Multivariate Short-term Electricity Load Forecasting with Deep Learning and exogenous covariates

University essay from Umeå universitet/Institutionen för tillämpad fysik och elektronik

Abstract: Maintaining the electricity balance between supply and demand is a challenge for electricity suppliers. If there is an under or overproduction, it entails financial costs and affects consumers and the climate. To better understand how to maintain the balance, can the suppliers use short-term forecasts of electricity load. Hence it is of paramount importance that the forecasts are reliable and of high accuracy. Studies show that time series modeling moves towards more data-driven methods, such as Artificial Neural Networks due to their ability to extract complex relationships and flexibility. This study evaluates the performance of a multivariate Deep Autoregressive Neural Network (DeepAR) in ashort-term forecasting scenario of electricity load, with forecasted weather parameters as exogenous covariates. This thesis’s goal is twofold: to test the performance in terms of evaluation metrics of day-ahead forecasts in exogenous covariates’ presence and examine the robustness when exposing DeepAR to deviations in input data. We perform feature selection on given covariates to identify and extract relevant parameters to facilitate the training process and implement a feature importance algorithm to examine which parameters the model considers essential. To test the robustness, we simulate two cases. In the first case, we introduce Quarantine periods, which mask data prior to the forecast range, and the second case introduces an artificial outlier. An exploratory analysis displays significant annual characteristic differences between seasons, therefore do we use two test sets, one in winter and one in summer. The result shows that DeepAR is robust against potential deviations in input data and that DeepAR surpassed both benchmark models in all of the tested scenarios. In the ideal test scenario where weather parametershad the most significant impact (winter), do DeepAR achieve a Normalized Deviation(ND) of 2.5%, compared to the second-best model, with an ND of 4.4%

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