Electricity Demand Forecasting Using Deep Polynomial Neural Networks and Gene Expression Programming During COVID-19 Pandemic

The power-generation mix of future grids will be quite diversified with the ever-increasing share of renewable energy technologies. Therefore, the prediction of electricity demand will become crucial for resource optimization and grid stability. Machine learning- and artificial intelligence-based methods are widely studied by researchers to tackle the demand forecasting problem. However, since the COVID-19 pandemic broke out, new challenges have surfaced for forecasting research. In such a short amount of time, significant shifts have emerged in electricity demand trends, making it apparent that the pandemic and the possibility of similar crises in the future have escalated the complexity of energy management problems. Motivated by the circumstances, this research presents an hour-ahead and day-ahead electricity demand forecasting benchmark using Deep Polynomial Neural Networks (DNN) and Gene Expression Programming (GEP) methods. The DNN and GEP algorithms utilize on-site electricity consumption data collected from a university hospital for over two years with a temporal granularity of 15-minute intervals. Quarter-hourly meteorological, calendar, and daily COVID-19 data, including new cases and cumulative cases divided by four restriction levels, were also considered. These datasets are used not only to predict the electricity demand but also to investigate the impact of the COVID-19 pandemic on the electricity consumption of the hospital. The hour-ahead and day-ahead nRMSE results show that the DNN outperforms the GEP by 8.27% and 14.32%, respectively. For the computational times, the DNN appears to be much faster than the GEP by 82.83% and 78.56% in the hour-ahead and day-ahead forecasting, respectively.