Hybrid PSO-ANN Application for Improved Accuracy of Short Term Load Forecasting

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Abstract: Short Term Load Forecasting (STLF) is a power system operating procedures that have an important role in terms of realizing the economic electric production. This research focuses on the application of hybrid PSO-ANN algorithm in STLF. Load data grouped by the type of weekdays and holidays. Consumption of electricity load in West Java Indonesia, used as input to the learning algorithm PSO-ANN. Data are grouped according to three clusters, namely the weekdays that starts on Monday to Friday. Weekends are Saturdays and Sundays and national holidays. The forecasting results from the PSO-ANN algorithm compared against the load planning system (LPS) from Indonesia Power Company. The results from the load forecasting PSO-ANN algorithm has a better accuracy than the forecasting of the LPS. Load forecasting accuracy will reduce the level of energy losses and cost of generation.

WSEAS Transactions on Power Systems, ISSN / E-ISSN: 1790-5060 / 2224-350X, Volume 9, 2014, Art. #45

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A. G. Abdullah, G. M. Suranegara, D. L. Hakim, "Hybrid PSO-ANN Application for Improved Accuracy of Short Term Load Forecasting," WSEAS Transactions on Power Systems, vol. 9, pp. 446-451, 2014, DOI:
A. G. Abdullah, G. M. Suranegara, D. L. Hakim. Hybrid PSO-ANN Application for Improved Accuracy of Short Term Load Forecasting. WSEAS Transactions on Power Systems. 2014;9:446-451.
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