Energy efficiency analysis: A household digital transformation

Gunnar Lima; Andreas Nascimento; Marcelo P. Oliveira; Fagner L. G. Dias; Gunnar Lima; Andreas Nascimento; Marcelo P. Oliveira; Fagner L. G. Dias

doi:10.3934/energy.2024037

AIMS Energy

2024, Volume 12, Issue 4: 774-808. doi: 10.3934/energy.2024037

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Research article

Energy efficiency analysis: A household digital transformation

1.
Graduate Program in Engineering, São Paulo State University-UNESP, Guaratinguetá, São Paulo, Brazil
2.
Institute of Mechanical Engineering, Itajubá Federal University-UNIFEI, Itajubá, Minas Gerais, Brazil

Received: 18 January 2024 Revised: 10 March 2024 Accepted: 15 March 2024 Published: 05 July 2024

Nowadays, the increased demand for energy and electrification associated with higher production costs from renewable and cleaner sources has driven up prices, impacting the industrial, commercial, and residential sectors. With a direct influence on the development of these economic sectors, its direct and indirect impacts to products and services have become important to find more efficient ways and best practices on energy use to support sustainable development. Aiming to shed light on this topic, and how individuals and society behave in this energy market transformation, this article explores opportunities for reducing electricity consumption through the use of modern technologies, such as of monitoring, optimization, automation, and adjustment of routines. At the same time, it is also our intention to bring to the surface a discussion around the rational use of everyday resources and raising the awareness of its impact to individuals and institutions. At its core, this work consists of continuous data collection of single devices and equipment in regard to status, energy consumption, and other relevant data of a typical household. Through behavioral changes and introduction of smart home automation techniques, it was possible to trace a parallel comparison between different scenarios and their influence on the energy consumption without negative impact to the comfort of individuals. Seeking a continuous improvement approach, extensive iterations were conducted, and it was possible to notice not only an energy efficiency improvement, but at the same time gains in living standards and safety. The significant results observed over subsequent months and years highlight not only practical and financial benefits, but also increased awareness and behavioral changes toward the rational use of electricity in households.

Keywords:

Citation: Gunnar Lima, Andreas Nascimento, Marcelo P. Oliveira, Fagner L. G. Dias. Energy efficiency analysis: A household digital transformation[J]. AIMS Energy, 2024, 12(4): 774-808. doi: 10.3934/energy.2024037

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Abstract

Abbreviations: (α, β, γ, η, θ, λ, μ, a, b): Model parameters; R (t): Reliability function; MTBF (τ): the average Lifetime; PV: Photovoltaic; a-Si: Amorphous silicon; pc-Si: Polycrystalline silicon; mc-Si: Single crystalline silicon

1. Introduction

Many articles in literature have studied the degradation of photovoltaic modules when exposed to natural environments using accelerated tests to observe degradation in reality ^[1,2]. A study confirmed that after 20 years of continuous exposure a matrix of 70 polycrystalline silicon photovoltaic modules has undergone an average performance decay of 0.24% per year in a moderate subtropical climate environment ^[3,4]. Another study stated that after only one year of exposure in a tropical climate environment the electrical powers of two modules of type (a-Si) and (pc-Si) were degraded to 60% and 56% respectively of their initial values ^[5]. In addition to these results, another study has shown that some photovoltaic modules (mc-Si and pc-Si) had been degraded by 0.22% /year to 2.96% / year for the maximum power ^[6]. In the long run, the polycrystalline silicon modules have the best reliability with a degradation rate of 0.41% per year in a natural environment ^[7]. In a tropical environment (Ghana), the exposure of 14 polycrystalline silicon modules during a 19-year period recorded a degradation rate of 21% to 35% of nominal power ^[8]. The degradation is in the order of 1.2% per year for polycrystalline silicon modules and 0.8% per year for single crystalline silicon modules ^[9]. An important study that followed the degradation of 204 modules (123 mc-Si and 81 pc-Si) had revealed a degradation variance from 0 to 6% per year for exposure periods of 18 years to 24 years in a subtropical moderate environment ^[10]. In Saharan environment (southern Algeria for example) the degradation rate of polycrystalline silicon modules was very high ranging from 3.33% / year to 4.64% / year unlike mono-crystalline silicon modules which recorded a rate of 1.22% / year after 28 years of exposure ^[11,12]. Accelerated tests cannot evaluate totally the effect of natural environment on electrical and optical characteristics of a photovoltaic module ^[13]. But it is the only method to see the effect of single factor or limited number of climatic factors ^[13,14]. The return of experimental data within a period of operation in a natural environment allows to predict the lifetime and the degradation over the long term ^[15]. Our objective in this study is to search in the literature for an adequate model to simulate the reliability of photovoltaic modules (crystalline silicon) exposed in desert environments in order to probably estimate their degradation at any period of their operation. The method consists of using a genetic algorithm (artificial intelligence optimization method) to estimate the unknown parameters of the models and to check the competence of the simulation by comparing with feedbacks of experimental data.

