A Hybrid Technique for Estimating and Forecasting Household Electrical Energy Consumption Utilizing Machine Deep Learning and Fuzzy Wavelet
الموضوعات : Optimizationعبدالرضا رحماني فر 1 , 2 , ali taghizadeh herat 3 , Asghar Darigh 4
1 -
2 - 1Department of Industrial Engineering Parand branch, Islamic Azad University, Parand, Iran
3 - گروه مهندسی صنایع دانشکده فنی دانشگاه آزاد اسلامی پرند. تهران
4 - Department of Industrial Engineering, Islamic Azad University, Parand Branch
الکلمات المفتاحية: Deep Learning, neural network, fuzzy wavelet, households,
ملخص المقالة :
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 to previous approaches, the findings demonstrate that the algorithm's prediction accuracy is greater and its error is noticeably lower.
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