Electrical Power Load Forecasting using Hybrid Self-Organizing Maps and Support Vector Machines

Abstract

Forecasting of future electricity demand is very important for decision making in power system operation and planning. In recent years, due to the privatization and deregulation of the power industry, accurate forecasting of future electricity demand has become an important research area for secure operation, management of modern power systems and electricity production in the power generation sector. This paper presents a novel approach for mid-term electricity load forecasting. It uses a hybrid artificial intelligence scheme based on self-organizing maps (SOMs) and support vector machines (SVMs). According to the similarity degree of time series input samples, the SOM is used as a filtering scheme to cluster historical electricity load data into two subsets using the Kohonen rule in an unsupervised manner. As a novel learning machine, the SVM based on statistical learning theory is used for prediction, using support vector regression (SVR). Two epsilon-SVRs are employed to fit the training data of each SOM clustered subset individually in a supervised manner for load prediction. The proposed hybrid SOM-SVR model is evaluated in MATLAB on the electricity load dataset used in the European Network on Intelligent Technologies (EUNITE) competition, arranged by the Eastern Slovakian Electricity Corporation. This proposed model is robust with different data types and can deal well with non- stationarity of load series. Practical application results show that this hybrid technique gives far better prediction accuracy for mid-term electricity load forecasting compared to previous research findings.