2. Research method

Two kinds of methods in the literature are used to predict the duration of good operation and the reliability of a photovoltaic module exposed in a natural environment, the first that uses the return of experiments, or the second that utilizes accelerated tests ^[16]. In this study, we use the feedback data that are practically measured in the desert of California and in the Algerian Sahara (Adrar region), extracted from references ^[16,17,18] to estimate the lifespan of photovoltaic modules (single crystalline silicon) in these environments. In order to calculate the parameters of models we will use a genetic algorithm. The iterative stochastic genetic algorithm uses an initial population to reach an optimal solution of any problem ^[19]. The initially chosen population has evolved from generation to generation where the most suitable individuals have a great chance of breeding. This mechanism of intelligence is realized by respecting the following steps ^[20,21]:

1. Creation of an initial population

2. Assessment of individuals in population

3. Selection of adapted individuals

4. Reproduction by crossing and mutation

5. Formation of a new generation

This process is circulated until an optimal solution is obtained. Practically, we represent these steps according to the flowchart below (Figure 1):

Figure 1. Flowchart of the genetic algorithm.

DownLoad: Full-Size Img PowerPoint

Originally, the reliability concerned the high technology systems (nuclear, aerospace...) to guarantee their operational safety. Today, all areas are interested in the study of reliability to make decisions on ratio Cost / gain and to control the failure sources ^[22,23]. Reliability of a system is a quantity characterizing the safety of operation or measuring the probability of operation of an appliance according to prescribed standards (definition presented in 1962 by the Academy of Sciences). Reliability (or survival function) is expressed by:

$R\left( t \right) = {r^{ - \smallint _0^th\left( x \right)dx}}$

(1)

h(t): Instant failure rate (probability of seeing a failure in a short interval after instant t.

The average time of operation (lifetime) which is the Mean Time Before Failure (MTBF) is given by:

$MTBF = \int_0^{ + \infty } {R\left( t \right)dt}$

(2)

According to their instantaneous rates of failure, the parametric reliability models are classified in the literature as follows ^[24,25,26]:

1. Models of constant rate: Exponential model.

2. Models of monotone rate: Weibul model, gamma model, Gompertz-Makeham model, exponential Weibul model, Mix of exponential models.

3. Models of rates of a bathtub shape: Modified Weibul model, exponential power model, quadratic model, and uniform model.

4. Models of rate in bell form: Generalized Weibul model, normal model, log-normal model, log logistic model, extreme values model.

The characteristics of chosen models are presented in the table 1 below:

Table 1. Parametric models and their characteristics.

