hybrid Model for Solar Radiation Forecasting towards Energy Efficient Buildings. 2023 7th International Conference on Green Energy and Applications (ICGEA), 7–12. https://doi.org/10.1109/ICGEA57077.2023.10125987 |
| [20] | Salisu S, Mustafa MW, Mustapha M (2018) Predicting Global Solar Radiation in Nigeria Using Adaptive Neuro-Fuzzy Approach. Recent Trends in Information and Communication Technology. IRICT 2017. Lecture Notes on Data Engineering and Communications Technologies, 5: 513–521. https://doi.org/10.1007/978-3-319-59427-9_54 |
| [21] |
Kaur A, Nonnenmacher L, Pedro HTC, et al. (2016) Benefits of solar forecasting for energy imbalance markets. Renewable Energy 86: 819–830. https://doi.org/10.1016/J.RENENE.2015.09.011 doi: 10.1016/J.RENENE.2015.09.011
|
| [22] |
Yang D, Li W, Yagli GM, et al. (2021) Operational solar forecasting for grid integration: Standards, challenges, and outlook. Sol Energy 224: 930–937. https://doi.org/10.1016/J.SOLENER.2021.04.002 doi: 10.1016/J.SOLENER.2021.04.002
|
| [23] | Shi G, Eftekharnejad S (2016) Impact of solar forecasting on power system planning. 2016 North American Power Symposium (NAPS), 1–6. https://doi.org/10.1109/NAPS.2016.7747909 |
| [24] |
Shi J, Guo J, Zheng S (2012) Evaluation of hybrid forecasting approaches for wind speed and power generation time series. Renewable Sustainable Energy Rev 16: 3471–3480. https://doi.org/10.1016/j.rser.2012.02.044 doi: 10.1016/j.rser.2012.02.044
|
| [25] |
Mohanty S, Patra PK, Sahoo SS, et al. (2017) Forecasting of solar energy with application for a growing economy like India: Survey and implication. Renewable Sustainable Energy Rev 78: 539–553. https://doi.org/10.1016/J.RSER.2017.04.107 doi: 10.1016/J.RSER.2017.04.107
|
| [26] |
Sweeney C, Bessa RJ, Browell J, et al. (2020) The future of forecasting for renewable energy. Wiley Interdiscip Rev Energy Environ 9: e365. https://doi.org/10.1002/WENE.365 doi: 10.1002/WENE.365
|
| [27] |
Brancucci Martinez-Anido C, Botor B, Florita AR, et al. (2016) The value of day-ahead solar power forecasting improvement. Sol Energy 129: 192–203. https://doi.org/10.1016/J.SOLENER.2016.01.049 doi: 10.1016/J.SOLENER.2016.01.049
|
| [28] |
Inman RH, Pedro HTC, Coimbra CFM (2013) Solar forecasting methods for renewable energy integration. Prog Energy Combust Sci 39: 535–576. https://doi.org/10.1016/J.PECS.2013.06.002 doi: 10.1016/J.PECS.2013.06.002
|
| [29] |
Cui M, Zhang J, Hodge BM, et al. (2018) A Methodology for Quantifying Reliability Benefits from Improved Solar Power Forecasting in Multi-Timescale Power System Operations. IEEE T Smart Grid 9: 6897–6908. https://doi.org/10.1109/TSG.2017.2728480 doi: 10.1109/TSG.2017.2728480
|
| [30] |
Wang H, Lei Z, Zhang X, et al. (2019) A review of deep learning for renewable energy forecasting. Energy Convers Manage 198: 111799. https://doi.org/10.1016/J.ENCONMAN.2019.111799 doi: 10.1016/J.ENCONMAN.2019.111799
|
| [31] |
Aupke P, Kassler A, Theocharis A, et al. (2021) Quantifying Uncertainty for Predicting Renewable Energy Time Series Data Using Machine Learning. Eng Proc 5: 50. https://doi.org/10.3390/ENGPROC2021005050 doi: 10.3390/ENGPROC2021005050
|
| [32] |
Rajagukguk RA, Ramadhan RAA, Lee HJ (2020) A Review on Deep Learning Models for Forecasting Time Series Data of Solar Irradiance and Photovoltaic Power. Energies 13: 6623. https://doi.org/10.3390/EN13246623 doi: 10.3390/EN13246623
|
| [33] | SETO 2020—Artificial Intelligence Applications in Solar Energy. Available from: https://www.energy.gov/eere/solar/seto-2020-artificial-intelligence-applications-solar-energy. |
| [34] |
Freitas S, Catita C, Redweik P, et al. (2015) Modelling solar potential in the urban environment: State-of-the-art review. Renewable Sustainable Energy Rev 41: 915–931. https://doi.org/10.1016/J.RSER.2014.08.060 doi: 10.1016/J.RSER.2014.08.060
|
| [35] |
Gürtürk M, Ucar F, Erdem M (2022) A novel approach to investigate the effects of global warming and exchange rate on the solar power plants. Energy 239: 122344. https://doi.org/10.1016/J.ENERGY.2021.122344 doi: 10.1016/J.ENERGY.2021.122344
|
| [36] |
Gaye B, Zhang D, Wulamu A (2021) Improvement of Support Vector Machine Algorithm in Big Data Background. Math Probl Eng 2021: 5594899. https://doi.org/10.1155/2021/5594899 doi: 10.1155/2021/5594899
|
| [37] |
Yogambal Jayalakshmi N, Shankar R, Subramaniam U, et al. (2021) Novel Multi-Time Scale Deep Learning Algorithm for Solar Irradiance Forecasting. Energies 14: 2404. https://doi.org/10.3390/EN14092404 doi: 10.3390/EN14092404
|
| [38] |
Benti NE, Chaka MD, Semie AG (2023) Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects. Sustainability 15: 7087. https://doi.org/10.3390/SU15097087 doi: 10.3390/SU15097087
|
| [39] |
Li J, Ward JK, Tong J, et al. (2016) Machine learning for solar irradiance forecasting of photovoltaic system. Renewable Energy 90: 542–553. https://doi.org/10.1016/J.RENENE.2015.12.069 doi: 10.1016/J.RENENE.2015.12.069
|
| [40] |
Long H, Zhang Z, Su Y (2014) Analysis of daily solar power prediction with data-driven approaches. Appl Energy 126: 29–37. https://doi.org/10.1016/J.APENERGY.2014.03.084 doi: 10.1016/J.APENERGY.2014.03.084
|
| [41] |
Jebli I, Belouadha FZ, Kabbaj MI, et al. (2021) Prediction of solar energy guided by pearson correlation using machine learning. Energy 224: 120109. https://doi.org/10.1016/J.ENERGY.2021.120109 doi: 10.1016/J.ENERGY.2021.120109
|
| [42] |
Khandakar A, Chowdhury MEH, Kazi MK, et al. (2019) Machine Learning Based Photovoltaics (PV) Power Prediction Using Different Environmental Parameters of Qatar. Energies 12: 2782. https://doi.org/10.3390/EN12142782 doi: 10.3390/EN12142782
|
| [43] |
Kim SG, Jung JY, Sim MK (2019) A Two-Step Approach to Solar Power Generation Prediction Based on Weather Data Using Machine Learning. Sustainability 11: 1501. https://doi.org/10.3390/SU11051501 doi: 10.3390/SU11051501
