Prediction of market-clearing price using neural networks based methods and boosting algorithms

Yıl 2021, , 240 - 246, 15.08.2021

Aslı Boru İpek

https://doi.org/10.35860/iarej.824168

Cited By: 2

Öz

The development of Turkey's industry is contributing to a significant rise in electrical energy demand. Also, electricity is one of the critical elements in the household sectors. Therefore, the planning and managing of electrical energy is of great importance to support economic growth. In addition, effective prediction of market-clearing prices (MCP) is critical topic to meet the increasing energy demand and provide basis for decision making process. In this paper, MCP is predicted using artificial neural network (ANN), convolutional neural network (CNN), and also three boosting algorithms including extreme gradient boosting (XGBoost), categorical boosting (CatBoost), and adaptive boosting (AdaBoost). Various performance metrics are employed to evaluate the prediction performance of proposed methods. The results showed that proposed methods provide reasonable prediction results for energy sector. Hence, producers and consumers can use these methods to determine the bidding strategies and to maximize their profits.

Anahtar Kelimeler

ANN, Boosting algorithms, Market-clearing price, CNN, Energy

Kaynakça

• 1. Kiliç, A. M., Turkey's main energy sources and importance of usage in energy sector. Energy Exploration & Exploitation, 2006. 24(1): p. 1–17.
• 2. Akbalik, M., and Kavcıoğlu, Ş., Energy sector outlook in Turkey. Dumlupinar University Journal of Social Science, Special Issue of XIV. International Symposium on Econometrics, Operations Research and Statistics, 2014. p. 97–118.
• 3. Başoğlu, B., and Bulut, M., Kısa dönem elektrik talep tahminleri için yapay sinir ağları ve uzman sistemler tabanlı hibrit sistem geliştirilmesi. Journal of the Faculty of Engineering & Architecture of Gazi University, 2017. 32(2): p. 575–583.
• 4. Tutun, S., Chou, C.-A., and Canıyılmaz, E., A new forecasting framework for volatile behavior in net electricity consumption: A case study in Turkey. Energy, 93(2), 2015. p. 2406–2422.
• 5. Prabavathi, M., and Gnanadass, R., Electric power bidding model for practical utility system. Alexandria Engineering Journal, 2018. 57(1): p. 277–286.
• 6. Ceyhan, G., Türkiye`de elektrik piyasa takas fiyatı ve sistem marjinal fiyatı farkı üzerine istatistiksel bir çalışma, 2016. [cited 2020 25 September]; Available from: https://blog.metu.edu.tr/e162742/files/2016/08/PTF_vs_SMF_original.pdf.
• 7. Kocadayı, Y., Erkaymaz, O., and Uzun, R., Estimation of Tr81 area yearly electric energy consumption by artificial neural networks. Bilge International Journal of Science and Technology Research, 1(Special Issue), 2017. p. 59–64.
• 8. Bilgili, M., Estimation of net electricity consumption of Turkey. Journal of Thermal Science & Technology, 2009. 29(2): p. 89–98.
• 9. Marvuglia, A., and Messineo, A., Using recurrent artificial neural networks to forecast household electricity consumption. Energy Procedia, 2012. 14: p. 45–55.
• 10. Es, H. A., Kalender, F. Y., and Hamzaçebi, C., Yapay sinir ağları ile Türkiye net enerji talep tahmini. Journal of the Faculty of Engineering and Architecture of Gazi University, 2014. 29(3): p. 495–504.
• 11. Nugaliyadde, A., Somaratne, U., and Wong, K. W., Predicting electricity consumption using deep recurrent neural networks. 2019. arXiv:1909.08182.
• 12. Kaya, M. V., Doyar, B. V., and Demir, F., The effects of internet usage and GDP on electricity consumption: the case of Turkey. Yönetim ve Ekonomi, 2017. 24(1): p. 185–198.
• 13. Çamurdan, Z., and Ganiz, M. C., Machine learning based electricity demand forecasting. 2017 International Conference on Computer Science and Engineering (UBMK), Antalya, 2017. p. 412–417.
• 14. Li, K., and Zhang, T., Forecasting electricity consumption using an improved grey prediction model. Information, 2018. 9: p. 204.
• 15. Sun, T., Zhang, T., Teng, Y., Chen, Z., and Fang, J., Monthly electricity consumption forecasting method based on X12 and STL decomposition model in an integrated energy system. Mathematical Problems in Engineering, 2019. 9012543: p. 1-16.
• 16. Şenocak, F., and Kahveci, H., Periodic price avarages forecasting of MCP in day-ahead market. 2016 National Conference on Electrical, Electronics and Biomedical Engineering (ELECO), Bursa, 2016. p. 664–668.