Model	Model of two parameters
Model	Reliability function	Average lifetime
Exponential model	$R\left(t \right) = {e^{ - \lambda t}}\; with\; \lambda > 0$	$MTBF = \frac{1}{\lambda }$
Weibul model	$R\left(t \right) = {e^{ - {{\left({\frac{t}{\eta }} \right)}^\beta }}}; \beta, \eta > 0$	$MTBF = \eta \mathit{\Gamma }\left({1{ + ^1}{/_\beta }} \right)$
Gamma model	$R\left(t \right) = 1 - \frac{1}{{\mathit{\Gamma }\left(\mu \right)}}\int_0^{\theta t} {{x^{\mu - 1}}{e^{ - x}}dx; \left({\mu, \theta } \right) > 0}$	$MTBF = \frac{\mu }{\theta }$
Exponential power model	$R\left(t \right) = {e^{1 - e \left({\lambda t} \right)\alpha }}\; with\alpha > 0;\lambda, \alpha > 0$	$MTBF = \int_0^{ + \infty } {{e^{1 - e \left({\lambda t} \right)\alpha }}dt}$
Normal model	$R\left(t \right) = 1 - \frac{1}{{\sqrt {2\pi } \sigma }}\int_0^{ + \infty } {{e^{ - \frac{{{{\left({x - \mu } \right)}^2}}}{{2{\sigma ^2}}}}}dx}$	$MTBF = \mu$
Log-normal model	$R\left(t \right) = 1 - \frac{1}{{\sqrt {2\pi } \sigma }}\int_{ - \infty }^{Int} {{e^{ - \frac{{{{\left({x - \mu } \right)}^2}}}{{2{\sigma ^2}}}}}dx}$	$MTBF = {e^{\mu + \frac{{{\sigma ^2}}}{2}}}$
Log logistic model	$R\left(t \right) = \frac{{{\alpha ^\beta }}}{{{\alpha ^\beta } + {t^\beta }}}\; or\; \alpha > 0;\beta > 1$	$MTBF = \int_0^{ + \infty } {\frac{{{\alpha ^\beta }}}{{{\alpha ^\beta } + {t^\beta }}}dt}$
Uniform model	$R\left(t \right) = \frac{{b - t}}{{b - a}}; for\; t \in \left[{a, b} \right]$	$MTBF = \int_a^b {\frac{{b - t}}{{b - a}}} dt$
Extreme values model	$R\left(t \right) = {e^{ - \alpha \left({{e^{\beta t}} - 1} \right)}}\; with\; \alpha > 0, \beta > 0$	$MTBF = \int_0^{ + \infty } {{e^{ - \alpha \left({{e^{\beta t}} - 1} \right)}}dt}$
Gompertz-Makeham model	$R\left(t \right) = {e^{ - at - \frac{b}{{Inc}}\left({{c^t} - 1} \right)}}$	$MTBF = \int_0^{ + \infty } {{e^{ - at - \frac{b}{{Inc}}\left({{c^t} - 1} \right)}}dt}$
exponential Weibul model	$R\left(t \right) = 1 - {\left\{ {1 - {e^{ - {{\left({\frac{t}{\eta }} \right)}^\beta }}}} \right\}^\mu }\; or\; \eta, \beta, \mu > 0$	$MTBF = \int_0^{ + \infty } {1 - {{\left\{ {1 - {e^{ - {{\left({\frac{t}{\eta }} \right)}^\beta }}}} \right\}}^\mu }dt}$
Mix of exponential models	$\begin{array}{l} R\left(t \right) = {a_1}{e^{ - \frac{t}{{{\theta _1}}}}} + \left({1 - {a_1}} \right){e^{ - \frac{t}{{{\theta _2}}}}}; \\ {\theta _1}, {\theta _2} > 0;\; 0 < {a_1} < 1 \end{array}$	$MTBF = {a_1}{\theta _1} + \left({1 - {a_1}} \right){\theta _2}$
Modified Weibul model	$R\left(t \right) = {e^{ - {{\left({\frac{t}{\eta }} \right)}^\beta }{e^{\mu t}}}}\; with\; \left({\eta; \beta; \mu > 0} \right)$	$MTBF = \int_0^{ + \infty } {{e^{ - {{\left({\frac{t}{\eta }} \right)}^\beta }{e^{\mu t}}}}} dt$
Quadratic model	$R\left(t \right) = {e^{ - \left({\alpha t + \frac{\beta }{2}{t^2} + \frac{\gamma }{3}{t^3}} \right)}}\alpha, \gamma > 0; - 2\sqrt {\gamma \alpha } \le \beta \le 0$	$MTBF = \int_0^{ + \infty } {{e^{ - \left({\alpha t + \frac{\beta }{2}{t^2} + \frac{\gamma }{3}{t^3}} \right)}}} dt$
Generalized Weibul model	$R\left(t \right) = {e^{1 - {{\left({1 + {{\left({\frac{t}{\eta }} \right)}^\beta }} \right)}^{\frac{1}{\gamma }}}}}\; with\; \left({\eta, \beta, \gamma } \right) > 0$	$MTBF = \int_0^{ + \infty } {{e^{1 - {{\left({1 + {{\left({\frac{t}{\eta }} \right)}^\beta }} \right)}^{\frac{1}{\gamma }}}}}} dt$

