Comparison of Machine Learning Models for the Prediction of System Marginal Price of Greek Energy Market
The Greek Energy Market is structured as a mandatory pool where the producers make their bid offers in day-ahead basis. The System Operator solves an optimization routine aiming at the minimization of the cost of produced electricity. The solution of the optimization problem leads to the calculation of the System Marginal Price (SMP). Accurate forecasts of the SMP can lead to increased profits and more efficient portfolio management from the producer`s perspective. Aim of this study is to provide a comparative analysis of various machine learning models such as artificial neural networks and neuro-fuzzy models for the prediction of the SMP of the Greek market. Machine learning algorithms are favored in predictions problems since they can capture and simulate the volatilities of complex time series.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.2643644Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1161
 F. Ziel, R. Steinert, and S. Husmann, “Efficient modeling and forecasting of electricity spot prices”, Energy Econ, vol. 47, pp. 98-111, 2015.
 D. Singhal, and K.S. Swarup, “Electricity price forecasting using artificial neural networks”, Int J Elect Power Energy Syst, vol. 33, pp. 550-555, 2011.
 S.K. Aggarwal, L.M. Saini, and A. Kumar, “Electricity price forecasting in deregulated markets: A review and evaluation”, Int J Elect Power Energy Syst, vol. 31, pp. 13-22, 2009.
 A. Ahmadi, M. Charwand, and J. Aghaei, “Risk-constrained optimal strategy for retailer forward contract portfolio”, Int J Elect Power Energy Sys, vol. 53, pp. 704-713, 2013.
 M. Carrión, A.J. Conejo, and J.M. Arroyo, “Forward contracting and selling price determination for a retailer”, IEEE Trans Power Sys, vol. 22, pp. 2105-2114, 2007.
 S. Yousefi, M.P. Moghaddam, and V.J. Majd, “Optimal real time pricing in an agent-based retail market using a comprehensive demand response model”, Energy, vol. 36, pp. 5716-5727, 2011.
 J. Yang, G. Zhang, K. Ma, “Matching supply with demand: A power control and real time pricing approach”, Appl. Energy, vol. 61, pp. 111-117, 2014.
 R. Weron, “Electricity price forecasting: A review of the state-of-the-art with a look into the future”, Int J Forecast, vol. 30, pp. 1030-1081, 2014.
 M. Cerjan, I. Krzelj, M. Vidak, and M. Delimar, “A literature review with statistical analysis of electricity price forecasting methods”, In Proceedings of the 2013 EUROCON, 756-763.
 R. Gareta, L.M. Romeo, and A. Gil, “Forecasting of electricity prices with neural networks”, Energy Conv Manag, vol. 47, pp. 1770-1778, 2006.
 N. Amjady, and F. Keynia, “Day ahead price forecasting of electricity markets by a mixed data model and hybrid forecast method”, Int J Electr Power Energy Syst, vol. 30, pp. 533-546, 2008.
 N. Amjady, and F. Keynia, “Day-ahead price forecasting of electricity markets by mutual information technique and cascaded neuro-evolutionary algorithm”. IEEE Trans Power Sys, vol. 24, pp. 306-308, 2009.
 S. Chakravarty, and P.K. Dash, “Dynamic filter weights neural network model integrated with differential evolution for day-ahead price forecasting in energy market”, Expert Sys Appl, vol. 38, pp. 10974-10982, 2011.
 P. Dev, and M.A. Martin, “Using neural networks and extreme value distributions to model electricity pool prices: Evidence from the Australian National Electricity Market 1998–2013”, Energy Conv Manag, vol. 84, pp. 122-132, 2014.
 B. Neupane, K.S. Perera, Z. Aung, and W.L. Woon, “Artificial neural network-based electricity price forecasting for smart grid deployment”, In Proceedings of the 2012 International Conference on Computer Systems and Industrial Informatics, Sharjah, pp. 1-6.
 H.T. Pao, “Forecasting electricity market pricing using artificial neural networks”, Energy Conv Manag, vol 48, pp. 907-912, 2007.
 T.M. Peng, N.F. Hubele, and G.G. Karady, “Advancement in the application of neural networks for short-term load forecasting”, IEEE Trans Power Syst, vol. 7, pp. 250-257, 1992.
 N.M. Pindoriya, S.N. Singh, and S.K. Singh, “An adaptive wavelet neural network-based energy price forecasting in electricity markets”, IEEE Trans Power Syst, vol. 23, pp. 1423-1431, 2008
 P. Areekul, Y. Senjyu, H. Toyama, and A. Yona, “A hybrid ARIMA and neural network model for short-term price forecasting in deregulated market”, IEEE Trans Power Syst, vol. 25, pp. 524-530, 2010.
