Probabilistic forecasts of wind power generation in regions with complex topography using deep learning methods: An Arctic case

• URL: http://arxiv.org/abs/2203.07080v1
• Date: Thu, 10 Mar 2022 15:52:11 GMT
• Title: Probabilistic forecasts of wind power generation in regions with complex topography using deep learning methods: An Arctic case
• Authors: Odin Foldvik Eikeland, Finn Dag Hovem, Tom Eirik Olsen, Matteo Chiesa, and Filippo Maria Bianchi
• Abstract summary: This work presents concepts and approaches concerning probabilistic forecasts with deep learning. Deep learning models are used to make probabilistic forecasts of day-ahead power generation from a wind power plant located in Northern Norway.
• Score: 3.3788638227700734
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The energy market relies on forecasting capabilities of both demand and power generation that need to be kept in dynamic balance. Today, when it comes to renewable energy generation, such decisions are increasingly made in a liberalized electricity market environment, where future power generation must be offered through contracts and auction mechanisms, hence based on forecasts. The increased share of highly intermittent power generation from renewable energy sources increases the uncertainty about the expected future power generation. Point forecast does not account for such uncertainties. To account for these uncertainties, it is possible to make probabilistic forecasts. This work first presents important concepts and approaches concerning probabilistic forecasts with deep learning. Then, deep learning models are used to make probabilistic forecasts of day-ahead power generation from a wind power plant located in Northern Norway. The performance in terms of obtained quality of the prediction intervals is compared for different deep learning models and sets of covariates. The findings show that the accuracy of the predictions improves when historical data on measured weather and numerical weather predictions (NWPs) were included as exogenous variables. This allows the model to auto-correct systematic biases in the NWPs using the historical measurement data. Using only NWPs, or only measured weather as exogenous variables, worse prediction performances were obtained.

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