|
| [44] |
Gutiérrez L, Patiño J, Duque-Grisales E (2021) A Comparison of the Performance of Supervised Learning Algorithms for Solar Power Prediction. Energies 14: 4424. https://doi.org/10.3390/EN14154424 doi: 10.3390/EN14154424
|
| [45] |
Wang Z, Xu Z, Zhang Y, et al. (2020) Optimal Cleaning Scheduling for Photovoltaic Systems in the Field Based on Electricity Generation and Dust Deposition Forecasting. IEEE J Photovolt 10: 1126–1132. https://doi.org/10.1109/JPHOTOV.2020.2981810 doi: 10.1109/JPHOTOV.2020.2981810
|
| [46] |
Massaoudi M, Chihi I, Sidhom L, et al. (2021) An Effective Hybrid NARX-LSTM Model for Point and Interval PV Power Forecasting. IEEE Access 9: 36571–36588. https://doi.org/10.1109/ACCESS.2021.3062776 doi: 10.1109/ACCESS.2021.3062776
|
| [47] |
Arora I, Gambhir J, Kaur T (2021) Data Normalisation-Based Solar Irradiance Forecasting Using Artificial Neural Networks. Arab J Sci Eng 46: 1333–1343. https://doi.org/10.1007/S13369-020-05140-Y/METRICS doi: 10.1007/S13369-020-05140-Y/METRICS
|
| [48] |
Alipour M, Aghaei J, Norouzi M, et al. (2020) A novel electrical net-load forecasting model based on deep neural networks and wavelet transform integration. Energy 205: 118106. https://doi.org/10.1016/J.ENERGY.2020.118106 doi: 10.1016/J.ENERGY.2020.118106
|
| [49] |
Zolfaghari M, Golabi MR (2021) Modeling and predicting the electricity production in hydropower using conjunction of wavelet transform, long short-term memory and random forest models. Renewable Energy 170: 1367–1381. https://doi.org/10.1016/J.RENENE.2021.02.017 doi: 10.1016/J.RENENE.2021.02.017
|
| [50] |
Li FF, Wang SY, Wei JH (2018) Long term rolling prediction model for solar radiation combining empirical mode decomposition (EMD) and artificial neural network (ANN) techniques. J Renewable Sustainable Energy 10: 013704. https://doi.org/10.1063/1.4999240 doi: 10.1063/1.4999240
|
| [51] | Wang S, Guo Y, Wang Y, et al. (2021) A Wind Speed Prediction Method Based on Improved Empirical Mode Decomposition and Support Vector Machine. IOP Conference Series: Earth and Environmental Science, IOP Publishing. 680: 012012. https://doi.org/10.1088/1755-1315/680/1/012012 |
| [52] |
Moreno SR, dos Santos Coelho L (2018) Wind speed forecasting approach based on Singular Spectrum Analysis and Adaptive Neuro Fuzzy Inference System. Renewable Energy 126: 736–754. https://doi.org/10.1016/J.RENENE.2017.11.089 doi: 10.1016/J.RENENE.2017.11.089
|
| [53] |
Zhang Y, Le J, Liao X, et al. (2019) A novel combination forecasting model for wind power integrating least square support vector machine, deep belief network, singular spectrum analysis and locality-sensitive hashing. Energy 168: 558–572. https://doi.org/10.1016/J.ENERGY.2018.11.128 doi: 10.1016/J.ENERGY.2018.11.128
|
| [54] | Espinar B, Aznarte JL, Girard R, et al. (2010) Photovoltaic Forecasting: A state of the art. 5th European PV-hybrid and mini-grid conference. OTTI-Ostbayerisches Technologie-Transfer-Institut. |
| [55] | Moreno-Munoz A, De La Rosa JJG, Posadillo R, et al. (2008) Very short term forecasting of solar radiation. 2008 33rd IEEE Photovoltaic Specialists Conference, San Diego, CA, USA. https://doi.org/10.1109/PVSC.2008.4922587 |
| [56] |
Anderson D, Leach M (2004) Harvesting and redistributing renewable energy: on the role of gas and electricity grids to overcome intermittency through the generation and storage of hydrogen. Energy Policy 32: 1603–1614. https://doi.org/10.1016/S0301-4215(03)00131-9 doi: 10.1016/S0301-4215(03)00131-9
|
| [57] |
Zhang J, Zhao L, Deng S, et al. (2017) A critical review of the models used to estimate solar radiation. Renewable Sustainable Energy Rev 70: 314–329. https://doi.org/10.1016/J.RSER.2016.11.124 doi: 10.1016/J.RSER.2016.11.124
|
| [58] |
Coimbra CFM, Kleissl J, Marquez R (2013) Overview of Solar-Forecasting Methods and a Metric for Accuracy Evaluation. Sol Energy Forecast Resour Assess, 171–194. https://doi.org/10.1016/B978-0-12-397177-7.00008-5 doi: 10.1016/B978-0-12-397177-7.00008-5
|
| [59] |
Miller SD, Rogers MA, Haynes JM, et al. (2018) Short-term solar irradiance forecasting via satellite/model coupling. Sol Energy 168: 102–117. https://doi.org/10.1016/J.SOLENER.2017.11.049 doi: 10.1016/J.SOLENER.2017.11.049
|
| [60] |
Kumari P, Toshniwal D (2021) Deep learning models for solar irradiance forecasting: A comprehensive review. J Cleaner Prod 318: 128566. https://doi.org/10.1016/J.JCLEPRO.2021.128566 doi: 10.1016/J.JCLEPRO.2021.128566
|
| [61] |
Hassan GE, Youssef ME, Mohamed ZE, et al. (2016) New Temperature-based Models for Predicting Global Solar Radiation. Appl Energy 179: 437–450. https://doi.org/10.1016/J.APENERGY.2016.07.006 doi: 10.1016/J.APENERGY.2016.07.006
|
| [62] |
Angstrom A (1924) Solar and terrestrial radiation. Report to the international commission for solar research on actinometric investigations of solar and atmospheric radiation. Q J R Meteorol Soc 50: 121–126. https://doi.org/10.1002/QJ.49705021008 doi: 10.1002/QJ.49705021008
|
| [63] |
Samuel TDMA (1991) Estimation of global radiation for Sri Lanka. Sol Energy 47: 333–337. https://doi.org/10.1016/0038-092X(91)90026-S doi: 10.1016/0038-092X(91)90026-S
|
| [64] |
Ögelman H, Ecevit A, Tasdemiroǧlu E (1984) A new method for estimating solar radiation from bright sunshine data. Sol Energy 33: 619–625. https://doi.org/10.1016/0038-092X(84)90018-5 doi: 10.1016/0038-092X(84)90018-5
|
| [65] |
Badescu V, Gueymard CA, Cheval S, et al. (2013) Accuracy analysis for fifty-four clear-sky solar radiation models using routine hourly global irradiance measurements in Romania. Renewable Energy 55: 85–103. https://doi.org/10.1016/J.RENENE.2012.11.037 doi: 10.1016/J.RENENE.2012.11.037
|
| [66] |
