An integrated method based on relevance vector machine for short-term load forecasting

Abstract Short-term electricity load forecasting has become increasingly important due to the privatization and deregulation in the energy market. This study proposes a probabilistic learning method to predict hour-ahead and day-ahead load demand. Unlike methods in previous studies, the proposed method integrates wavelet transform and feature selection as key preprocessing steps. Features are divided into current state related features and historical information related features. Current state related features are forecasted by the regression model before being added into the load prediction model. The entire learning and prediction process is based on the relevance vector machine (RVM) that utilizes load data characteristics. A number of test cases are presented using benchmark datasets from the New York Independent System Operator (NYISO) and ISO New England. Based on the detailed empirical comparison, the proposed RVM-based integrated method outperforms classical time series approaches and state-of-the-art artificial intelligence methods on short-term load forecasting.

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