engleski

Application of Short Term Load Forecasting using Support Vector Machines in RapidMiner 5.0

This paper presents an application of RapidMiner 5.0 in Load Forecasting using Support Vector Machines. In Power Systems, Load Forecasting is an important task since equilibrium of production and consumption in electricity grid must be maintained at all times. Short Term Load Forecasting(STLF) is forecasting of load up to 72 hours in the future. Method of Support Vector Machines (SVM) has been used for Load Forecasting for its good generalization properties which lead to low Mean Absolute Percentage Error (MAPE). Besides its importance in power system planning, differences in MAPE greater than 0, 5% have significant economic effects in liberalized electricity markets for producers and suppliers. The paper presents application of SVM on high-voltage customers’ consumption in Croatian electricity grid in RapidMiner 5.0. SVM is used as a learning algorithm and its parameters are optimized through Grid Parameter Optimization. Mentioned combination of fast Support Vector Machines with good generalization properties and Grid Parameter Optimization, that optimizes a model, is chosen because it provides a solution for STLF with lower MAPE than previously used similar day technique. First section in this paper gives an overview of the field of application. In second section a description of the algorithm is provided. Third section presents the process in RapidMiner which is followed by results and a conclusion.

short-term load forecasting; support vector machine; rapidminer

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