Mecibah MS, Boukelia TE, Tahtah R, et al. (2014) Introducing the best model for estimation the monthly mean daily global solar radiation on a horizontal surface (Case study: Algeria). Renewable Sustainable Energy Rev 36: 194–202. https://doi.org/10.1016/J.RSER.2014.04.054 doi: 10.1016/J.RSER.2014.04.054
|
| [67] |
Hargreaves GH, Samani ZA (1982) Estimating Potential Evapotranspiration. J Irrig Drain Div 108: 225–230. https://doi.org/10.1061/JRCEA4.0001390 doi: 10.1061/JRCEA4.0001390
|
| [68] |
Bristow KL, Campbell GS (1984) On the relationship between incoming solar radiation and daily maximum and minimum temperature. Agric For Meteorol 31: 159–166. https://doi.org/10.1016/0168-1923(84)90017-0 doi: 10.1016/0168-1923(84)90017-0
|
| [69] |
Chen JL, He L, Yang H, et al. (2019) Empirical models for estimating monthly global solar radiation: A most comprehensive review and comparative case study in China. Renewable Sustainable Energy Rev 108: 91–111. https://doi.org/10.1016/j.rser.2019.03.033 doi: 10.1016/j.rser.2019.03.033
|
| [70] |
Chen Y, Zhang S, Zhang W, et al. (2019) Multifactor spatio-temporal correlation model based on a combination of convolutional neural network and long short-term memory neural network for wind speed forecasting. Energy Convers Manage 185: 783–799. https://doi.org/10.1016/j.enconman.2019.02.01 doi: 10.1016/j.enconman.2019.02.01
|
| [71] | Siddiqui TA, Bharadwaj S, Kalyanaraman S (2019) A Deep Learning Approach to Solar-Irradiance Forecasting in Sky-Videos. 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), 2166–2174. https://doi.org/10.1109/WACV.2019.00234 |
| [72] |
Nie Y, Li X, Paletta Q, et al. (2024) Open-source sky image datasets for solar forecasting with deep learning: A comprehensive survey. Renewable Sustainable Energy Rev 189: 113977. https://doi.org/10.1016/j.rser.2023.113977 doi: 10.1016/j.rser.2023.113977
|
| [73] | SkyImageNet, 2024. Available from: https://github.com/SkyImageNet. |
| [74] |
Brahma B, Wadhvani R (2020) Solar Irradiance Forecasting Based on Deep Learning Methodologies and Multi-Site Data. Symmetry 12: 1–20. https://doi.org/10.3390/sym12111830 doi: 10.3390/sym12111830
|
| [75] |
Paletta Q, Terrén-Serrano G, Nie Y, et al. (2023) Advances in solar forecasting: Computer vision with deep learning. Adv Appl Energy 11: 100150. https://doi.org/10.1016/j.adapen.2023.100150 doi: 10.1016/j.adapen.2023.100150
|
| [76] |
Ghimire S, Deo RC, Raj N, et al. (2019) Deep solar radiation forecasting with convolutional neural network and long short-term memory network algorithms. Appl Energy 253: 113541. https://doi.org/10.1016/J.APENERGY.2019.113541 doi: 10.1016/J.APENERGY.2019.113541
|
| [77] |
Elsaraiti M, Merabet A (2022) Solar Power Forecasting Using Deep Learning Techniques. IEEE Access 10: 31692–31698. https://doi.org/10.1109/ACCESS.2022.3160484 doi: 10.1109/ACCESS.2022.3160484
|
| [78] |
Reikard G (2009) Predicting solar radiation at high resolutions: A comparison of time series forecasts. Sol Energy 83: 342–349. https://doi.org/10.1016/J.SOLENER.2008.08.007 doi: 10.1016/J.SOLENER.2008.08.007
|
| [79] |
Yang D, Jirutitijaroen P, Walsh WM (2012) Hourly solar irradiance time series forecasting using cloud cover index. Sol Energy 86: 3531–3543. https://doi.org/10.1016/J.SOLENER.2012.07.029 doi: 10.1016/J.SOLENER.2012.07.029
|
| [80] |
Jaihuni M, Basak JK, Khan F, et al. (2020) A Partially Amended Hybrid Bi-GRU—ARIMA Model (PAHM) for Predicting Solar Irradiance in Short and Very-Short Terms. Energies 13: 435. https://doi.org/10.3390/EN13020435 doi: 10.3390/EN13020435
|
| [81] |
Verbois H, Huva R, Rusydi A, et al. (2018) Solar irradiance forecasting in the tropics using numerical weather prediction and statistical learning. Sol Energy 162: 265–277. https://doi.org/10.1016/j.solener.2018.01.007 doi: 10.1016/j.solener.2018.01.007
|
| [82] |
Munkhammar J, van der Meer D, Widén J (2019) Probabilistic forecasting of high-resolution clear-sky index time-series using a Markov-chain mixture distribution model. Sol Energy 184: 688–695. https://doi.org/10.1016/j.solener.2019.04.014 doi: 10.1016/j.solener.2019.04.014
|
| [83] |
Dong J, Olama MM, Kuruganti T, et al. (2020) Novel stochastic methods to predict short-term solar radiation and photovoltaic power. Renewable Energy 145: 333–346. https://doi.org/10.1016/j.renene.2019.05.073 doi: 10.1016/j.renene.2019.05.073
|
| [84] |
Ahmad T, Zhang D, Huang C (2021) Methodological framework for short-and medium-term energy, solar and wind power forecasting with stochastic-based machine learning approach to monetary and energy policy applications. Energy 231: 120911. https://doi.org/10.1016/j.energy.2021.120911 doi: 10.1016/j.energy.2021.120911
|
| [85] | Box GE, Jenkins GM, Reinsel GC, et al. (2015) Time series analysis: Forecasting and control, John Wiley & Sons. |
| [86] |
Louzazni M, Mosalam H, Khouya A (2020) A non-linear auto-regressive exogenous method to forecast the photovoltaic power output. Sustain Energy Techn 38: 100670. https://doi.org/10.1016/j.seta.2020.100670 doi: 10.1016/j.seta.2020.100670
|
| [87] |
Larson DP, Nonnenmacher L, Coimbra CFM (2016) Day-ahead forecasting of solar power output from photovoltaic plants in the American Southwest. Renewable Energy 91: 11–20. https://doi.org/10.1016/j.renene.2016.01.039 doi: 10.1016/j.renene.2016.01.039
|
| [88] |
Sharma V, Yang D, Walsh W, et al. (2016) Short term solar irradiance forecasting using a mixed wavelet neural network. Renewable Energy 90: 481–492. https://doi.org/10.1016/J.RENENE.2016.01.020 doi: 10.1016/J.RENENE.2016.01.020
|
| [89] | Kumari P, Toshniwal D (2020) Real-time estimation of COVID-19 cases using machine learning and mathematical models-The case of India. 2020 IEEE 15th International Conference on Industrial and Information Systems, 369–374. https://doi.org/10.1109/ICIIS51140.2020.9342735 |