| Show Table

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3. Results and discussion

After filtering we present only the cases where the calculated average error is less than 2%. This choice is solely made to limit the size of the study. The other cases are not interesting as our goal is to visualize the most adequate model having the least error.

3.1. Simulation of reliability by modified Weibul model

Estimated parameters and simulated reliability graphs are shown below (Table 2, Figures 2 and 3):

Table 2. Parameters of modified Weibul model in the two areas.

	In Adrar Sahara	In the desert of California
Parameters model (η, β, μ)	(71, 1.35, 0.03)	(49, 1.05, 0.01)
Average lifetime MTBF (years)	28.75	30.47
Uncertainty	0.0039	0.0059

| Show Table

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Figure 2. Reliability of photovoltaic module in Adrar Sahara by modified Weibul model.

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Figure 3. Reliability of photovoltaic modules in the desert of California by Weibul modified model.

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After 20 years of operation in Adrar region we observe that the Weibul modified model predicts a 30% degradation of starting value of electrical power for this type of photovoltaic modules while the degradation is approximately of 38% in the desert of California.

3.2. Simulation of reliability by Generalized Weibul model

In the Table 3 and Figures (4, 5) we present the estimated parameters and the simulated reliability:

Table 3. Parameters of generalized Weibul model in the two areas.

	In Adrar Sahara	In the desert of California
Parameters model (η, β, γ)	(38, 2.5, 0.5)	(77, 1.2, 0.4)
Average lifetime MTBF (years)	23.45	26.09
Uncertainty	0.0184	0.0092

| Show Table

DownLoad: CSV

Figure 4. Reliability of photovoltaic modules in Adrar Sahara by generalized Weibul model.

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Figure 5. Reliability of photovoltaic modules in the desert of California by generalized Weibul model.

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In this case the generalized Weibul model predicts a degradation of 35% in Adrar region after 20 years of operation while the degradation is approximately of 44% in the desert of California.

3.3. Simulation of reliability by exponential Weibul model

The Table 4 and the Figures 6, 7 show the estimated parameters and the simulated reliability of PV module:

Table 4. Parameters of exponential Weibul model in the two areas.

	In Adrar Sahara	In the desert of California
Parameters model (η, β, μ)	(28, 4.05, 0.66)	(37, 2.65, 0.45)
Average lifetime MTBF (years)	22.57	23.40
Uncertainty	0.0254	0.0028

| Show Table

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Figure 6. Reliability of photovoltaic module in Adrar Sahara by exponential Weibul model.

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Figure 7. Reliability of photovoltaic module in the desert of California by exponential Weibul model.

