Short-Term Load Forecasting With Seasonal Decomposition Using Evolution for Parameter Tuning

This paper studies data-driven short-term load forecasting, where historic data are used to predict the expected load for the next 24 h. Our focus is to simplify and automate the estimation and analysis of various forecasting models. We propose a three-stage approach to load forecasting, consisting of preprocessing, forecasting, and postprocessing, where the forecasting stage uses evolution to automatically set the parameters for each model. In our implementation, the preprocessing stage includes removal of daily and weekly seasonality by a nonparametric method. This seasonal pattern is added in the postprocessing stage. The system allows for easy exploration of several forecasting models, without the need to have in-depth knowledge of how to obtain the best performance for each model. We apply the method to several forecasting algorithms and on three datasets: (1) distribution substation; (2) GEFCom 2012; and (3) a transmission level dataset. We find that the forecasting algorithms considered produce significantly more accurate forecasts when combined with our proposed preprocessing stage compared with applying the same algorithms directly on the raw data. We also find that the parameter values chosen by evolution often provide insights into the interplay between the different datasets and forecast models. Software is available online.