17th AIAI 2021, 25 - 27 June 2021, Greece

Short Term Net Imbalance Volume Forecasting through Machine and Deep Learning: A UK case study

Elpiniki Makri, John Koskinas, Apostolos Tsolakis, Dimosthenis Ioannidis, Dimitrios Tzovaras

Abstract:

  As energy markets become more and more dynamic, the importance of price forecasting has gained a lot of attention over the last few years. Considering also the introduction of new business models and roles, such as Aggregators and energy flexibility traders, in the constantly evolving energy landscape which follows the general opening of the European electricity markets, the need for anticipating energy price trends and flows holds significant business value. On top of that, the exponential renewable energy sources penetration, adds to the challenges introduced to this dynamic scheme of things. Given their volatile and intermittent nature, supply-demand imbalance can reach critical margins, threatening the overall system stability. In the scope of reducing the power imbalances, a forecast for the imbalance volume will be beneficial either from the perspective of the system operator that could minimise mitigation costs, or the market participants that could target extreme prices for maximising their profit, while effectively managing their risks. The development of a deep learning algorithm for the prediction of the net imbalance volume in the UK market is proposed in this paper in comparison with a common but widely used machine learning approach, namely a gradient boosting trees regression model. The variables which contributed the most on those models were mainly the historical values of net imbalance volume. The deep neural network returns a Root mean squared error (RMSE) and Mean Absolute Error (MAE) equal to 200 and 152 MWh in a range of values between [-1.5,2.0] GWh, respectively, the gradient boosting trees model has an RMSE and MAE equal to 203 and 154 MWh, in contrast to an ARIMA model that has RMSE and MAE equal to 226 and 173 MWh.  

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