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By this model the degradation is about of 38% in Adrar region after 20 years of operation while the degradation is approximately of 46% in the desert of California.

3.4. Simulation of reliability by Extreme values model

Estimated parameters and simulated reliability graphs are shown below (Table 5, Figures 8 and 9):

Table 5. Parameters of extreme values model in the two areas.

	In Adrar Sahara	In the desert of California
Parameters model (α, β)	(0.015, 0.68)	(0.02, 0.9)
Average lifetime MTBF (years)	50.09	31.99
Uncertainty	0.0081	0.0227

| Show Table

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Figure 8. Reliability of photovoltaic modules in Adrar Sahara by extreme values model.

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Figure 9. Reliability of photovoltaic modules in the desert of California by extreme values model.

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By model of extreme values, the degradation is about of 22% in Adrar region after 20 years of operation while it is approximately of 37% in the desert of California.

3.5. Simulation of Reliability by Uniform model

In this case the estimated parameters and the simulated reliability are shown below (table 6, figure 10 and 11):

Table 6. Parameters of uniform model in the two areas.

	In Adrar Sahara	In the desert of California
Parameters model (a, b)	(1.6, 77)	(0.4, 48)
Average lifetime MTBF (years)	37.70	23.80
Uncertainty	0.0074	0.0139

| Show Table

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Figure 10. Reliability of photovoltaic module in Adrar Sahara by uniform model.

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Figure 11. Reliability of photovoltaic modules in the desert of California by uniform model.

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Finally, the uniform model predicts a degradation of 24% in Adrar region After 20 years of operation while it is approximately of 41% in the desert of California.

These results are summarized in the following Table 7.

Table 7. Summary of results.

Model of Reliability	Average life time (years)		Error Means (%)
	in Adrar	in California
modified Weibul model	28.75	30.47	0.4
Uniform model	37.70	23.80	1.1
Generalized Weibul model	23.45	26.09	1.3
exponential Weibul model	22.57	23.40	1.4
Extreme values model	50.09	31.99	1.5
Average lifetime calculated	32.51	27.15
Average lifetime calculated	29.83 ≈ 30years

| Show Table

DownLoad: CSV

The above results indicate that:

1. The experimental data used have guided us to predict the future of solar panels operating in desert environments. We therefore believe that more return data will give us confidence in models and methods.

2. The calculated mean error that present the average relative distance of the graph from the points of comparison is generally small (especially for the modified Weibul model). These reflect the skill of the optimization method used (the genetic algorithm).

3. Extrapolation of curves in longer durations allows informing on the reliability (outside of periods of real measurements).

4. The modified Weibul model is the most adequate of the models tested to simulate the reliability of photovoltaic modules (single crystalline silicon) and to estimate their lifetimes (MTBF) in the desert environments. It predicted a duration of nearly 30 years in the desert of California and of around 29 years for the Adrar area.

5. It should be noted that the degradation of electrical power of photovoltaic modules in Californian desert is significant compared to that of Adrar region in the first step (in the initial period of 30 years).

6. These obtained results are more or less comparable to those stated in references ^[11,12] (a degradation close to 1.53% /year in this study).

4. Conclusion

It has been confirmed in this article that the modified Weibul law is the most adequate model compared to other tested models to simulate the reliability function of photovoltaic modules and estimate their lifetime while operating in desert environments (California and Adrar). Using simulation findings, an average lifespan of about 30 years has been predicted for photovoltaic modules exposed in desert regions where the maximum power of the photovoltaic module is degraded to almost 46% of its initial value. The annual rate of degradation is in the order of 1.5% / year. This obtained result is more or less comparable to those presented in the literature. The prediction results must be taken into consideration for any study of construction of solar stations in the Saharan environments.

Acknowledgment

I thank my fellow researchers in Renewable Energy Research Unit in Saharan areas (URERMS) for all given help.

Conflict of interest

The authors declare there are no conflicts of interest in this paper.

