A Comparison of the Performance of Classification Methods and Artificial Neural Networks for Electricity Load Forecasting

Yıl 2018, Sayı: 2, 233 - 238, 19.08.2018

Gurkan Tuna Ahmet Vatansever Resul Das

Öz

Electricity load forecasting plays a key role for
utility companies. Short-term and medium-term electricity load forecasting
processes allow the utility companies to retain reliable operation and high
energy efficiency. On the other hand, long-term electricity load forecasting
allows the utility companies to minimize the risks. Long-term forecasting also
helps the utility companies to plan and make feasible decisions in regard to
generation and transmission investments. Since there are commercial and technical
implications of electricity load forecasting, the accuracy of the electricity
forecasting is important not only to the utility companies but also to the
consumers. In this paper, we carry out a performance evaluation study to
evaluate the accuracy of different classification approaches for electricity
load forecasting. As shown with the results of the performance evaluation
study, some of the investigated approaches can successfully achieve high
accuracy rates and therefore can be used for short-, mid-, or long-term
electricity load forecasting. 

Anahtar Kelimeler

Load-Forecasting plan, Artificial neural networks, Regression analysis, Support vector machine, Prediction techniques

Kaynakça

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