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With the booming growth of advanced digital technologies, it has becomepossible for users as well as distributors of energy to obtain detailed andtimely information about the electricity consumption of households. Thesetechnologies can also be used to forecast the household's electricityconsumption (a.k.a. the load). In this paper, we investigate the use ofVariational Mode Decomposition and deep learning techniques to improve theaccuracy of the load forecasting problem. Although this problem has beenstudied in the literature, selecting an appropriate decomposition level and adeep learning technique providing better forecasting performance have garneredcomparatively less attention. This study bridges this gap by studying theeffect of six decomposition levels and five distinct deep learning networks.The raw load profiles are first decomposed into intrinsic mode functions usingthe Variational Mode Decomposition in order to mitigate their non-stationaryaspect. Then, day, hour, and past electricity consumption data are fed as athree-dimensional input sequence to a four-level Wavelet Decomposition Networkmodel. Finally, the forecast sequences related to the different intrinsic modefunctions are combined to form the aggregate forecast sequence. The proposedmethod was assessed using load profiles of five Moroccan households from theMoroccan buildings' electricity consumption dataset (MORED) and was benchmarkedagainst state-of-the-art time-series models and a baseline persistence model.

Experimental Investigation of Variational Mode Decomposition and Deep Learning for Short-Term Multi-horizon Residential Electric Load Forecasting