Short-term and Long-term Electric Load Prediction Using New Machine Learning Techniques with Temperature and Solar Altitude Angle

This study is aimed to apply three machine learning techniques including Random Forest (RF), Support Vector Machine (SVM), and Multivariate Adaptively Regression Spline (MARS) in both short-term and long-term electric load forecasting problems and compare their performance. Last hour load, temperature, and solar altitude angle of the present hour with holidays were considered as inputs. Three different criteria including root mean square error, mean absolute error, and coefficient of determination were used to evaluate the performance of prediction methods. These methods are all applied to practical electric load demand data obtained from a sub-transmission substation in Hamedan using R programming language. The temperature data are collected from the nearest meteorological weather station and the hourly solar altitude angle for the whole year is accurately calculated with astronomical equations for the studied location. The results show that the implemented methods provide acceptable forecasts and RF and SVM models exhibit superb results and provide more accurate forecasts in short-term load and long-term load forecasting respectively.