Comparison of the Deep Learning Performance for Short-Term Power Load Forecasting

Abstract

Electricity demand forecasting enables the stable operation of electric power systems and reduces electric power consumption. Previous studies have predicted electricity demand through a correlation analysis between power consumption and weather data; however, this analysis does not consider the influence of various factors on power consumption, such as industrial activities, economic factors, power horizon, and resident living patterns of buildings. This study proposes an efficient power demand prediction using deep learning techniques for two industrial buildings with different power consumption patterns. The problems are presented by analyzing the correlation between the power consumption and weather data by season for industrial buildings with different power consumption patterns. Four models were analyzed using the most important factors for predicting power consumption and weather data (temperature, humidity, sunlight, solar radiation, total cloud cover, wind speed, wind direction, humidity, and vapor pressure). The prediction horizon for power consumption forecasting was kept at 24 h. The existing deep learning methods (DNN, RNN, CNN, and LSTM) cannot accurately predict power consumption when it increases or decreases rapidly. Hence, a method to reduce this prediction error is proposed. DNN, RNN, and LSTM were superior when using two-year electricity consumption rather than one-year electricity consumption and weather data.