Very Short-Term Prosumer Electric Load Forecasting Using Deep Learning-Based Techniques

In recent years, owing to the increasing penetration of renewable energy sources into the modern electric distribution networks within the age of smart grid, the concept of consumer in the electricity markets has been evolved into the concept of prosumer which can be referred to as an individual who consumes and produces electricity. In order to maintain the crucial balance between the generation and consumption of electricity, prosumer electric load forecasting (PELF) has become a requisite for energy management and planning in today’s microgrids. Deep learning (DL)-based techniques are frequently employed for forecasting the electric load that is nonstationary and affected by several factors such as seasonal effects, climatological conditions, and random effects. The aim of this paper is to present a benchmark regarding PELF of a household residing in the state of California, USA via using DL-based techniques, namely convolutional neural networks (CNN) and gated recurrent unit networks (GRU) within the very short-term horizon. In addition, hourly meteorological data belonging to the residential area has been obtained from Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) database of NASA. Consequently, the results of the paper unveiled that utilizing CNN achieved better performance for PELF in terms of mean absolute error (MAE) and root mean squared error (RMSE) by 13% and 8%, respectively. Furthermore, it is considered that there is a gap in the literature for PELF and this paper will bridge this gap along with guiding the potential researchers in the field.