Hybrid Model of Support Vector Machine and Genetic Algorithm for Forecasting the Annual Peak Electricity Demand of Thailand
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Abstract
One of several energy resources is electricity, which plays a significant role in driving the economic growth of Thailand and has been increasingly used in past decades continuously. In order to gain a reliable and efficient system of power supply handling, the future peak electricity demand, which is inevitably uncertain, will play a crucial role. Therefore, the peak electricity demand has to be predicted by using the most appropriate forecasting models. In this research, a hybrid model of the Support Vector Machine (SVM) and Genetic Algorithm (GA) is proposed in order to forecast future peak demand. The motivation of the proposed model was to develop a complex model that takes advantage of the capability of the SVM model to formulate a predictive complex model. Even though the SVM model is more attractive in terms of forecasting, its performance relies heavily on the appropriate selection of the SVM hyper-parameters. In this regard, the GA was introduced to search for the optimal hyper-parameters of the SVM model. In order to evaluate the performance of the proposed model, it was compared with traditional single forecasting models (i.e., ARIMA and SVM) based on six measures of forecast accuracy. The empirical results indicated that the proposed model outperforms those compared models. Furthermore, the proposed model is able to reduce the errors of the ARIMA model and the SVM model at at least 7.78% and 5.72%, respectively.
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