A Hybrid Technique for Estimating and Forecasting Household Electrical Energy Consumption Utilizing Machine Deep Learning and Fuzzy Wavelet
Abdolreza Rahmanifar
1
(
1Department of Industrial Engineering, Pa.c., Islamic Azad University, Parand, Iran
)
mehran khalaj
2
(
1Department of Industrial Engineering Parand branch, Islamic Azad University, Parand, Iran
)
Ali Taghizade Herat
3
(
Department of Industrial Engineering,Faculty of Parand,Parand Branch,ISLAMIC AZAD UNIVERSITY,PARAND.IRAN
)
Asghar Darigh
4
(
Department of Industrial Engineering, Islamic Azad University, Parand Branch
)
کلید واژه: Deep Learning, Neural Network, Fuzzy Wavelet, Households,
چکیده مقاله :
This study investigates the electrical consumption of households as a microcosm of a macro society, with the individual appliances inside each home serving as the "electricity consuming units. The goal is to provide an optimal approach for addressing the issue of efficient energy usage. To accomplish this objective, it is essential to divide the total electrical consumption of the home into its component elements, which are the individual signals utilized by every appliance. Likewise, estimating the energy consumption of the appliances is a very efficient means of foreseeing how much energy each device would consume in the future and, if necessary, controlling it. In this research, a Fuzzy Wavelet- and Convolutional Network-based method is established as a way of decomposing the signals generated by individual home appliances from the overall (composite) signal. In addition, the proposed algorithm is employed in conjunction with two well-known and strong algorithms in Time-series data analysis, Long-Short Term Memory (LSTM) and Multilayer Perceptron (MLP). Hence, the proposed approach is compared to the aforementioned two renowned algorithms as well as other techniques from previous studies. The proposed neural network is trained using the Stochastic Gradient Descent (SGD) optimization approach at each stage, and the Nesterov Accelerated Gradient (NAG) optimization method is also investigated. In comparison with previous approaches, the findings demonstrate that the algorithm's prediction accuracy is greater and its error is noticeably lower. It means that the proposed algorithm is a top contender among the existing algorithms for predicting of energy consumption in residential buildings.
چکیده انگلیسی :
This study investigates the electrical consumption of households as a microcosm of a macro society, with the individual appliances inside each home serving as the "electricity consuming units. The goal is to provide an optimal approach for addressing the issue of efficient energy usage. To accomplish this objective, it is essential to divide the total electrical consumption of the home into its component elements, which are the individual signals utilized by every appliance. Likewise, estimating the energy consumption of the appliances is a very efficient means of foreseeing how much energy each device would consume in the future and, if necessary, controlling it. In this research, a Fuzzy Wavelet- and Convolutional Network-based method is established as a way of decomposing the signals generated by individual home appliances from the overall (composite) signal. In addition, the proposed algorithm is employed in conjunction with two well-known and strong algorithms in Time-series data analysis, Long-Short Term Memory (LSTM) and Multilayer Perceptron (MLP). Hence, the proposed approach is compared to the aforementioned two renowned algorithms as well as other techniques from previous studies. The proposed neural network is trained using the Stochastic Gradient Descent (SGD) optimization approach at each stage, and the Nesterov Accelerated Gradient (NAG) optimization method is also investigated. In comparison with previous approaches, the findings demonstrate that the algorithm's prediction accuracy is greater and its error is noticeably lower. It means that the proposed algorithm is a top contender among the existing algorithms for predicting of energy consumption in residential buildings.
1. Y.Liu, S.Yu, Y.Zhu, D.Wang, J.Liu, Modeling, planning, application and management of energy systems for isolated areas: A review, Renewable and Sustainable Energy Reviews, 82 (2018), 460-470.
2. Council, W. E. (2013). World energy scenarios composing energy futures to 2050. PSI, London.
3. M.Sehatpour, A.Kazemi, H.Sehatpour, Evaluation of alternative fuels for light-duty vehicles in Iran using a multi-criteria approach, Renewable and Sustainable Energy Reviews, 72(2017) 295-310.
