Annual Power Load Forecasting Using Support Vector Regression Machines: A Study on Guangdong Province of China 1985-2008
نویسندگان
چکیده
Load forecasting has always been the essential part of an efficient power system operation and planning. A novel approach based on support vector machines is proposed in this paper for annual power load forecasting. Different kernel functions are selected to construct a combinatorial algorithm. The performance of the new model is evaluated with a real-world dataset, and compared with two neural networks and some traditional forecasting techniques. The results show that the proposed method exhibits superior performance. Keywords—combinatorial algorithm, data mining, load forecasting, support vector machines
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