| [90] |
Ahmad MW, Mourshed M, Rezgui Y (2018) Tree-based ensemble methods for predicting PV power generation and their comparison with support vector regression. Energy 164: 465–474. https://doi.org/10.1016/J.ENERGY.2018.08.207 doi: 10.1016/J.ENERGY.2018.08.207
|
| [91] |
Wang Z, Wang Y, Zeng R, et al. (2018) Random Forest based hourly building energy prediction. Energy Buildings 171: 11–25. https://doi.org/10.1016/J.ENBUILD.2018.04.008 doi: 10.1016/J.ENBUILD.2018.04.008
|
| [92] |
Zou L, Wang L, Lin A, et al. (2016) Estimation of global solar radiation using an artificial neural network based on an interpolation technique in southeast China. J Atmos Sol-Terr Phys 146: 110–122. https://doi.org/10.1016/J.JASTP.2016.05.013 doi: 10.1016/J.JASTP.2016.05.013
|
| [93] |
Mellit A, Benghanem M, Kalogirou SA (2006) An adaptive wavelet-network model for forecasting daily total solar-radiation. Appl Energy 83: 705–722. https://doi.org/10.1016/J.APENERGY.2005.06.003 doi: 10.1016/J.APENERGY.2005.06.003
|
| [94] |
Çelik Ö, Teke A, Yildirim HB (2016) The optimized artificial neural network model with Levenberg–Marquardt algorithm for global solar radiation estimation in Eastern Mediterranean Region of Turkey. J Cleaner Prod 116: 1–12. https://doi.org/10.1016/J.JCLEPRO.2015.12.082 doi: 10.1016/J.JCLEPRO.2015.12.082
|
| [95] |
Rehman S, Mohandes M (2008) Artificial neural network estimation of global solar radiation using air temperature and relative humidity. Energy Policy 36: 571–576. https://doi.org/10.1016/J.ENPOL.2007.09.033 doi: 10.1016/J.ENPOL.2007.09.033
|
| [96] |
Gürel AE, Ağbulut Ü, Biçen Y (2020) Assessment of machine learning, time series, response surface methodology and empirical models in prediction of global solar radiation. J Cleaner Prod 277: 122353. https://doi.org/10.1016/J.JCLEPRO.2020.122353 doi: 10.1016/J.JCLEPRO.2020.122353
|
| [97] |
Díaz-Gómez J, Parrales A, Á lvarez A, et al. (2015) Prediction of global solar radiation by artificial neural network based on a meteorological environmental data. Desalin Water Treat 55: 3210–3217. https://doi.org/10.1080/19443994.2014.939861 doi: 10.1080/19443994.2014.939861
|
| [98] |
Rocha PAC, Fernandes JL, Modolo AB, et al. (2019) Estimation of daily, weekly and monthly global solar radiation using ANNs and a long data set: a case study of Fortaleza, in Brazilian Northeast region. Int J Energy Environ Eng 10: 319–334. https://doi.org/10.1007/S40095-019-0313-0/TABLES/6 doi: 10.1007/S40095-019-0313-0/TABLES/6
|
| [99] |
Rezrazi A, Hanini S, Laidi M (2016) An optimisation methodology of artificial neural network models for predicting solar radiation: a case study. Theor Appl Climatol 123: 769–783. https://doi.org/10.1007/s00704-015-1398-x doi: 10.1007/s00704-015-1398-x
|
| [100] |
Pang Z, Niu F, O'Neill Z (2020) Solar radiation prediction using recurrent neural network and artificial neural network: A case study with comparisons. Renewable Energy 156: 279–289. https://doi.org/10.1016/J.RENENE.2020.04.042 doi: 10.1016/J.RENENE.2020.04.042
|
| [101] |
Toth E, Brath A, Montanari A (2000) Comparison of short-term rainfall prediction models for real-time flood forecasting. J Hydrol 239: 132–147. https://doi.org/10.1016/S0022-1694(00)00344-9 doi: 10.1016/S0022-1694(00)00344-9
|
| [102] | Mamoulis N, Seidl T, Pedersen TB, et al. (2009) Advances in Spatial and Temporal Databases, Springer Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02982-0 |
| [103] |
Ren J, Ren B, Zhang Q, et al. (2019) A Novel Hybrid Extreme Learning Machine Approach Improved by K Nearest Neighbor Method and Fireworks Algorithm for Flood Forecasting in Medium and Small Watershed of Loess Region. Water 11: 1848. https://doi.org/10.3390/W11091848 doi: 10.3390/W11091848
|
| [104] | Larose DT, Larose CD (2014) k‐Nearest Neighbor Algorithm. Discovering Knowledge in Data: An Introduction to Data Mining, Second Edition, 149–164. https://doi.org/10.1002/9781118874059.CH7 |
| [105] |
Sutton C (2012) Nearest-neighbor methods. WIREs Comput Stat 4: 307–309. https://doi.org/10.1002/WICS.1195 doi: 10.1002/WICS.1195
|
| [106] |
Chen JL, Li GS, Xiao BB, et al. (2015) Assessing the transferability of support vector machine model for estimation of global solar radiation from air temperature. Energy Convers Manage 89: 318–329. https://doi.org/10.1016/j.enconman.2014.10.004 doi: 10.1016/j.enconman.2014.10.004
|
| [107] | Shamshirband S, Mohammadi K, Tong CW, et al. (2016) A hybrid SVM-FFA method for prediction of monthly mean global solar radiation. Theor Appl Climatol 125: 53–65. |
| [108] |
Olatomiwa L, Mekhilef S, Shamshirband S, et al. (2015) Potential of support vector regression for solar radiation prediction in Nigeria. Nat Hazards 77: 1055–1068. https://doi.org/10.1007/s11069-015-1641-x doi: 10.1007/s11069-015-1641-x
|
| [109] |
Ramedani Z, Omid M, Keyhani A, et al. (2014) Potential of radial basis function based support vector regression for global solar radiation prediction. Renewable Sustainable Energy Rev 39: 1005–1011. https://doi.org/10.1016/J.RSER.2014.07.108 doi: 10.1016/J.RSER.2014.07.108
|
| [110] |
Olatomiwa L, Mekhilef S, Shamshirband S, et al. (2015) A support vector machine-firefly algorithm-based model for global solar radiation prediction. Sol Energy 115: 632–644. https://doi.org/10.1016/j.solener.2015.03.015 doi: 10.1016/j.solener.2015.03.015
|
| [111] |
Mohammadi K, Shamshirband S, Danesh AS, et al. (2016) Temperature-based estimation of global solar radiation using soft computing methodologies. Theor Appl Climatol 125: 101–112. https://doi.org/10.1007/s00704-015-1487-x doi: 10.1007/s00704-015-1487-x
|
| [112] |
Hassan MA, Khalil A, Kaseb S, et al. (2017) Potential of four different machine-learning algorithms in modeling daily global solar radiation. Renewable Energy 111: 52–62. https://doi.org/10.1016/j.renene.2017.03.083 doi: 10.1016/j.renene.2017.03.083
