Commenced in January 2007
Paper Count: 31515
Electricity Price Forecasting: A Comparative Analysis with Shallow-ANN and DNN
Abstract:Electricity prices have sophisticated features such as high volatility, nonlinearity and high frequency that make forecasting quite difficult. Electricity price has a volatile and non-random character so that, it is possible to identify the patterns based on the historical data. Intelligent decision-making requires accurate price forecasting for market traders, retailers, and generation companies. So far, many shallow-ANN (artificial neural networks) models have been published in the literature and showed adequate forecasting results. During the last years, neural networks with many hidden layers, which are referred to as DNN (deep neural networks) have been using in the machine learning community. The goal of this study is to investigate electricity price forecasting performance of the shallow-ANN and DNN models for the Turkish day-ahead electricity market. The forecasting accuracy of the models has been evaluated with publicly available data from the Turkish day-ahead electricity market. Both shallow-ANN and DNN approach would give successful result in forecasting problems. Historical load, price and weather temperature data are used as the input variables for the models. The data set includes power consumption measurements gathered between January 2016 and December 2017 with one-hour resolution. In this regard, forecasting studies have been carried out comparatively with shallow-ANN and DNN models for Turkish electricity markets in the related time period. The main contribution of this study is the investigation of different shallow-ANN and DNN models in the field of electricity price forecast. All models are compared regarding their MAE (Mean Absolute Error) and MSE (Mean Square) results. DNN models give better forecasting performance compare to shallow-ANN. Best five MAE results for DNN models are 0.346, 0.372, 0.392, 0,402 and 0.409.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1317174Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 708
 H.Y. Yamin, S.M. Shahidehpour, and Z. Li. Adaptive short-term electricity price forecasting using artificial neural networks in the restructured power markets. International Journal of Electrical Power & Energy Systems, 26(8):571 – 581, 2004.
 David Young, Stephen Poletti, and Oliver Browne. Can agent-based models forecast spot prices in electricity markets? evidence from the new zealand electricity market. Energy Economics, 45:419 – 434, 2014.
 Fabio Genoese and Massimo Genoese. Assessing the value of storage in a future energy system with a high share of renewable electricity generation; an agent-based simulation approach with integrated optimization methods. Energy Systems, 5(1):19, 2014.
 T Kristiansen. A time series spot price forecast model for the nord pool market. International Journal of Electrical Power & Energy Systems, 61:20 – 26, 2014.
 Rafal Weron and Adam Misiorek. Forecasting spot electricity prices: A comparison of parametric and semiparametric time series models. International Journal of Forecasting, 24(Energy Forecasting):744 – 763, 2008.
 Dogan Keles, Jonathan Scelle, Florentina Paraschiv, and Wolf Fichtner. Extended forecast methods for day-ahead electricity spot prices applying artificial neural networks. Applied Energy, 162:218 – 230, 2016.
 P Mandal, AK Srivastava, T Senjyu, and M Negnevitsky. A new recursive neural network algorithm to forecast electricity price for pjm day-ahead market. International Journal of Energy Research, 34(6):507 – 522, 2009.
 G.P. Girish. Spot electricity price forecasting in indian electricity market using autoregressive-garch models. Energy Strategy Reviews, 2016.
 Heping Liu and Jing Shi. Applying arma–garch approaches to forecasting short-term electricity prices. Energy Economics, 37:152 – 166, 2013.
 AJ Conejo, MA Plazas, R Espinola, and AB Molina. Day-ahead electricity price forecasting using the wavelet transform and arima models. IEEE Transactions on Power Systems, 20(2):1035 – 1042, 2005.
 Hang T. Nguyen and Ian T. Nabney. Short-term electricity demand and gas price forecasts using wavelet transforms and adaptive models. Energy, pages 3674 – 3685, 2010.
 S. Voronin and J. Partanen. Forecasting electricity price and demand using a hybrid approach based on wavelet transform, arima and neural networks. International Journal of Energy Research, 38(5):626–637, 2014.
 M. Shafie-khah, M. Parsa Moghaddam, and M.K. Sheikh-El-Eslami. Price forecasting of day-ahead electricity markets using a hybrid forecast method. Energy Conversion and Management, 52:2165 – 2169, 2011.
 Suyi Li Mohammed Shahidehpour, Hatim Yamin. Market Operations in Electric Power Systems. John Wiley & Sons Ltd, 2002.
 Kumar A Aggarwal S. K, Saini L. M. Electricity price forecasting in deregulated markets: A review and evaluation. International Journal of Electrical Power And Energy Systems, 2009.
 Fazil Gokgoz Fahrettin Filiz. Electricity price forecasting in turkey with artificial neural network models. Investment Management and Financial Innovations, 2016.
 Hu M. Y. Guoqiang Z, Patuwo. Forecasting with artificial neural networks: The state of the art. International Journal of Forecasting, 1998.
 Yoshua Bengio et al. Learning deep architectures for ai. Foundations and trends R in Machine Learning, 2(1):1–127, 2009.
 Olivier Delalleau and Yoshua Bengio. Shallow vs. deep sum-product networks. In J. Shawe-Taylor, R. S. Zemel, P. L. Bartlett, F. Pereira, and K. Q. Weinberger, editors, Advances in Neural Information Processing Systems 24, pages 666–674. Curran Associates, Inc., 2011.
 Ping-Huan Kuo and Chiou-Jye Huang. An electricity price forecasting model by hybrid structured deep neural networks. Sustainability, 10(4):1280, 2018.
 Tomaso Poggio, Hrushikesh Mhaskar, Lorenzo Rosasco, Brando Miranda, and Qianli Liao. Why and when can deep-but not shallow-networks avoid the curse of dimensionality: A review. International Journal of Automation and Computing, 14(5):503–519, Oct 2017.
 K. Greff, R. K. Srivastava, J. Koutník, B. R. Steunebrink, and J. Schmidhuber. Lstm: A search space odyssey. IEEE Transactions on Neural Networks and Learning Systems, 28(10):2222–2232, Oct 2017.
 S. Hosein and P. Hosein. Load forecasting using deep neural networks. In 2017 IEEE Power Energy Society Innovative Smart Grid Technologies Conference (ISGT), pages 1–5, April 2017.
 Hrushikesh Mhaskar, Qianli Liao, and Tomaso A. Poggio. Learning real and boolean functions: When is deep better than shallow. CoRR, abs/1603.00988, 2016.