Short-Term Load Forecasting Model Using Flower Pollination Algorithm

Year 2017, Volume: 1 Issue: 1, 22 - 29, 31.12.2017

Volkan Ateş Necaattin Barışçı

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 (weekdays, weekends and holidays etc.), seasonal
effects, economic and politic changes (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 (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 (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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