Short-Term Electric Load Prediction in Smart Grid using Multi-Output Gaussian Processes Regression

Electric load prediction (ELP) can support smart grid (SG) goals such as reliability and efficiency. In ELP, we predict the electricity demand at aggregated levels which is very vital for the proper functioning of SG and keeping a balance between load and supply demand. ELP can be categorized as very short-term, short-term, medium-term, and long-term ELP. In this paper, we proposed multi-output Gaussian processes (MOGP) regression for hourly day-ahead short-term ELP (predicting 24 load values of the next day) based on load, temperature, and dew point values of previous days. We evaluated the performance of the proposed MOGP and compared it with the persistence and multiple linear regression methods. The results show that the proposed MOGP has a very good prediction accuracy.