References

[1]	Tzeiranaki ST, Bertold P, Castellazz L, et al. (2022) Energy consumption and energy efficiency trends in the EU, 2000–2020. European Commission, Joint Research Centre, Publications Office of the European Union, 2022. https://dx.doi.org/10.2760/727548
[2]	IRENA (2023) World energy transitions outlook 2023: 1.5 ℃ pathway. Abu Dhabi: Int Renewable Energy Agency. Available from: https://www.irena.org/Publications/2023/Jun/World-Energy-Transitions-Outlook-2023.
[3]	BBC (2022) Nord Stream blast 'blew away 50 meters of pipe'. BBC News. Available from: https://www.bbc.com/news/world-europe-63297085.
[4]	BP p.l.c. (2023) Energy outlook 2023 edition. BP p.l.c. Available from: https://www.bp.com/content/dam/bp/business-sites/en/global/corporate/pdfs/energy-economics/energy-outlook/bp-energy-outlook-2023.pdf.
[5]	Edelstein S (2022) Global EV sales more than doubled in 2021 vs. 2020, tripled vs. 2019. Green Car Rep. Available from: https://www.greencarreports.com/news/1134999_global-ev-sales-more-than-doubled-in-2021-vs-2020-tripled-vs-2019.
[6]	RMI (2023) Cryptocurrency's energy consumption problem. RMI. Available from: https://rmi.org/cryptocurrencys-energy-consumption-problem/#: ~: text = Bitcoin%20alone%20is%20estimated%20to, fuel%20used%20by%20US%20railroads.
[7]	Statista Inc (2021) Projected installed electricity generation capacity from battery storage worldwide from 2020 to 2050. Statista. Available from: https://www.statista.com/statistics/1307203/world-battery-storage-electricity-generation-capacity/.
[8]	Roth L, Lowitzsch J, Yildiz Ö (2021) An empirical study of how household energy consumption is affected by co-owning different technological means to produce renewable energy and the production purpose. Energies 14: 3996. https://doi.org/10.3390/en14133996 doi: 10.3390/en14133996
[9]	Matuszewska-Janica A, Żebrowska-Suchodolska D, Zalewska ME (2023) Differences in the structure of household electricity prices in EU countries. Energies 16: 6636. https://doi.org/10.3390/en16186636 doi: 10.3390/en16186636
[10]	IRENA (2022) Renewable power generation costs in 2021. Int Renewable Energy Agency, Abu Dhabi. Available from: https://www.irena.org/publications/2022/Jul/Renewable-Power-Generation-Costs-in-2021.
[11]	Eurostat (2020) Energy prices and costs in Europe. European Commission Brussels, Belgium. Available from: https://energy.ec.europa.eu/data-and-analysis/energy-prices-and-costs-europe_en.
[12]	Milewska B, Milewski D (2023) The Impact of energy consumption costs on the profitability of production companies in Poland in the context of the energy crisis. Energies 16: 6519. https://doi.org/10.3390/en16186519 doi: 10.3390/en16186519
[13]	Scheier E (2022) A measurement strategy to address disparities across household energy burdens. Nat Commun 13: 288. https://doi.org/10.1038/s41467-021-27673-y doi: 10.1038/s41467-021-27673-y
[14]	Nord Pool AS (2023) System price curve data. Nord Pool. Available from: https://www.nordpoolgroup.com/en/elspot-price-curves/.
[15]	Eurostat (2023) Electricity prices for household consumers—bi-annual data (from 2007 onwards). Eurostat. Available from: https://ec.europa.eu/eurostat/databrowser/view/NRG_PC_204__custom_7314052/default/table?lang = en.
[16]	Shang WL, Lv Z (2023) Low carbon technology for carbon neutrality in sustainable cities: A survey. Sustainable Cities Soc 92: 104489. https://doi.org/10.1016/j.scs.2023.104489 doi: 10.1016/j.scs.2023.104489
