Mid-term interval load forecasting using multi-output support vector regression with a memetic algorithm for feature selection

Accurate forecasting of mid-term electricity load is an important issue for power system planning and operation. Instead of point load forecasting, this study aims to model and forecast mid-term interval loads up to one month in the form of interval-valued series consisting of both peak and valley points by using MSVR (Multi-output Support Vector Regression). In addition, an MA (Memetic Algorithm) based on the firefly algorithm is used to select proper input features among the feature candidates, which include time lagged loads as well as temperatures. The capability of this proposed interval load modeling and forecasting framework to predict daily interval electricity demands is tested through simulation experiments using real-world data from North America and Australia. Quantitative and comprehensive assessments are performed and the experimental results show that the proposed MSVR-MA forecasting framework may be a promising alternative for interval load forecasting.