
AIMS Energy, 2019, 7(3): 382394. doi: 10.3934/energy.2019.3.382.
Research article Topical Section
Export file:
Format
 RIS(for EndNote,Reference Manager,ProCite)
 BibTex
 Text
Content
 Citation Only
 Citation and Abstract
Shortterm load forecasting using machine learning and periodicity decomposition
1 Laboratory of Physics of Atmosphere, Modeling and Simulation LPAMS. FSTM, Mohammedia. BP 146 Mohammedia 20650 Morocco. Hassan II University Casablanca
2 LSSIEE ENSAM Casablanca. 150 Avenue Nile Sidi Othman Casablanca 20670, Morocco. Hassan II University Casablanca
Received: , Accepted: , Published:
Topical Section: Smart Grids and Networks
Keywords: load forecasting; machine learning; periodicity decomposition; time series; smart grid
Citation: Abdelkarim El khantach, Mohamed Hamlich, Nour eddine Belbounaguia. Shortterm load forecasting using machine learning and periodicity decomposition. AIMS Energy, 2019, 7(3): 382394. doi: 10.3934/energy.2019.3.382
References:
 1. Murthy Balijepalli VSK, Pradhan V, Khaparde SA, et al. (2011) Review of demand response under smart grid paradigm. ISGT2011India, Kollam, Kerala, 236–243.
 2. Faria P, Vale Z (2011) Demand response in electrical energy supply: An optimal real time pricing approach. Energy 36: 5374–5384.
 3. Moslehi K, Kumar R (2010) A reliability perspective of the smart grid. IEEE Trans Smart Grid 1: 57–64.
 4. Chan SC, Tsui KM, Wu HC, et al. (2012) Load/price forecasting and managing demand response for smart grids: Methodologies and challenges. IEEE Signal Process Mag 29: 68–85.
 5. Palma, Wilfredo (2016) Book: Time series analysis.
 6. Raza MQ, Khosravi A (2015) A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings. Renewable Sustainable Energy Rev 50: 1352–1372.
 7. Gross G, Galiana FD (1987) ShortTerm load forecasting. Proc IEEE 75: 1558–1573.
 8. Hyde O, Hodnett PF (1997) An adaptable automated procedure for shortterm electricity load forecasting. IEEE Trans Power Syst 12: 84–94.
 9. Broadwater RR, Sargent A, Yarali A, et al. (1997) Estimating substation peaks from load research data. IEEE Trans Power Delivery, 12: 451–456.
 10. Huang SR (1997) Shortterm load forecasting using threshold autoregressive models. IEE Proc Gener, Transm Distrib 144: 477–481.
 11. ElKeib AA, Ma X, Ma H (1995) Advancement of statistical based modeling techniques for shortterm load forecasting. Electr Power Syst Res 35: 51–58.
 12. Chen J, Wang W, Huang C (1995) Analysis of an adaptive timeseries autoregressive movingaverage (ARMA) model for shortterm load forecasting. Electr Power Syst Res 34: 187–196.
 13. Barakat EH, Qayyum MA, Hamed MN, et al. (1990) Shortterm peak demand forecasting in fast developing utility with inherit dynamic load characteristics. I. Application of classical timeseries methods. II. Improved modelling of system dynamic load characteristics. IEEE Trans Power Syst 5: 813–824.
 14. Taylor JW (2003) Shortterm electricity demand forecasting using double seasonal exponential smoothing. J Oper Res Soc 54: 799–805.
 15. Hyndman RJ, Fan S (2010) Density forecasting for longterm peak electricity demand. IEEE Trans Power Syst 25: 1142–1153.
 16. Badri A, Ameli Z, Birjandi AM (2012) Application of artificial neural networks and fuzzy logic methods for short term load forecasting. Energy Procedia 14: 1883–1888.
 17. Li D, Chang C, Chen C, et al. (2012) Forecasting shortterm electricity consumption using the adaptive greybased approachAn Asian case. Omega 40: 767–773.
 18. Yang HY, Ye H, Wang G, et al. (2006) Fuzzy neural veryshortterm load forecasting based on chaotic dynamics reconstruction. Chaos Solitons Fractals 29: 462–469.
 19. Alkandari AM, Soliman SA, Elhawary ME (2004) Fuzzy shortterm electric load forecasting. Int J Electr Power Energy Syst 26: 111–122.
 20. Smith M (2000) Modeling and shortterm forecasting of new South Wales electricity system load. J Bus Econ Stat 18: 465–478.
 21. Amina M, Kodogiannis VS, Petrounias I, et al. (2012) A hybrid intelligent approach for the prediction of electricity consumption. Int J Electr Power Energy Syst 43: 99–108.
 22. Hsu C, Chen C (2003) Applications of improved grey prediction model for power demand forecasting. Energy Convers Manage 44: 2241–2249.
 23. Fiot J, Dinuzzo F (2018) Electricity demand forecasting by multitask learning. IEEE Trans Smart Grid 9: 544–551.
 24. GonzalezRomera E, JaramilloMoran MA, CarmonaFernandez D (2006) Monthly electric energy demand forecasting based on trend extraction. IEEE Trans Power Syst 21: 1946–1953.
 25. Zahedi G, Azizi S, Bahadori A, et al. (2013) Electricity demand estimation using an adaptive neurofuzzy network : A case study from the Ontario provinceCanada. Energy 49: 323–328.
 26. Dudek G (2015) ShortTerm load forecasting using random forests. IEEE Conf Intell Syst 821–828.
 27. Chaturvedi DK, Sinha AP, Malik OP (2015) Short term load forecast using fuzzy logic and wavelet transform integrated generalized neural network. Int J Electr Power Energy Syst 67: 230–237.
 28. Ryu S, Noh J, Kim H (2016) Deep neural network based demand side short term load forecasting. Energies 10: 1–20.
 29. Shevade SK, Keerthi SS, Bhattacharyya C, et al. (2000) Improvements to the SMO algorithm for SVM regression. IEEE Trans Neural Networks 11: 1188–1193.
 30. Mashor MY (2000) Hybrid training algorithm for RBF network. Int J Comput Internet Manage 8: 50–65.
 31. Quinlan JR (1987) Simplifying decision trees. Int J ManMach Stud 27: 221–234.
 32. Rasmussen CE (2004) Gaussian processes in machine learning. Adv Lect Mach Learn 63–71.
This article has been cited by:
 1. Davut Solyali, A Comparative Analysis of Machine Learning Approaches for Short/LongTerm Electricity Load Forecasting in Cyprus, Sustainability, 2020, 12, 9, 3612, 10.3390/su12093612
 2. Kei Hirose, Keigo Wada, Maiya Hori, Rinichiro Taniguchi, Event Effects Estimation on Electricity Demand Forecasting, Energies, 2020, 13, 21, 5839, 10.3390/en13215839
Reader Comments
© 2019 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)
Associated material
Metrics
Other articles by authors
Related pages
Tools
your name: * your email: *