Forecasting of Electricity Prices Using Deep Learning Networks

Estimating future electricity prices is a critical economic success factor for participants in the energy market. In this paper, a modular day-ahead electricity price forecasting approach is proposed. Feeding on historical price data, grid weather data and load forecasts, the model delivers precise estimations of hourly day-ahead electricity prices, for a chosen case study of the German bidding zone. A time series model, based on a combination of convolutional and recurrent neural networks, provides a predictive foundation. This basic estimation is enriched with predictions of wind and solar power generation in Germany, derived from a convolutional neural network feeding on multi-dimensional grid weather data. Together with a load estimation, provided by the German Transmission System Operators, all three input streams are aggregated in a gradient boosting regressor to produce a final estimation for the electricity prices of the upcoming day.

[1]  Eileen F. St. Pierre,et al.  Estimating EGARCH-M models: Science or art? , 1998 .

[2]  Dipti Srinivasan,et al.  A multi-agent based integrated volt-var optimization engine for fast vehicle-to-grid reactive power dispatch and electric vehicle coordination , 2018, Applied Energy.

[3]  D. J. Swider,et al.  Extended ARMA models for estimating price developments on day-ahead electricity markets , 2007 .

[4]  N. Amjady,et al.  Energy price forecasting - problems and proposals for such predictions , 2006 .

[5]  Tara N. Sainath,et al.  Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups , 2012, IEEE Signal Processing Magazine.

[6]  Dipti Srinivasan,et al.  Parallel and reliable probabilistic load forecasting via quantile regression forest and quantile determination , 2018, Energy.

[7]  Wenjie Zhang,et al.  An ensemble machine learning based approach for constructing probabilistic PV generation forecasting , 2017, 2017 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC).

[8]  Steven Kou,et al.  A Jump Diffusion Model for Option Pricing , 2001, Manag. Sci..

[9]  M. Obersteiner,et al.  Forecasting electricity spot-prices using linear univariate time-series models , 2004 .

[10]  Dipti Srinivasan,et al.  An Improved Quantile Regression Neural Network for Probabilistic Load Forecasting , 2019, IEEE Transactions on Smart Grid.

[11]  Y. Ni,et al.  An ARIMA approach to forecasting electricity price with accuracy improvement by predicted errors , 2004, IEEE Power Engineering Society General Meeting, 2004..