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• W3122056586 endingPage "116405" @default.
• W3122056586 startingPage "116405" @default.
• W3122056586 abstract "Deep learning models have the potential to advance the short-term decision-making of electricity market participants and system operators by capturing the complex dependences and uncertainties of power system operation. Currently, however, the adoption of global deep learning models for multivariate energy forecasting in power systems is far behind the developments in the deep learning research field. In this context, the objectives of this study are to review recent developments in the field of probabilistic, multivariate, and multihorizon time series forecasting and empirically evaluate the performance of novel global deep learning models for forecasting wind and solar generation, electricity load, and wholesale electricity price for intraday and day-ahead time horizons. Two forecast types, deterministic and probabilistic forecasts, are studied. The evaluation data consist of real-world datasets with hourly resolution at the levels of an individual customer and regional and national electricity market bidding zones. The model evaluation criteria include achievable levels of forecasting accuracy and uncertainty risks, hyperparameter sensitivity, the effect of exogenous variables and fieldwise dataset split, and run-time efficiency factors, such as memory utilization, simulation time, electricity consumption, and convergence rate. We conclude that the performance of the global models is more beneficial for intraday forecasts of heterogeneous datasets with nonuniform patterns of time series, but can be affected by the hyperparameter sensitivity and hardware limitations with the growth of dataset dimensionality. The results can serve as a reference point for the quantitative evaluation of deep learning models for probabilistic multivariate energy forecasting in power systems. • Assessment of short-term deterministic and probabilistic energy forecasts. • Real-world datasets with electricity load and price, and wind and solar time series. • Model evaluation for memory, time, electricity consumption, and convergence rate. • Effects of exogenous variables and fieldwise dataset splitting on forecast quality. • Model performance dependence on dataset heterogeneity, hyperparameters, and hardware." @default.
• W3122056586 created "2021-02-01" @default.
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• W3122056586 date "2021-03-01" @default.
• W3122056586 modified "2023-10-04" @default.
• W3122056586 title "Assessing the performance of deep learning models for multivariate probabilistic energy forecasting" @default.
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• W3122056586 doi "https://doi.org/10.1016/j.apenergy.2020.116405" @default.
• W3122056586 hasPublicationYear "2021" @default.
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