An integrated machine learning model for day-ahead electricity price forecasting

This paper proposes a novel model for short-term electricity price forecasting based on an integration of two machine learning technologies: Bayesian Clustering by Dynamics (BCD) and Support Vector Machine (SVM). The proposed forecasting system adopts an integrated architecture. Firstly, a BCD classifier is applied to cluster the input data set into several subsets in an unsupervised manner. Then, groups of 24 SVMs for the next day's electricity price profile are used to fit the training data of each subset in a supervised way. To demonstrate the effectiveness, the proposed model has been trained and tested on the data of the historical energy prices from the New England electricity market.