Short-Term Load Forecasting Using a Parallel CNN-BPNN Prediction Model with COVID-19 Pandemic Restriction as an Added Input Parameter and ReLU Activation Function

Short-term load forecasting provides a vital tool for the power system. This study delved into applying a hybridized machine learning algorithm to improve load forecasting accuracy. It aims to investigate the accuracy of the parallel CNN-BPNN prediction model in short-term load forecasting with Philippine pandemic restriction as an added parameter and a ReLU activation function. The CNN, BPNN, and the proposed parallel CNN-BPNN models were implemented using Python. They were trained, validated, and tested using the input parameters such as historical power demand, day of weeks/ Holidays, meteorological data such as temperature, wind speed, humidity, and COVID-19 pandemic restriction. The accuracy of the three models was tested using the MAPE. Results showed that the proposed model achieved the lowest MAPE of 3.52 %, lower than that of the CNN, 4.62%, and BPNN, 3.98%. Furthermore, Pearson correlation analysis showed that the relationship between electricity usage and mobility constraints is moderately correlated with a correlation value of -0.57. © 2022 WCSE. All Rights Reserved.