|
| [113] |
Quej VH, Almorox J, Arnaldo JA, et al. (2017) ANFIS, SVM and ANN soft-computing techniques to estimate daily global solar radiation in a warm sub-humid environment. J Atmos Sol-Terr Phys 155: 62–70. https://doi.org/10.1016/J.JASTP.2017.02.002 doi: 10.1016/J.JASTP.2017.02.002
|
| [114] |
Baser F, Demirhan H (2017) A fuzzy regression with support vector machine approach to the estimation of horizontal global solar radiation. Energy 123: 229–240. https://doi.org/10.1016/j.energy.2017.02.008 doi: 10.1016/j.energy.2017.02.008
|
| [115] |
Breiman L (2001) Random forests. Mach Learn 45: 5–32. https://doi.org/10.1023/A:1010933404324 doi: 10.1023/A:1010933404324
|
| [116] | Fernández-Delgado M, Cernadas E, Barro S, et al. (2014) Do we need hundreds of classifiers to solve real world classification problems? J Mach Learn Res 15: 3133–3181. |
| [117] | Ke G, Meng Q, Finley T, et al. (2017) Lightgbm: A highly efficient gradient boosting decision tree. Adv Neural Inf Proc Syst, 30. |
| [118] |
Wang Y, Pan Z, Zheng J, et al. (2019) A hybrid ensemble method for pulsar candidate classification. Astrophys Space Sci 364: 139 https://doi.org/10.1007/s10509-019-3602-4 doi: 10.1007/s10509-019-3602-4
|
| [119] |
Si Z, Yang M, Yu Y, et al. (2021) Photovoltaic power forecast based on satellite images considering effects of solar position. Appl Energy 302: 117514. https://doi.org/10.1016/j.apenergy.2021.117514 doi: 10.1016/j.apenergy.2021.117514
|
| [120] | Chung J, Gulcehre C, Cho K, et al. (2014) Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. arXiv preprint arXiv: 1412.3555. |
| [121] |
Wang Y, Liao W, Chang Y (2018) Gated Recurrent Unit Network-Based Short-Term Photovoltaic Forecasting. Energies 11: 2163. https://doi.org/10.3390/EN11082163 doi: 10.3390/EN11082163
|
| [122] |
Pazikadin AR, Rifai D, Ali K, et al. (2020) Solar irradiance measurement instrumentation and power solar generation forecasting based on Artificial Neural Networks (ANN): A review of five years research trend. Sci Total Environ 715: 136848. https://doi.org/10.1016/j.scitotenv.2020.136848 doi: 10.1016/j.scitotenv.2020.136848
|
| [123] |
Wang F, Xuan Z, Zhen Z, et al. (2020) A day-ahead PV power forecasting method based on LSTM-RNN model and time correlation modification under partial daily pattern prediction framework. Energy Convers Manage 212: 112766. https://doi.org/10.1016/j.enconman.2020.112766 doi: 10.1016/j.enconman.2020.112766
|
| [124] |
Zhang J, Yan J, Infield D, et al. (2019) Short-term forecasting and uncertainty analysis of wind turbine power based on long short-term memory network and Gaussian mixture model. Appl Energy 241: 229–244. https://doi.org/10.1016/j.apenergy.2019.03.044 doi: 10.1016/j.apenergy.2019.03.044
|
| [125] |
Liu H, Mi X, Li Y, et al. (2019) Smart wind speed deep learning based multi-step forecasting model using singular spectrum analysis, convolutional Gated Recurrent Unit network and Support Vector Regression. Renewable Energy 143: 842–854. https://doi.org/10.1016/j.renene.2019.05.039 doi: 10.1016/j.renene.2019.05.039
|
| [126] |
Tealab A (2018) Time series forecasting using artificial neural networks methodologies: A systematic review. Future Comput Inf J 3: 334–340. https://doi.org/10.1016/j.fcij.2018.10.003 doi: 10.1016/j.fcij.2018.10.003
|
| [127] |
Dong N, Chang JF, Wu AG, et al. (2020) A novel convolutional neural network framework based solar irradiance prediction method. Int J Electr Power Energy Syst 114: 105411. https://doi.org/10.1016/j.ijepes.2019.105411 doi: 10.1016/j.ijepes.2019.105411
|
| [128] | Hinton GE, Srivastava N, Krizhevsky A, et al. (2012) Improving neural networks by preventing co-adaptation of feature detectors. |
| [129] |
Han Z, Zhao J, Leung H, et al. (2021) A Review of Deep Learning Models for Time Series Prediction. IEEE Sens J 21: 7833–7848. https://doi.org/10.1109/JSEN.2019.2923982 doi: 10.1109/JSEN.2019.2923982
|
| [130] | Shi X, Chen Z, Wang H, et al. (2015) Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting. Adv Neural Inf Proc Syst, 28. |
| [131] | Oord A van den, Dieleman S, Zen H, et al. (2016) WaveNet: A Generative Model for Raw Audio. arXiv preprint arXiv: 1609.03499. https://doi.org/10.48550/arXiv.1609.03499 |
| [132] | Bai S, Kolter JZ, Koltun V (2018) An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling. https://doi.org/10.48550/arXiv.1803.01271 |
| [133] | Vaswani A, Brain G, Shazeer N, et al. (2017) Attention Is All You Need. arXiv preprint arXiv: 1706.03762. |
| [134] |
Zang H, Liu L, Sun L, et al. (2020) Short-term global horizontal irradiance forecasting based on a hybrid CNN-LSTM model with spatiotemporal correlations. Renewable Energy 160: 26–41. https://doi.org/10.1016/j.renene.2020.05.150 doi: 10.1016/j.renene.2020.05.150
|
| [135] |
Qu J, Qian Z, Pei Y (2021) Day-ahead hourly photovoltaic power forecasting using attention-based CNN-LSTM neural network embedded with multiple relevant and target variables prediction pattern. Energy 232: 120996. https://doi.org/10.1016/j.energy.2021.120996 doi: 10.1016/j.energy.2021.120996
|
| [136] |
Schmidhuber J, Hochreiter S (1997) Long Short-Term Memory. Neural Comput 9: 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735 doi: 10.1162/neco.1997.9.8.1735
|
| [137] |
Venkatraman A, Hebert M, Bagnell J (2015) Improving Multi-Step Prediction of Learned Time Series Models. Proceedings of the AAAI Conference on Artificial Intelligence, 29. https://doi.org/10.1609/aaai.v29i1.9590 doi: 10.1609/aaai.v29i1.9590
|
| [138] |
Muhammad, Kennedy J, Lim CW (2022) Machine learning and deep learning in phononic crystals and metamaterials—A review. Mater Today Commun 33: 104606. https://doi.org/10.1016/J.MTCOMM.2022.104606 doi: 10.1016/J.MTCOMM.2022.104606
