Short-Term Load Forecasting Model Using Flower Pollination Algorithm

Authors : Volkan ATEŞ, Necaattin BARIŞÇI

Pages : 22-29

View : 57 | Download : 17

Publication Date : 2017-12-31

Article Type : Research Paper

Abstract :Electricity is natural but not a storable resource and has a vital role in modern life. Balancing between consumption and production of the electricity is highly important for power plants and production facilities. Researches show that electricity load consumption characteristic is highly related to exogenous factors such as weather condition, day type insert ignore into journalissuearticles values(weekdays, weekends and holidays etc.);, seasonal effects, economic and politic changes insert ignore into journalissuearticles values(crisis, elections etc.);.  In this study, we propose a short-term load forecasting models using artificial intelligence based optimization technique. Proposed 5 different empirical models were optimized using flower pollination algorithm insert ignore into journalissuearticles values(FPA);. Training and testing phase of the proposed models held with historical load and weather temperature dataset for the years between 2011-2014. Forecasting accuracy of the models was measured with Mean Absolute Percentage Error insert ignore into journalissuearticles values(MAPE); and monthly minimum approximately %1,79 for February 2013. Results showed that proposed load forecasting model is very competent for short-term load forecasting.
Keywords : Artificial Intelligence, Flower Pollination Algorithm, Nature Inspired Optimization, Short Term Load Forecasting

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