TY  - JOUR
TT  - Short-Term Load Forecasting Model Using Flower Pollination Algorithm
AU  - Ateş, Volkan
AU  - Barışçı, Necaattin
PY  - 2017
DA  - December
Y2  - 2017
JF  - International Scientific and Vocational Studies Journal
JO  - ISVOS
PB  - Umut SARAY
WT  - DergiPark
SN  - 2618-5938
SP  - 22
EP  - 29
VL  - 1
IS  - 1
KW  - Artificial Intelligence
KW  - Flower Pollination Algorithm
KW  - Nature-Inspired Optimization
KW  - Short-Term Load Forecasting
N2  - Electricity is natural but not astorable resource and has a vital role in modern life. Balancing betweenconsumption and production of the electricity is highly important for powerplants and production facilities. Researches show that electricity loadconsumption characteristic is highly related to exogenous factors such asweather condition, day type (weekdays, weekends and holidays etc.), seasonaleffects, economic and politic changes (crisis, elections etc.). In this study, we propose a short-term loadforecasting models using artificial intelligence based optimization technique.Proposed 5 different empirical models were optimized using flower pollinationalgorithm (FPA). Training and testing phase of the proposed models held withhistorical load and weather temperature dataset for the years between2011-2014. Forecasting accuracy of the models was measured with Mean AbsolutePercentage Error (MAPE) and monthly minimum approximately %1,79 for February 2013.Results showed that proposed load forecasting model is very competent forshort-term load forecasting.
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UR  - https://dergipark.org.tr/tr/pub/bilmes/article/376325
L1  - https://dergipark.org.tr/tr/download/article-file/401617
ER  -