|
| [139] |
Yao G, Lei T, Zhong J (2019) A review of Convolutional-Neural-Network-based action recognition. Pattern Recogn Lett 118: 14–22. https://doi.org/10.1016/J.PATREC.2018.05.018 doi: 10.1016/J.PATREC.2018.05.018
|
| [140] |
Akram MW, Li G, Jin Y, et al. (2019) CNN based automatic detection of photovoltaic cell defects in electroluminescence images. Energy 189: 116319. https://doi.org/10.1016/J.ENERGY.2019.116319 doi: 10.1016/J.ENERGY.2019.116319
|
| [141] |
Bejani MM, Ghatee M (2021) A systematic review on overfitting control in shallow and deep neural networks. Artif Intell Rev 54: 6391–6438. https://doi.org/10.1007/s10462-021-09975-1 doi: 10.1007/s10462-021-09975-1
|
| [142] |
McCann MT, Jin KH, Unser M (2017) Convolutional neural networks for inverse problems in imaging: A review. IEEE Signal Proc Mag 34: 85–95. https://doi.org/10.1109/MSP.2017.2739299 doi: 10.1109/MSP.2017.2739299
|
| [143] |
Qian C, Xu B, Chang L, et al. (2021) Convolutional neural network based capacity estimation using random segments of the charging curves for lithium-ion batteries. Energy 227: 120333. https://doi.org/10.1016/J.ENERGY.2021.120333 doi: 10.1016/J.ENERGY.2021.120333
|
| [144] |
Liu Y, Guan L, Hou C, et al. (2019) Wind Power Short-Term Prediction Based on LSTM and Discrete Wavelet Transform. Appl Sci 9: 1108. https://doi.org/10.3390/APP9061108 doi: 10.3390/APP9061108
|
| [145] |
Husein M, Chung IY (2019) Day-Ahead Solar Irradiance Forecasting for Microgrids Using a Long Short-Term Memory Recurrent Neural Network: A Deep Learning Approach. Energies 12: 1856. https://doi.org/10.3390/EN12101856 doi: 10.3390/EN12101856
|
| [146] |
Zhao Z, Chen W, Wu X, et al. (2017) LSTM network: a deep learning approach for short-term traffic forecast. IET Intell Transp Syst 11: 68–75. https://doi.org/10.1049/IET-ITS.2016.0208 doi: 10.1049/IET-ITS.2016.0208
|
| [147] |
Suresh V, Janik P, Rezmer J, et al. (2020) Forecasting Solar PV Output Using Convolutional Neural Networks with a Sliding Window Algorithm. Energies 13: 723. https://doi.org/10.3390/EN13030723 doi: 10.3390/EN13030723
|
| [148] |
Zameer A, Jaffar F, Shahid F, et al. (2023) Short-term solar energy forecasting: Integrated computational intelligence of LSTMs and GRU. PLoS One 18: e0285410. https://doi.org/10.1371/journal.pone.0285410 doi: 10.1371/journal.pone.0285410
|
| [149] | Bommasani R, Hudson DA, Adeli E, et al. (2021) On the Opportunities and Risks of Foundation Models. arXiv preprint arXiv: 2108.07258. https://doi.org/10.48550/arXiv.2108.07258 |
| [150] | Devlin J (2018) BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv preprint arXiv: 1810.04805. |
| [151] | Mann B, Ryder N, Subbiah M, et al. (2020) Language Models are Few-Shot Learners. arXiv preprint arXiv: 2005.14165, 1. |
| [152] | Radford A, Kim JW, Hallacy C, et al. (2021) Learning Transferable Visual Models from Natural Language Supervision. International conference on machine learning. PMLR. |
| [153] | Child R, Gray S, Radford A, et al. (2019) Generating Long Sequences with Sparse Transformers. arXiv preprint arXiv: 1904.10509. https://doi.org/10.48550/arXiv.1904.10509 |
| [154] | Kitaev N, Kaiser Ł, Levskaya A (2020) Reformer: The Efficient Transformer. arXiv preprint arXiv: 2001.04451. https://doi.org/10.48550/arXiv.2001.04451 |
| [155] | Beltagy I, Peters ME, Cohan A (2020) Longformer: The Long-Document Transformer. arXiv preprint arXiv: 2004.05150. https://doi.org/10.48550/arXiv.2004.05150 |
| [156] | Wang S, Li BZ, Khabsa M, et al. (2020) Linformer: Self-Attention with Linear Complexity. arXiv preprint arXiv: 2006.04768. https://doi.org/10.48550/arXiv.2006.04768 |
| [157] | Rae JW, Potapenko A, Jayakumar SM, et al. (2020) Compressive Transformers for Long-Range Sequence Modelling. arXiv preprint arXiv: 1911.05507. https://doi.org/10.48550/arXiv.1911.05507 |
| [158] | Dai Z, Yang Z, Yang Y, et al. (2019) Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2978–2988, Florence, Italy. Association for Computational Linguistics. https://doi.org/10.18653/v1/p19-1285 |
| [159] |
Zhou H, Zhang S, Peng J, et al. (2021) Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting. Proceedings of the AAAI Conference on Artificial Intelligence, 35: 11106–11115. https://doi.org/10.1609/AAAI.V35I12.17325 doi: 10.1609/AAAI.V35I12.17325
|
| [160] |
Hanif MF, Mi J (2024) Harnessing AI for solar energy: Emergence of transformer models. Appl Energy 369: 123541. https://doi.org/10.1016/J.APENERGY.2024.123541 doi: 10.1016/J.APENERGY.2024.123541
|
| [161] |
Hussain A, Khan ZA, Hussain T, et al. (2022) A Hybrid Deep Learning-Based Network for Photovoltaic Power Forecasting. Complexity. https://doi.org/10.1155/2022/7040601 doi: 10.1155/2022/7040601
|
| [162] |
Vennila C, Titus A, Sudha TS, et al. (2022) Forecasting Solar Energy Production Using Machine Learning. Int J Photoenergy 2022: 7797488. https://doi.org/10.1155/2022/7797488 doi: 10.1155/2022/7797488
|
| [163] |
So D, Oh J, Leem S, et al. (2023) A Hybrid Ensemble Model for Solar Irradiance Forecasting: Advancing Digital Models for Smart Island Realization. Electronics 12: 2607. https://doi.org/10.3390/electronics12122607 doi: 10.3390/electronics12122607
|
| [164] |
He Y, Liu Y, Shao S, et al. (2019) Application of CNN-LSTM in Gradual Changing Fault Diagnosis of Rod Pumping System. Math Probl Eng 2019: 4203821. https://doi.org/10.1155/2019/4203821 doi: 10.1155/2019/4203821
|
| [165] |
Huang CJ, Kuo PH (2018) A Deep CNN-LSTM Model for Particulate Matter (PM2.5) Forecasting in Smart Cities. Sensors 18: 2220. https://doi.org/10.3390/S18072220 doi: 10.3390/S18072220
|
| [166] |
Cao K, Kim H, Hwang C, et al. (2018) CNN-LSTM Coupled Model for Prediction of Waterworks Operation Data. J Inf Process Syst 14: 1508–1520. https://doi.org/10.3745/JIPS.02.0104 doi: 10.3745/JIPS.02.0104
