Electricity price forecasting by clustering-LSSVM

There is a general consensus that the movement of electricity price is crucial for electricity market. As a practical tool to estimate the future prices, electricity price forecaster is of great importance and use for the operations of market participants. This paper presents a hybrid forecast model that integrates clustering algorithm with least square support vector machine (LS-SVM). First, clustering of the data samples are performed, which aims at mining the latent patterns in the data. After that, LS-SVM is applied for the nonlinear regression modeling of electricity price and its influence factors signed with its class, which results in a more efficient training and forecasting. Finally, hourly prices and loads of Californian market are employed to test the proposed approach.