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

Year 2018, Issue: 2, 233 - 238, 19.08.2018

Abstract

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. 

References

  • Almeshaiei, E., & Soltan, H. (2011). A methodology for Electric Power Load Forecasting. Alexandria Engineering Journal, 50(2), 137-144. Guerard, J. B., & Schwartz, E. (2010). Quantitative corporate finance. New York: Springer. Hastie, T. J., Tibshirani, R. J., & Friedman, J. H. (2009). The elements of statistical learning: data mining, inference, and prediction. Springer New York Inc., USA. Hernandez, L., Baladron, C., Aguiar, J. M., Carro, B., Sanchez-Esguevillas, A. J., Lloret, J., & Massana, J. (2014). A survey on electric power demand forecasting: Future trends in smart grids, microgrids and smart buildings. IEEE Communications Surveys & Tutorials, 16(3), 1460-1495. Popescu, M. –C., Balas, V. E., Perescu-Popescu, L., & Mastorakis, N. (2009). Multilayer perceptron and neural networks. WSEAS Trans. Cir. and Sys., 8(7), 579-588. Reynaldi, A., Lukas, S., & Margaretha, H. (2012). Backpropagation and Levenberg-Marquardt Algorithm for Training Finite Element Neural Network. Proceedings of the 2012 Sixth UKSim/AMSS European Symposium on Computer Modeling and Simulation (EMS '12) (pp. 89-94). Steinwart, I. (2014). Support vector machines. Springer. Suganthi, L., & Samuel, A. A. (2012). Energy models for demand forecasting—A review. Renewable and Sustainable Energy Reviews, 16(2), 1223-1240. Weron, R. (2006). Modeling and forecasting electricity loads and prices. Chichester: Wiley & Sons. Yan, X., & Su, X. G. (2009). Linear regression analysis theory and computing. Singapore: World Scientific. https://www.cs.waikato.ac.nz/ml/weka/ https://www.mathworks.com/products/matlab.html
Year 2018, Issue: 2, 233 - 238, 19.08.2018

Abstract

References

  • Almeshaiei, E., & Soltan, H. (2011). A methodology for Electric Power Load Forecasting. Alexandria Engineering Journal, 50(2), 137-144. Guerard, J. B., & Schwartz, E. (2010). Quantitative corporate finance. New York: Springer. Hastie, T. J., Tibshirani, R. J., & Friedman, J. H. (2009). The elements of statistical learning: data mining, inference, and prediction. Springer New York Inc., USA. Hernandez, L., Baladron, C., Aguiar, J. M., Carro, B., Sanchez-Esguevillas, A. J., Lloret, J., & Massana, J. (2014). A survey on electric power demand forecasting: Future trends in smart grids, microgrids and smart buildings. IEEE Communications Surveys & Tutorials, 16(3), 1460-1495. Popescu, M. –C., Balas, V. E., Perescu-Popescu, L., & Mastorakis, N. (2009). Multilayer perceptron and neural networks. WSEAS Trans. Cir. and Sys., 8(7), 579-588. Reynaldi, A., Lukas, S., & Margaretha, H. (2012). Backpropagation and Levenberg-Marquardt Algorithm for Training Finite Element Neural Network. Proceedings of the 2012 Sixth UKSim/AMSS European Symposium on Computer Modeling and Simulation (EMS '12) (pp. 89-94). Steinwart, I. (2014). Support vector machines. Springer. Suganthi, L., & Samuel, A. A. (2012). Energy models for demand forecasting—A review. Renewable and Sustainable Energy Reviews, 16(2), 1223-1240. Weron, R. (2006). Modeling and forecasting electricity loads and prices. Chichester: Wiley & Sons. Yan, X., & Su, X. G. (2009). Linear regression analysis theory and computing. Singapore: World Scientific. https://www.cs.waikato.ac.nz/ml/weka/ https://www.mathworks.com/products/matlab.html
There are 1 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Gurkan Tuna

Ahmet Vatansever

Resul Das

Publication Date August 19, 2018
Published in Issue Year 2018Issue: 2

Cite

APA Tuna, G., Vatansever, A., & Das, R. (2018). A Comparison of the Performance of Classification Methods and Artificial Neural Networks for Electricity Load Forecasting. The Eurasia Proceedings of Science Technology Engineering and Mathematics(2), 233-238.