Short-Term Load Forecasting Model Using Flower Pollination Algorithm
| Autor: | ATEŞ, Volkan, BARIŞÇI, Necaattin |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Computer Science
Information System Short-Term Load Forecasting Nature-Inspired Optimization Flower Pollination Algorithm Artificial Intelligence Engineering Electrical and Electronic Bilgisayar Bilimleri Bilgi Sistemleri Mühendislik Elektrik ve Elektronik Artificial Intelligence Flower Pollination Algorithm Nature-Inspired Optimization Short-Term Load Forecasting |
| Zdroj: | Volume: 1, Issue: 1 22-29 International Scientific and Vocational Studies Journal |
| ISSN: | 2618-5938 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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