A Short-Term Electric Load Forecasting Scheme Using 2-Stage Predictive Analytics

One key issue for stable power supply is to forecast electric load accurately. Since buildings of the same type show similar power consumption patterns, it should be considered for accurate electric load forecast. In particular, university buildings show various electric loads depending on time and other external factors. In this paper, we propose a short-term load forecast model for educational buildings using 2-stage predictive analytics for the effective operation of their power system. To do that, we collect the electric load data of five years from a university campus. Next, we consider the electric load pattern by using the moving average method according to the day of the week. Next, we predict the daily electric load using the random forest method and finally evaluate its performance using the time series cross-validation. The experimental results show that our forecasting model outperforms other competing methods in terms of prediction accuracy.