Abstract
Based on the empirical, the precision of the forecasting will directly affect the reliability, economy and quality of power supply in power system. An improved particle swarm optimizer (IPSO) is proposed to be used on the least squares support vector machine (LSSVM) algorithm, which optimized the initialization parameters and improved the accuracy of short-term load forecasting. This thesis use the historical data of a certain grid to set up the short-term load forecasting model based on the optimization algorithm. While the data had comprehensive consideration the meteorology, weather, date, type and other factors which influencing the load. Compare with the LSSVM algorithm and the standard PSO-LSSVM, the empirical results show that IPSO-LSSVM model is more applicable in terms of convergence effect, accurate prediction and fast speed. The IPSO not only improves the accuracy of load forecasting, but also prevents LSSVM from great reliance on empirical results and random selection.
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Gong, Q., Lu, W., Gong, W., Wang, X. (2014). Short-Term Load Forecasting of LSSVM Based on Improved PSO Algorithm. In: Li, S., Liu, C., Wang, Y. (eds) Pattern Recognition. CCPR 2014. Communications in Computer and Information Science, vol 483. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45646-0_7
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DOI: https://doi.org/10.1007/978-3-662-45646-0_7
Publisher Name: Springer, Berlin, Heidelberg
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