Forecasting energy demand and efficiency in a smart home environment through advanced ensemble model: Stacking and voting

Abstract

Smart homes integrate several sensors to facilitate information exchange and the execution of tasks. In addition, with the development of the Internet of Things (IoT) platforms, the control of appliances and remote devices has become possible. This sensor collects data in real time to closely monitor the devices of a user’s household. The present study employs a machine learning methodology to perform a global analysis of energy consumption and efficiency in smart homes. In This work we propose two advanced ensemble models to improve the performance of energy consumption in smart homes, the first one is a voting ensemble model based on a ranking weight averaging that combines following basic machine learning techniques: decision tree (DT), random forest (RF), and eXtreme Gradient Boosting (XGB). The second one is the stacking ensemble model in which the basic models (DT-RF-XGB) are combined through stacked generalization, then uses a secondary layer model or meta-learner (RF) to provide output prediction. The findings obtained show that the proposed ensemble model based on DT-RF-XGB using stacking technique surpasses all other basic algorithms with R² around 0.9825.

Keywords

Smart Home voting/stacking ensemble model decision tree random forest (RF)extreme gradient boosting (XGB)

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References

Al-Hajj,

Assi and

M.M.

Fouad, Forecasting solar radiation strength using machine learning ensemble, in: 2018 7th International Conference on Renewable Energy Research and Applications (ICRERA), IEEE, 2018, pp. 184–188. doi:10.1109/ICRERA.2018.8567020.

M.S.

Alam,

Sharma and

U.R.

Salve, Assessment of thermal comfort in a hot and humid indoor built environment of a kitchen at a university canteen, Work 72(1) (2022), 189–199. doi:10.3233/WOR-205174.

M.S.

Alam,

Sharma and

U.R.

Salve, Investigating thermal environment inside the kitchen of air-conditioned pantry car on Indian Railway using CFD, Human Factors and Ergonomics in Manufacturing & Service Industries 32(5) (2022), 359–372. doi:10.1002/hfm.20950.

R.E.

Alden,

Gong,

E.S.

Jones,

Ababei and

D.M.

Ionel, Artificial intelligence method for the forecast and separation of total and HVAC loads with application to energy management of smart and NZE homes, IEEE Access 9 (2021), 497–509. doi:10.1109/ACCESS.2020.3047122.

J.M.M.

Arce and

E.Q.B.

Macabebe, Real-time power consumption monitoring and forecasting using regression techniques and machine learning algorithms, 2019 IEEE International Conference on Internet of Things and Intelligence System (IoTaIS), pp. 135–140.

Ashton, The Internet of things thing, RFID journal 22(7) (2014), 97–114.

Balachandran,

Venkatesan,

Lakshmi and

Jahnavi, Energy consumption analysis and load management for smart home, in: Proceedings of the Third International Conference on Trends in Electronics and Informatics, IEEE Xplore Part Number: CFP19J32-ART; ISBN: 978-1-5386-9439-8, ICOEI, 2019.

Biau and

Scornet, A random forest guided tour, Test 25(2) (2016), 197–227. doi:10.1007/s11749-016-0481-7.

Boubaker,

Benghanem,

Mellit et al., Deep neural networks for predicting solar radiation at hail region, Saudi Arabia, IEEE Access 9 (2021), 36719–36729. doi:10.1109/ACCESS.2021.3062205.

10.

Breiman, Random forests, Machine Learning 45(1) (2001), 5–32. doi:10.1023/A:1010933404324.

11.

Chen and

Guestrin, XGBoost: A scalable tree boosting system, in: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016.

12.

Dong,

Deng and

Wang, A short-term power load forecasting method based on k-means and SVM, Journal of Ambient Intelligence and Humanized Computing (2021), 1–15.

13.

Drir,

Chekired and

Rekioua, An integrated neural network for the dynamic domestic energy management of a solar house, International Transaction electrical energy system 31(12) (2021).

14.

European Commission Buildings, Online Available: ec.europa.eu/energy/en/topics/energy-efficiency/buildings.

15.

García-Martín,

C.F.

Rodrigues,

Riley and

Grahn, Estimation of energy consumption in machine learning, Journal of Parallel Distributed Computing 134 (2019), 75–88. doi:10.1016/j.jpdc.2019.07.007.

16.

Hamed,

A.I.

Alzahrani,

Shae,

Mustaffa,

M.C.

Ismail and

K.K.

Eng, Two steps hybrid calibration algorithm of support vector regression and k-nearest neighbors, Alexandria Engineering Journal 59(3) (2020), 1181–1190. doi:10.1016/j.aej.2020.01.033.

17.

Javed,

B.K.

Kasi and

F.A.

Khan, Predicting solar irradiance using machine learning techniques, in: Proc,15th International Wireless Communications & Mobile Computing Conference (IWCMC), 2019, pp. 1458–1462.