 A. Khosravi, S. Nahavandi, and D. Creighton, “A neural network-GARCH-based method for construction of Prediction Intervals”, Elect Power Sys Res, vol. 96, pp. 185-193, 2013.
 N. Amjady, A. Daraeepour, and F. Keynia, “Day-ahead electricity price forecasting by modified relief algorithm and hybrid neural network”, IET Gener Transm Distrib, vol. 4, pp. 432-444, 2010.
 S. Hassan, A. Khosravi, J. Jaafar, amd M.Q. Raza, “Electricity load and price forecasting with influential factors in a deregulated power industry”, In Proceedings of the 2014 9th International Conference on System of Systems Engineering (SOSE), 79-84.
 H.S. Sandhu, L. Fang, and L. Guan, “Forecasting day-ahead electricity prices using data mining and neural network techniques”, In Proceedings of the 2014 11th International Conference on Service Systems and Service Management (ICSSSM), 1-6.
 A. Agarwal, A. Ojha, S,C, Tewari, and M.M. Tripathi, “Hourly load and price forecasting using ANN and fourier analysis”, In Proceedings of the 2014 6th IEEE Power India International Conference (PIICON), 1-6.
 E. Elattar, and E.K. Shebin, “Day-ahead price forecasting of electricity markets based on local informative vector machine”, IET Gener Trans Distrib, vol. 7, pp. 1063-1071, 2013.
 H. Shayeghi, and A. Ghasemi, “Day-ahead electricity prices forecasting by a modified CGSA technique and hybrid WT in LSSVM based scheme”, Energy Conv Manag, vol. 74, pp. 482-491, 2013.
 N.A. Shrivastava, A. Khosravi, and B.K. Panigrahi, “Prediction interval estimation for electricity price and demand using support vector machines”, In Proceedings of the 2014 International Joint Conference on Neural Networks (IJCNN), 3995-4002.
 S. Fan, C. Mao, and L. Chen, “Next-day electricity-price forecasting using a hybrid network”, IET Gener Trans Distrib, vol. 1, pp. 176-82, 2007.
 D. Niu, D. Liu, and D.D. Wu, “A soft computing system for day-ahead electricity price forecasting”, Appl. Soft Comp, vol. 10, pp. 868-875, 2010.
 M. Bashari, A. Darudi, and N. Raeyatdoost, “Kalman Fusion algorithm in electricity price forecasting”, In Proceedings of the 2014 14th International Conference on Environment and Electrical Engineering (EEEIC), 313-317.
 LAGIE S.A.: http://www.lagie.gr/
 D. Graupe. Principles of Artificial Neural Networks. Singapore: World Scientific Publishing Company; 2007.
 J-SR. Jang, “ANFIS: adaptive-network-based fuzzy inference system”, Syst Man Cyber, vol. 23, pp. 665-685, 1993.
 B. Soldo, P. Potocnik, G. Simunovi, T. Sari, and E. Govekar, “Improving the residential natural gas consumption forecasting models by using solar radiation”, Energy and Buildings, vol. 69, pp. 498-506, 2014.
 R.A. Hatami, H. Seifi, and M.K. Sheikh-El-Eslami, “Optimal selling price and energy procurement strategies for a retailer in an electricity market”, Elect Power Syst Res, vol. 79, 246-254, 2009
 J.M. Yusta, I.J.R. Rosado, J.A.D. Navarro, J.M.P. Vidal, “Optimal electricity price calculation model for retailers in a deregulated market”, Int J Elect Power Energy Syst vol.27, pp. 437-447, 2005
 S.A. Gabriel, M.F. Genc, and S. Balakrishnan, “A simulation approach to balancing annual risk and reward in retail electrical power markets”, IEEE Trans Power Syst, vol. 17, pp. 1050-1057, 2002
 S.A. Gabriel, A.J. Conejo, M.A. Plazas, and S. Balakrishnan, “Optimal price and quantity determination for retail electric power contracts”, IEEE Trans Power Syst, vol. 21, pp. 180-187, 2006
 M. Carrión, J.M. Arroyo, and A.J. Conejo, “A bilevel stochastic programming approach for retailer futures market trading”, IEEE Trans Power Syst, vol. 24, pp. 1446-1456, 2009
 N. Mahmoudi-Kohan, M. Parsa Moghaddam, and M.K. Sheikh-El-Eslami, “An annual framework for clustering-based pricing for an electricity retailer”, Elect Power Syst Res, vol. 80, pp. 1042-1048, 2010