|
| [167] |
Swapna G, Soman KP, Vinayakumar R (2018) Automated detection of diabetes using CNN and CNN-LSTM network and heart rate signals. Procedia Comput Sci 132: 1253–1262. https://doi.org/10.1016/j.procs.2018.05.041 doi: 10.1016/j.procs.2018.05.041
|
| [168] |
Jalali SMJ, Ahmadian S, Kavousi-Fard A, et al. (2022) Automated Deep CNN-LSTM Architecture Design for Solar Irradiance Forecasting. IEEE Trans Syst Man Cybernetics Syst 52: 54–65. https://doi.org/10.1109/TSMC.2021.3093519 doi: 10.1109/TSMC.2021.3093519
|
| [169] |
Lim SC, Huh JH, Hong SH, et al. (2022) Solar Power Forecasting Using CNN-LSTM Hybrid Model. Energies 15: 8233. https://doi.org/10.3390/EN15218233 doi: 10.3390/EN15218233
|
| [170] |
Covas E (2020) Transfer Learning in Spatial-Temporal Forecasting of the Solar Magnetic Field. Astron Nachr 341: 384–394. https://doi.org/10.1002/ASNA.202013690 doi: 10.1002/ASNA.202013690
|
| [171] |
Sheng H, Ray B, Chen K, et al. (2020) Solar Power Forecasting Based on Domain Adaptive Learning. IEEE Access 8: 198580–198590. https://doi.org/10.1109/ACCESS.2020.3034100 doi: 10.1109/ACCESS.2020.3034100
|
| [172] |
Ren X, Wang Y, Cao Z, et al. (2023) Feature Transfer and Rapid Adaptation for Few-Shot Solar Power Forecasting. Energies 16: 6211. https://doi.org/10.3390/EN16176211 doi: 10.3390/EN16176211
|
| [173] | Zhou S, Zhou L, Mao M, et al. (2020) Transfer Learning for Photovoltaic Power Forecasting with Long Short-Term Memory Neural Network. 2020 IEEE International Conference on Big Data and Smart Computing (BigComp), Busan, Korea (South), 125–132. https://doi.org/10.1109/BIGCOMP48618.2020.00-87 |
| [174] | Soleymani S, Mohammadzadeh S (2023) Comparative Analysis of Machine Learning Algorithms for Solar Irradiance Forecasting in Smart Grids. arXiv preprint arXiv: 2310.13791. https://doi.org/10.48550/arXiv.2310.13791 |
| [175] | Sutarna N, Tjahyadi C, Oktivasari P, et al. (2023) Machine Learning Algorithm and Modeling in Solar Irradiance Forecasting. 2023 6th International Conference of Computer and Informatics Engineering (IC2IE), Lombok, Indonesia, 221–225. https://doi.org/10.1109/IC2IE60547.2023.10330942 |
| [176] |
Bamisile O, Oluwasanmi A, Ejiyi C, et al. (2022) Comparison of machine learning and deep learning algorithms for hourly global/diffuse solar radiation predictions. Int J Energy Res 46: 10052–10073. https://doi.org/10.1002/ER.6529 doi: 10.1002/ER.6529
|
| [177] | Sahaya Lenin D, Teja Reddy R, Velaga V (2023) Solar Irradiance Forecasting Using Machine Learning. 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT), Delhi, India, 1–7. https://doi.org/10.1109/ICCCNT56998.2023.10307660 |
| [178] |
Syahab AS, Hermawan A, Avianto D (2023) Global Horizontal Irradiance Prediction using the Algorithm of Moving Average and Exponential Smoothing. JISA 6: 74–81. https://doi.org/10.31326/JISA.V6I1.1649. doi: 10.31326/JISA.V6I1.1649
|
| [179] |
Aljanad A, Tan NML, Agelidis VG, et al. (2021) Neural Network Approach for Global Solar Irradiance Prediction at Extremely Short-Time-Intervals Using Particle Swarm Optimization Algorithm. Energies 14: 1213. https://doi.org/10.3390/EN14041213 doi: 10.3390/EN14041213
|
| [180] |
Mbah OM, Madueke CI, Umunakwe R, et al. (2022) Extreme Gradient Boosting: A Machine Learning Technique for Daily Global Solar Radiation Forecasting on Tilted Surfaces. J Eng Sci 9: E1–E6. https://doi.org/10.21272/JES.2022.9(2).E1 doi: 10.21272/JES.2022.9(2).E1
|
| [181] |
Cha J, Kim MK, Lee S, et al. (2021) Investigation of Applicability of Impact Factors to Estimate Solar Irradiance: Comparative Analysis Using Machine Learning Algorithms. Appl Sci 11: 8533. https://doi.org/10.3390/APP11188533 doi: 10.3390/APP11188533
|
| [182] | Reddy KR, Ray PK (2022) Solar Irradiance Forecasting using FFNN with MIG Feature Selection Technique. 2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP), Hyderabad, India, 01–05. https://doi.org/10.1109/ICICCSP53532.2022.9862335 |
| [183] |
Chandola D, Gupta H, Tikkiwal VA, et al. (2020) Multi-step ahead forecasting of global solar radiation for arid zones using deep learning. Procedia Comput Sci 167: 626–635. https://doi.org/10.1016/j.procs.2020.03.329 doi: 10.1016/j.procs.2020.03.329
|
| [184] |
Yang Y, Tang Z, Li Z, et al. (2023) Dual-Path Information Fusion and Twin Attention-Driven Global Modeling for Solar Irradiance Prediction. Sensors 23: 7649. https://doi.org/10.3390/S23177469 doi: 10.3390/S23177469
|
| [185] |
Meng F, Zou Q, Zhang Z, et al. (2021) An intelligent hybrid wavelet-adversarial deep model for accurate prediction of solar power generation. Energy Rep 7: 2155–2164. https://doi.org/10.1016/J.EGYR.2021.04.019 doi: 10.1016/J.EGYR.2021.04.019
|
| [186] | Kartini UT, Hariyati, Aribowo W, et al. (2022) Development Hybrid Model Deep Learning Neural Network (DL-NN) For Probabilistic Forecasting Solar Irradiance on Solar Cells To Improve Economics Value Added. 2022 Fifth International Conference on Vocational Education and Electrical Engineering (ICVEE), Surabaya, Indonesia, 151–156. https://doi.org/10.1109/ICVEE57061.2022.9930352 |
| [187] |
Singla P, Duhan M, Saroha S (2022) A dual decomposition with error correction strategy based improved hybrid deep learning model to forecast solar irradiance. Energy Sources Part A 44: 1583–1607. https://doi.org/10.1080/15567036.2022.2056267 doi: 10.1080/15567036.2022.2056267
|
| [188] |
Marinho FP, Rocha PAC, Neto ARR, et al. (2023) Short-Term Solar Irradiance Forecasting Using CNN-1D, LSTM and CNN-LSTM Deep Neural Networks: A Case Study with the Folsom (USA) Dataset. J Sol Energy Eng 145: 041002. https://doi.org/10.1115/1.4056122 doi: 10.1115/1.4056122
|
| [189] |