18.

Jha,

Kumar,

Abdel-Basset,

Priyadarshini,

Sharma and

H.V.

Long, Deep learning approach for software maintainability metrics prediction, IEEE Access 7 (2019), 61840–61855. doi:10.1109/ACCESS.2019.2913349.

19.

Kaushik,

Srinivasan,

Sharmila,

Devasena,

Suresh,

Panchal and

Srimali, Continuous monitoring of power consumption in urban buildings based on Internet of Things, International Journal of Ambient Energy 43 (2021), 1–7.

20.

Kontogiannis,

Bargiotas and

Daskalopulu, Fuzzy control system for smart energy management in residential buildings based on environmental data, Energies 14(3) (2021), 752. doi:10.3390/en14030752.

21.

Mansoor,

Khan et al., Enabling automation and edge intelligence over resource constraint IoT devices for smart home, Neurocomputing 491 (2022), 494–506. doi:10.1016/j.neucom.2021.04.138.

22.

Moradzadeh,

Mohammadi and

Abapour, Heating and cooling loads forecasting for residential buildings based on hybrid machine learning applications: A comprehensive review and comparative analysis, IEEE Access 10 (2021), 2196–2215.

23.

Muhammad Wassem,

Mourshed and

Rezgui, Tree-based ensemble methods for predicting PV power generation and their comparison with support vector regression, Energy 164(1) (2018), 465–474.

24.

Nageem and

Jayabarathi, Predicting the power output of a grid connected solar panel using multi-input support vector regression, Procedia Computer Science 115 (2017), 723–730. doi:10.1016/j.procs.2017.09.143.

25.

Panel and

Wolpert, Stacked generalization, Neural Networks 5(2) (1992), 241–259. doi:10.1016/S0893-6080(05)80023-1.

26.

Priyadarshini,

Sahu,

Kumar and

Taniar, A machine-learning ensemble model for predicting energy consumption in smart homes, Internet of Things 20 (2022), 100636, ISSN 2542-6605. doi:10.1016/j.iot.2022.100636.

27.

Ren,

Zhang and

P.N.

Suganthan, Ensemble classification and regression-recent developments, applications and future directions, IEEE Computational Intelligence Magazine 11(1) (2016), 41–53. doi:10.1109/MCI.2015.2471235.

28.

Schweizer

et al., Using consumer behavior data to reduce energy consumption in smart homes applying machine learning to save energy without lowering comfort of inhabitants, in: IEEE, 14th International Conference on Machine Learning and Applications (ICMLA), 2015.

29.

Seung Jang,

Yeol Bae and

Park, Solar power prediction based on satellite images and support vector machine, IEEE transaction on Sustainable Energy (2016), 1255–1263.

30.

Shapsough,

Dhaouadi and

Zualkernan, Using linear regression and back propagation neural networks to predict performance of soiled PV modules, Procedia Computer Science 155 (2019), 463–470. doi:10.1016/j.procs.2019.08.065.

31.

Szewcyzk,

Dwan,

Minor,

Swedlove and

Cook, Annotating smart environment sensor data for activity learning, Technology and Health Care 17(3) (2009), 161–169. doi:10.3233/THC-2009-0546.

32.

Tanaka,

Uchida,

Ogimi,

Goya,

Yona,

Senjyu,

Funabashi and

C.-H.

Kim, Optimal operation by controllable loads based on smart grid topology considering insolation forecasted error, IEEE Transactions on Smart Grid 2(3) (2021), 438–444. doi:10.1109/TSG.2011.2158563.

33.

T.A.

Tuan,

H.V.

Long,

Kumar,

Priyadarshini and

N.T.K.

Son, Performance evaluation of Botnet DDoS attack detection using machine learning, Evolutionary Intelligence (2019), 1–12.

34.

J.R.

Vazquez-Canteli and

Nagy, Reinforcement learning for demand response: A review of algorithms and modeling techniques, Appl. Energy 235(1) (2019), 1072–1089. doi:10.1016/j.apenergy.2018.11.002.

35.

Verma,

Singh and

Majumdar, Multi-label LSTM autoencoder for non-intrusive appliance load monitoring, Electric Power Systems Research 199 (2021), 107414. doi:10.1016/j.epsr.2021.107414.

36.

Zhang and

Ma, Ensemble Machine Learning, Methods and Applications, Springer, 2012.

37.

X.M.

Zhang,

Grolinger,

M.A.

Capretz and

Seewald, Forecasting residential energy consumption: Single household perspective, in: 17th IEEE International Conference on Machine Learning and Applications (ICMLA), 2018.

38.

Zhang,

W.-C.

Hong and

Li, Electric load forecasting by hybrid self-recurrent support vector regression model with variational mode decomposition and improved cuckoo search algorithm, IEEE Access 8 (2020), 14642–14658. doi:10.1109/ACCESS.2020.2966712.