Mutual Information-Based Inputs Selection for Electric Load Time Series Forecasting

Abstract

Providing accurate load forecast to electric utility corporations is essential in order to reduce their operational costs and increase profits. Hence, training set selection is an important preprocessing step which has to be considered in practice in order to increase the accuracy of load forecasts. The usage of mutual information (MI) has been recently proposed in regression tasks, mostly for feature selection and for identifying the real instances from training sets that contains noise and outliers. This paper proposes a methodology for the training set selection in a least squares support vector machines (LS-SVMs) load forecasting model. A new application of the concept of MI is presented for the selection of a training set based on MI computation between initial training set instances and testing set instances. Accordingly, several LS-SVMs models have been trained, based on the proposed methodology, for hourly prediction of electric load for one day ahead. The results obtained from a real-world data set indicate that the proposed method increases the accuracy of load forecasting as well as reduces the size of the initial training set needed for model training.

Research on Regional Short-Term Power Load Forecasting Model and Case Analysis

“…(3) Considering the influence of multiple meteorological factors without coupling effect; (4) Considering the influence of meteorological index formed by coupling multiple meteorological factors; (5) Consider the influence of real-time meteorological factors. In the actual prediction process, the appropriate treatment scheme is selected according to local conditions in combination with the actual local weather and load conditions.…”

Multi-Objective Evolutionary Instance Selection for Regression Tasks

“…If the loss of mutual information with respect to its neighbors was similar to the instances near the examined instance, this instance was included in the selected dataset. The authors of [ 30 , 31 ] extended this idea to instance selection in time series prediction by calculating the mutual information between every instance from the training set and the currently evaluated instance, then to order the training set in descending order by the distances and selected the predefined number of points.…”

Modular Predictor for Day-Ahead Load Forecasting and Feature Selection for Different Hours

“…The artificial neural network method is applied to the STLF of power systems owing to its self-learning ability and robustness to data noise. However, shortcomings such as the difficulty in determining initial network parameters and over-fitting still exist [19]. By adopting a structural risk minimization principle, the complexity and the learning ability of an SVM can be balanced.…”