Hybrid approach based on wavelets, Kalman filters, Arima, and neural networks for mastering electrical energy at the residential level: Forecasting the electrical energy consumption of residences in the city of Lomé

The energy sector, specifically residential electricity, must be managed to understand the electricity consumption model of each residence at any given time. This study introduces three hybrid forecasting techniques: Wavelet Transform-Kalman filters-ARIMA (WKA), Wavelet Transform-Artificial Neural Networks-Kalman filters (WNNK), and Wavelet Transform-Artificial Neural Networks-ARIMA (WNNA). These hybrid forecasting models were individually applied to each dataset of each residence. The data for this study were collected from 28 different residences over a period of 2 years and 5 months in Lomé, with measurements taken at one-minute intervals. The results, validated using error evaluation criteria such as Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and the correlation coefficient, revealed that 10 of the 28 residences achieved the best forecasting results with the WNNK hybrid model, 10 with the WKA model, and 8 with the WNNA model. This analysis enabled the classification of residences into three groups: Group 1, Group 2, and Group 3, corresponding respectively to the residences achieving the most accurate forecasting results with the hybrid models WNNK, WNNA, and WKA. This work not only enhanced the understanding of electricity consumption habits and provided a method for forecasting future electricity use but also categorized each residence into one of the three groups based on its level of consumption.