TY  - JOUR
T1  - A Comparison of the Performance of Classification Methods and Artificial Neural Networks for Electricity Load Forecasting
AU  - Tuna, Gurkan
AU  - Vatansever, Ahmet
AU  - Das, Resul
PY  - 2018
DA  - August
JF  - The Eurasia Proceedings of Science Technology Engineering and Mathematics
JO  - EPSTEM
PB  - ISRES Publishing
WT  - DergiPark
SN  - 2602-3199
SP  - 233
EP  - 238
IS  - 2
LA  - en
AB  - Electricity load forecasting plays a key role forutility companies. Short-term and medium-term electricity load forecastingprocesses allow the utility companies to retain reliable operation and highenergy efficiency. On the other hand, long-term electricity load forecastingallows the utility companies to minimize the risks. Long-term forecasting alsohelps the utility companies to plan and make feasible decisions in regard togeneration and transmission investments. Since there are commercial and technicalimplications of electricity load forecasting, the accuracy of the electricityforecasting is important not only to the utility companies but also to theconsumers. In this paper, we carry out a performance evaluation study toevaluate the accuracy of different classification approaches for electricityload forecasting. As shown with the results of the performance evaluationstudy, some of the investigated approaches can successfully achieve highaccuracy rates and therefore can be used for short-, mid-, or long-termelectricity load forecasting. 
KW  - Load-Forecasting plan
KW  - Artificial neural networks
KW  - Regression analysis
KW  - Support vector machine
KW  - Prediction techniques
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UR  - https://dergipark.org.tr/en/pub/epstem/issue//455941
L1  - https://dergipark.org.tr/en/download/article-file/528320
ER  -