[17]	EIA (2023) Annual energy outlook 2023. U.S. Energy Inf Adm. Available from: https://www.eia.gov/todayinenergy/detail.php?id = 56040#: ~: text = Our%20projections%20of%20residential%20consumption, 5.9%20quads%20and%206.3%20quads.
[18]	Kahlenborn W, Kabisch S, Klein J (2012) Energy management systems in practice-ISO 50001: A guide for companies and organizations. Federal Ministry for the Environment, Nature Conservation and Nuclear Safety (BMU), Berlin, Germany.
[19]	Lackner P, Holanek N (2007) Step by step guide for implementing energy management. Vienna: Austrian Energy Agency.
[20]	Lv Z, Shang WL, Guiza M (2022) Impact of digital twins and metaverse on cities: history, current situation, and application perspectives. Appl Sci 12: 12820. https://doi.org/10.3390/app122412820 doi: 10.3390/app122412820
[21]	Rojek I, Mikołajewski D, Mroziński A (2023) Machine learning- and Artificial intelligence-derived prediction for home smart energy systems with PV installation and battery energy storage. Energies 13: 6613. https://doi.org/10.3390/en16186613 doi: 10.3390/en16186613
[22]	Shelly Cloud (2023) Devices. Shelly Knowledge Base. Available from: https://kb.shelly.cloud/knowledge-base/shelly-plus-devices.
[23]	Signify Netherlands B.V. (2023) Hue white starter kit. Philips-Hue. Available from: https://www.philips-hue.com/en-us/p/hue-white-starter-kit-e26/046677563080.
[24]	Lumi United Technology Co. Ltd. (2023) Presence sensor FP2. Aqara. Available from: https://www.aqara.com/us/product/presence-sensor-fp2/.
[25]	Raspberry Pi Ltd. (2023) Raspberry Pi for home. Raspberry Pi. Available from: https://www.raspberrypi.com/.
[26]	Shelly Cloud (2023) Energy metering (energy efficiency). Shelly Cloud. Available from: https://www.shelly.com/.
[27]	Microsoft Corporate (2023) Power BI. Microsoft. Available from: https://powerbi.microsoft.com/en-us/.
[28]	OpenJS Foundation (2023) Node-RED Low-code programming for event-driven applications. Node-RED. Available from: https://nodered.org/.
[29]	Eclipse Foundation Inc. (2023) Eclipse Mosquitto: An open source MQTT broker. Eclipse Mosquitto. Available from: https://mosquitto.org/.
[30]	Wikimedia Foundation Inc. (2023) Five Ws. Wikipedia. Available from: https://en.wikipedia.org/wiki/Five_Ws.
[31]	Lean Six Sigma Institute (2023) Lean six sigma certification program. Available from: https://leansixsigmainstitute.org/.
[32]	Sinek S (2009) Start with why: how great leaders inspire everyone to take action. Portfolio.
[33]	IBM (2020) What is ETL extract transform load? IBM Corporation. Available from: https://www.ibm.com/topics/etl.
[34]	Vasilieva E, Deepa B, Soosan C (2024) IoT in home automation: A data-driven user behavior analysis and user adoption test. BIO Web Conf. 86: 1–9. https://doi.org/10.1051/bioconf/20248601085 doi: 10.1051/bioconf/20248601085
[35]	Abuhussain MA, Alotaibi BS, Aliero MS (2023) Adaptive HVAC system based on fuzzy controller approach. Appl Sci 13: 11354. https://doi.org/10.3390/app132011354 doi: 10.3390/app132011354
[36]	Lee SH, Oh ST, Lim JH (2024) Lighting control method based on RIIL to reduce building energy consumption. Energy Rep 11: 2090–2098. https://doi.org/10.1016/j.egyr.2024.01.075 doi: 10.1016/j.egyr.2024.01.075

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