Kumari P, Toshniwal D (2021) Long short term memory-convolutional neural network based deep hybrid approach for solar irradiance forecasting. Appl Energy 295: 117061. https://doi.org/10.1016/j.apenergy.2021.117061 doi: 10.1016/j.apenergy.2021.117061
|
| [190] |
Elizabeth Michael N, Mishra M, Hasan S, et al. (2022) Short-Term Solar Power Predicting Model Based on Multi-Step CNN Stacked LSTM Technique. Energies 15: 2150. https://doi.org/10.3390/EN15062150 doi: 10.3390/EN15062150
|
| [191] |
Srivastava RK, Gupta A (2023) Short term solar irradiation forecasting using Deep neural network with decomposition methods and optimized by grid search algorithm. E3S Web Conf 405. https://doi.org/10.1051/E3SCONF/202340502011 doi: 10.1051/E3SCONF/202340502011
|
| [192] |
Ziyabari S, Zhao Z, Du L, et al. (2023) Multi-Branch ResNet-Transformer for Short-Term Spatio-Temporal Solar Irradiance Forecasting. IEEE Trans Ind Appl 59: 5293–5303. https://doi.org/10.1109/TIA.2023.3285202 doi: 10.1109/TIA.2023.3285202
|
| [193] |
Carneiro TC, De Carvalho PCM, Dos Santos HA, et al. (2022) Review on Photovoltaic Power and Solar Resource Forecasting: Current Status and Trends. J Sol Energy Eng 144: 010801. https://doi.org/10.1115/1.4051652 doi: 10.1115/1.4051652
|
| [194] |
Chaibi M, Benghoulam ELM, Tarik L, et al. (2021) An Interpretable Machine Learning Model for Daily Global Solar Radiation Prediction. Energies 14: 7367. https://doi.org/10.3390/EN14217367 doi: 10.3390/EN14217367
|
| [195] |
Mason L, González AB de, García-Closas M, et al. (2023) Interpretable, non-mechanistic forecasting using empirical dynamic modeling and interactive visualization. PLoS One 18: e0277149. https://doi.org/10.1101/2022.10.21.22281384 doi: 10.1101/2022.10.21.22281384
|
| [196] |
Rafati A, Joorabian M, Mashhour E, et al. (2021) High dimensional very short-term solar power forecasting based on a data-driven heuristic method. Energy 219: 119647. https://doi.org/10.1016/J.ENERGY.2020.119647 doi: 10.1016/J.ENERGY.2020.119647
|
| [197] |
Wang H, Cai R, Zhou B, et al. (2020) Solar irradiance forecasting based on direct explainable neural network. Energy Convers Manage 226: 113487. https://doi.org/10.1016/J.ENCONMAN.2020.113487 doi: 10.1016/J.ENCONMAN.2020.113487
|
| [198] |
Theocharides S, Makrides G, Livera A, et al. (2020) Day-ahead photovoltaic power production forecasting methodology based on machine learning and statistical post-processing. Appl Energy 268: 115023. https://doi.org/10.1016/J.APENERGY.2020.115023 doi: 10.1016/J.APENERGY.2020.115023
|
| 1. | Shunchang Li, Gang Wu, A Semi‐Randomized and Augmented Kaczmarz Method With Simple Random Sampling for Large‐Scale Inconsistent Linear Systems, 2024, 1070-5325, 10.1002/nla.2591 | |
| 2. | Guang-Xin Huang, Shuang-You Zhong, Tensor randomized extended Kaczmarz methods for large inconsistent tensor linear equations with t-product, 2024, 96, 1017-1398, 1755, 10.1007/s11075-023-01684-w | |
| 3. | Ran-Ran Li, Hao Liu, The maximum residual block Kaczmarz algorithm based on feature selection, 2025, 10, 2473-6988, 6270, 10.3934/math.2025286 | |
| 4. | Ran-Ran Li, Hao Liu, The global block Kaczmarz method using double greedy strategy, 2025, 1017-1398, 10.1007/s11075-025-02069-x |
Figures(12) / Tables(3)
Ayesha Nadeem, Muhammad Farhan Hanif, Muhammad Sabir Naveed, Muhammad Tahir Hassan, Mustabshirha Gul, Naveed Husnain, Jianchun Mi. AI-Driven precision in solar forecasting: Breakthroughs in machine learning and deep learning[J]. AIMS Geosciences, 2024, 10(4): 684-734. doi: 10.3934/geosci.2024035
| Variable | Value (mean ± SD) | Percent (%) |
| Gender | ||
| Male | 7 | 70 |
| Female | 3 | 30 |
| Mean age (years) | 37 ± 12.3 (range: 17–54) | - |
| Acetabular fracture type | ||
| Posterior wall | 6 | 60 |
| Posterior column and posterior wall | 4 | 40 |
| Mechanism of injury | ||
| Fall from a height | 4 | 40 |
| Traffic accident | 6 | 60 |
| Associated injury | ||
| Posterior hip dislocation | 6 | 60 |
| Extremity fractures | 0 | 0 |
| Mean time from injury to surgery (days) | 6 ± 2.5 (range: 4–11) | - |
DownLoad:
CSV
| Postoperative data | Value mean ± SD | Percent (%) |
| Length of incision(cm) | 9.6 ± 1.4 (range: 8–11) | - |
| Mean operative time (min) | 50 ± 14.5 (range: 35–80) | - |
| Mean blood loss (ml) | 310 ± 82.6 (range: 200–440) | - |
| Mean union time (weeks) | 9.3 ± 1.0 (range: 8–12) | - |
| Reduction quality (Matta) | ||
| Anatomic (<1 mm) | 7 | 70 |
| Imperfect (1–3 mm) | 3 | 30 |
| Poor (>3 mm) | 0 | 0 |
| The modified Merle d'Aubigné score | ||
| Excellent | 6 | 60 |
| Good | 2 | 20 |
| Fair | 2 | 20 |
| Poor | 0 | 0 |
| Complications | ||
| Heterotopic ossification | 1 | 10 |
| Femoral head necrosis | 1 | 10 |
DownLoad:
CSV
| Variable | Value (mean ± SD) | Percent (%) |
| Gender | ||
| Male | 7 | 70 |
| Female | 3 | 30 |
| Mean age (years) | 37 ± 12.3 (range: 17–54) | - |
| Acetabular fracture type | ||
| Posterior wall | 6 | 60 |
| Posterior column and posterior wall | 4 | 40 |
| Mechanism of injury | ||
| Fall from a height | 4 | 40 |
| Traffic accident | 6 | 60 |
| Associated injury | ||
| Posterior hip dislocation | 6 | 60 |
| Extremity fractures | 0 | 0 |
| Mean time from injury to surgery (days) | 6 ± 2.5 (range: 4–11) | - |
| Postoperative data | Value mean ± SD | Percent (%) |
| Length of incision(cm) | 9.6 ± 1.4 (range: 8–11) | - |
| Mean operative time (min) | 50 ± 14.5 (range: 35–80) | - |
| Mean blood loss (ml) | 310 ± 82.6 (range: 200–440) | - |
| Mean union time (weeks) | 9.3 ± 1.0 (range: 8–12) | - |
| Reduction quality (Matta) | ||
| Anatomic (<1 mm) | 7 | 70 |
| Imperfect (1–3 mm) | 3 | 30 |
| Poor (>3 mm) | 0 | 0 |
| The modified Merle d'Aubigné score | ||
| Excellent | 6 | 60 |
| Good | 2 | 20 |
| Fair | 2 | 20 |
| Poor | 0 | 0 |
| Complications | ||
| Heterotopic ossification | 1 | 10 |
| Femoral head necrosis | 1 | 10 |