4. B.Salvi, K.Subramanian, N.Panwar, Alternative fuels for transportation vehicles: a technical review, Renewable and Sustainable Energy Reviews, 25(2013), 404-419.
5. T. Chaudhuri, Y.C. Soh, H. Li, L. Xie, A feedforward neural network based indoor-climate control framework for thermal comfort and energy saving in buildings, Applied Energy, 248 (2019), 44-53.
6. Y. Himeur, A. Alsalemi, F. Bensaali, A. Amira, Robust event-based non-intrusive appliance recognition using multi-scale wavelet packet tree and ensemble bagging tree, Applied Energy, 267 (2020) 114877.
7. A. Wójcik, R. Lukaszewski, R. Kowalik, W. Winiecki, Nonintrusive Appliance Load Monitoring: An Overview, Laboratory Test Results and Research Directions, Sensors, 19 (2019) 3621.
8. R. Usman, P. Mirzania, S.W. Alnaser, P. Hart, C. Long, Systematic Review of Demand-Side Management Strategies in Power Systems of Developed and Developing Countries. Energies, 15 (2022) 7858.
9. G.W. Hart, Nonintrusive appliances load monitoring, Proceedings of the IEEE, 80(1992) 1870-1891.
10. T.K. Nguyen, E. Dekneuvel, G.Jacquemod, Development of a real-time non-intrusive appliance load monitoring system: an application level model, Power Energy System 90(2017) 168–180.
11. C. Puente, R.Palacios, Y.González-Arechavala, E.F. Sánchez-Úbeda,. Non-Intrusive Load Monitoring (NILM) for Energy Disaggregation Using Soft Computing Techniques. Energies, 13(2020) 3117.
12. X. Wu, Y. Gao, D. Jiao, Multi-label classification based on random forest algorithm for non-intrusive load monitoring system, Processes, 7 (2019) 337.
13. B. Yin, Z. Li, J. Xu, Non-intrusive load monitoring algorithm based on household electricity use habits, Neural Computer & Application, 34 (2022) 15273–15291.
14. S. Wu, K.L. Lo, Non-intrusive monitoring algorithm for resident loads with similar electrical characteristic, Processes 8 (2020) 1385.
15. D. Li, S. Dick, Non-intrusive load monitoring using multi-label classification methods. Electr. Eng. 103(2021) 607–619.
16. Y. Liu, Q. Shi, Y. Wang, X. Zhao, S. Gao, X. Huang, An Enhanced Ensemble Approach for Non-Intrusive Energy Use Monitoring Based on Multidimensional Heterogeneity, Sensors 21 (202) 7750.
17. Y. Hu, Y. Lin, C. Lin, Artificial intelligence accelerated in parallel computing and applied to nonintrusive appliance load monitoring for residential demand-side management in a smart grid: A comparative study, Appl. Sci., 10 (2020).
18. X. Wu, D. Jiao, Y. Du, Automatic implementation of a self-adaption non-intrusive load monitoring method based on the convolutional neural network, Processes 8 (2020) 704.
19. X. Wu, Y. Gao, D. Jiao, Multi-label classification based on random forest algorithm for non-intrusive load monitoring system, Processes 7 (2019) 337.
20. İ.H. Çavdar, V. Faryad, New design of a supervised energy disaggregation model based on the deep neural network for a smart grid, Energies 12 (2019) 1217.
21. Y. Lin, Y. Hu, Electrical energy management based on a hybrid artificial neural network-particle swarm optimization-integrated two-stage non-intrusive load monitoring process in smart homes, Processes 6 (2018) 236.
22. S. Alotaibi, “Advancing energy performance efficiency in residential buildings for sustainable design: Integrating machine learning and optimized explainable AI (AIX),” Int. J. Energy Res., Article ID 6130634, 2024.
