Short Term Load Forecasting based on Regression models

Electricity becoming more and more important for the development of a secure & sustainable energy system. Electricity Demand growth is likely to be anemic. With the rising complexity of energy systems, new ways to use engineering experience and data collecting to increase load forecasting accurately are needed. With the recent advancements in the field of machine learning to leverage big data sets and draw inferences from a vast data set. The electrical load is predicted by using a variety of research strategies, including deterministic and stochastic procedures. This manuscript intends to estimate load demand by utilizing various stochastic methods for Short-Term Load Forecasting (STLF). The dataset is trained using various regression techniques which include a Decision tree, Support Vector machines (SVM), and Ensembles. The performance of the finest regression methodology is analyzed over parameters like RMSE (root mean square error), MSE (mean square error) and, R-squared, MAE (mean absolute error) & training time. The most effective technique is determined by feeding the best machine learning algorithm to various regression learners over data cross-validation of 5 folds and 7 folds. MATLAB platforms are used to implement the methods.