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

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. Key-Words: Particle Swarm Optimization, Artificial Neural Network, Short Term Load Forecasting.

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