Hybrid PSO-ANN application for improved accuracy of short term Load Forecasting

A.G. Abdullah; G.M. Suranegara; D.L. Hakim

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Hybrid PSO-ANN application for improved accuracy of short term Load Forecasting

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A.G. Abdullah, G.M. Suranegara, D.L. Hakim

2014 WSEAS Transactions on Power Systems Vol. 9 Article Cited by 25 SDG 7

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.

Affiliations

Electrical Engineering Education Department, Indonesia University of Education, Jl. Dr. Setiabudhi 207 Bandung, West Java, Indonesia

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