Presenting a New Model of Electric Power Consumption Estimation Based on Parallel Wavelet Converters and Convolutional Neural Networks with Deep Learning for Residential Buildings

Considering the increasing rate of electrical energy usage, this energy has become one of the most important resources for human life. So all countries are seeking access to reliable and planned energy resource. Regarding the non-renewability of fossil fuel resources, especially oil and gas, the issue of replacing these types of energy with renewable energy has been considered for decades. Saving and optimal use of electrical energy in important applications such as residential and commercial buildings is critical. One of the most important factors for planning power consumption and optimizing it is accurate forecasting for next hours’ power consumption of residential and commercial buildings. In this paper, first, the data sets of several residential buildings are analyzed using parallel wavelet converters. Then, using an optimal estimator model of the convolutional neural network, the short-term load of the building is estimated. The obtained results show that the proposed method has improved the prediction error about 70, 69 and 73 percent for ARIMA, SVR, and LSTM methods, respectively.