23. Z. Azim, H. Pooyafar, B. A. Naieri, D. P. Khiabani, and A. M. Bazargan, “Prediction of energy consumption for residential buildings based on the behavior of its residents using artificial intelligence neural network method in Tabriz city,” Strategic Soc. Energy Stud., vol. 11, no. 1, pp. 1–20, 2024.
24. M. Khodadadi, L. Riazi, and S. Yazdani, “A novel ensemble deep learning model for building energy consumption forecast,” Int. J. Eng., vol. 37, no. 6, pp. 1067–1075, 2024.
25. M. Neshat, M. Thilakaratne, M. El-Abd, S. Mirjalili, A. H. Gandomi, and J. Boland, “Smart buildings energy consumption forecasting using adaptive evolutionary ensemble learning models,” arXiv preprint, 2025. [Online]. Available: https://doi.org/10.48550/arXiv.2506.11864
26. S. Alam, “Deep learning applications for residential energy demand forecasting,” AI, IoT and the Fourth Industrial Revolution Review, vol. 14, no. 2, pp. 27–38, 2024.
27. J. Kelly, W. Knottenbelt, The UK-DALE dataset, domestic appliance-level electricity demand and whole-house demand from five UK homes, Scientific data, 2 (2015) 1-14.
28. B. Zhao, H. Song, Fuzzy Shannon wavelet finite element methodology of coupled heat transfer analysis for clearance leakage flow of single screw compressor. Engineering with Computers, (2021) 1-11.
29. A. Kulkarni, D.Chong, F. A. Batarseh, Foundations of data imbalance and solutions for a data democracy, Data Democracy, 5 (2020) 83-106.
30. A. Faustine, L. Pereira, H. Bousbiat, S. Kulkarni, UNet-NILM: A deep neural network for multi-tasks appliances state detection and power estimation in NILM, In Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring, (2020) 84-88.
31. K. Yan, X. Wang, Y. Du, N. Jin, H. Huang, H. Zhou,. Multi-step short-term power consumption forecasting with a hybrid deep learning strategy. Energies, 11 (2018) 3089.
32. S. Singh A. Yassine, Big data mining of energy time series for behavioral analytics and energy consumption forecasting, Energies, 11 (2018) 452.
33. C. Zhang, M. Zhong, Z. Wang, N. Goddard, C. Sutton, Sequence-to-point learning with neural networks for non-intrusive load monitoring. In Proceedings of the AAAI Conference on Artificial Intelligence (2018) Vol. 32, No. 1.
34. H. Kim, S. Lim,. Temporal Patternization of Power Signatures for Appliance Classification in NILM. Energies, 14 (2021) 2931.
35. Y.C. Hu, Y. H. Lin, H.L. Gururaj, Partitional Clustering-Hybridized Neuro-Fuzzy Classification Evolved through Parallel Evolutionary Computing and Applied to Energy Decomposition for Demand-Side Management in a Smart Home, Processes, 9 (2021) 1539.
36. J. Kelly, W. Knottenbelt, Neural NILM: Deep neural networks applied to energy disaggregation, In Proceedings of the 2nd ACM International Conference on Embedded Systems for Energy-Efficient Built Environments (2015) 55–64.
37. P. Franco, J.M. Martínez, Y.C. Kim, M.A. Ahmed, IoT based approach for load monitoring and activity recognition in smart homes, IEEE Access, 9 (2021) 45325-45339.
38. Y. Himeur, A. Alsalemi, F. Bensaali, A. Amira, Smart non-intrusive appliance identification using a novel local power histogramming descriptor with an improved k-nearest neighbors classifier, Sustainable Cities and Society, 67 (2021) 102764.
39. X. Zhou, J. Feng, Y. Li, Non-intrusive load decomposition based on CNN–LSTM hybrid deep learning model. Energy Reports, 7 (2021) 5762-5771.