• 17. Georgilakis, P. S., Market clearing price forecasting in deregulated electricity markets using adaptively trained neural networks. Hellenic Conference on Artificial Intelligence, 2006. p. 56–66.
• 18. Gao, F., Guan, X., Cao, X.-R., and Papalexopoulos, A., Forecasting power market clearing price and quantity using a neural network method. 2000 Power Engineering Society Summer Meeting (Cat. No. 00CH37134), Seattle, WA, 2000. 4: p. 2183–2188.
• 19. Singhal, D., and Swarup, K. S. 2011. Electricity price forecasting using artificial neural networks. Electrical Power and Energy Systems, 33(3): p. 550–555.
• 20. Anamika, and Kumar, N., Market clearing price prediction using ANN in Indian electricity markets. 2016 International Conference on Energy Efficient Technologies for Sustainability (ICEETS), Nagercoil, 2016. p. 454–458.
• 21. Anamika, and Kumar, N., Market-clearing price forecasting for Indian electricity markets. Proceeding of International Conference on Intelligent Communication, Control and Devices, Advances in Intelligent Systems and Computing, Springer, Singapore, 2017. 479: p. 633–642.
• 22. Kabak, M., and Tasdemir, T., Electricity day-ahead market price forecasting by using artificial neural networks: an application for Turkey. Arabian Journal for Science and Engineering, 2020. 45: p. 2317–2326.
• 23. Yan, X., and Chowdhury, N. A., Mid-term electricity market clearing price forecasting: A multiple SVM approach. Electrical Power & Energy Systems, 2014. 58: p 206-214.
• 24. Cheng, C., Luo, B., Miao, S., and Wu, X., Mid-term electricity market clearing price forecasting with sparse data: a case in newly-reformed Yunnan electricity market. Energies, 2016. 9: p. 804.
• 25. Tür, M. R., Mikro şebeke sistemlerine dayalı elektrik piyasasında fiyat oluşturulma senaryosu. Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, 2019. 7(1): p. 192–202.
• 26. Yanar, A., and Akay, M. F. Prediction of electricity market clearing price using machine learning and deep learning. Ç.Ü Fen ve Mühendislik Bilimleri Dergisi, 2020. 39(9), p. 137–141.
• 27. EPİAŞ, Market & Financial Settlement Center, [cited 2020 03 March]; Available from: https://rapor.epias.com.tr/rapor/xhtml/ptfSmfListeleme.xhtml.
• 28. Chen, T., and Guestrin, C., XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016. p. 785–794.
• 29. Qian, N., Wang, X., Fu, Y., Zhao, Z., Xu, J., and Chen, J., Predicting heat transfer of oscillating heat pipes for machining processes based on extreme gradient boosting algorithm. Applied Thermal Engineering, 2020. 164: p. 114521.
• 30. Daoud, E. A., Comparison between XGBoost, LightGBM and CatBoost using a home credit dataset. International Journal of Computer and Information Engineering, 2019. 13(1): p. 6–10.
• 31. Huang, G., Wu, L., Ma, X., Zhang, W., Fan, J., Yu, X., Zeng, W., and Zhou, H., Evaluation of CatBoost method for prediction of reference evapotranspiration in humid regions. Journal of Hydrology, 2019. 574: p. 1029–1041.
• 32. Zhang, Y., Zhao, Z., and Zheng, J., CatBoost: A new approach for estimating daily reference crop evapotranspiration in arid and semi-arid regions of Northern China. Journal of Hydrology, 2020. 588: p. 125087.
• 33. Liu, W., Deng, K., Zhang, X., Cheng, Y., Zheng, Z., Jiang, F., and Peng, J., A semi-supervised tri-catboost method for driving style recognition. Symmetry, 2020. 12(3): p. 336.
• 34. Dong, X., Dong, C., Chen, B., Zhong, J., He, G., and Chen, Z., Application of AdaBoost algorithm based on decision tree in forecasting net power of circulating power plants. 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), Chongqing, China, 2020. p. 747–750.
• 35. Jinbo, S., Xiu, L., and Wenhuang, L., The application of AdaBoost in customer churn prediction. 2007 International Conference on Service Systems and Service Management, 2007. p. 1–6.
• 36. Dhillon, A., and Verma, G. K., Convolutional neural network: a review of models, methodologies and applications to object detection. Progress in Artificial Intelligence, 2020. 9: p. 85–112.
• 37. Albawi, S., Mohammed, T. A., and Al-Zawi, S., Understanding of a convolutional neural network. 2017 International Conference on Engineering and Technology (ICET), Antalya, 2017. p. 1–6.
• 38. Cameron, A.C., and Windmeijer, F.A.G. An R-squared measure of goodness of fit for some common nonlinear regression models. Journal of Econometrics, 1997. 77(2): p. 329–342.
• 39. Makrıdakıs, S., and Hıbon, M., Evaluating Accuracy (or Error) Measures. 1995. [cited 2020 04 March]; Available from: http://www.insead.edu/facultyresearch/research/doc.cfm?did=46875.