Probabilistic forecasts of wind power generation in regions with complex topography using deep learning methods: An Arctic case
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 m...
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ftdatacite:10.48550/arxiv.2203.07080 2023-05-15T15:09:56+02:00 Probabilistic forecasts of wind power generation in regions with complex topography using deep learning methods: An Arctic case Eikeland, Odin Foldvik Hovem, Finn Dag Olsen, Tom Eirik Chiesa, Matteo Bianchi, Filippo Maria 2022 https://dx.doi.org/10.48550/arxiv.2203.07080 https://arxiv.org/abs/2203.07080 unknown arXiv Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 CC-BY Machine Learning cs.LG Data Analysis, Statistics and Probability physics.data-an FOS Computer and information sciences FOS Physical sciences Preprint Article article CreativeWork 2022 ftdatacite https://doi.org/10.48550/arxiv.2203.07080 2022-04-01T14:50:22Z 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. : 16 pages, 8 Figures, 4 Tables Report Arctic Northern Norway DataCite Metadata Store (German National Library of Science and Technology) Arctic Norway |
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topic |
Machine Learning cs.LG Data Analysis, Statistics and Probability physics.data-an FOS Computer and information sciences FOS Physical sciences |
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Machine Learning cs.LG Data Analysis, Statistics and Probability physics.data-an FOS Computer and information sciences FOS Physical sciences Eikeland, Odin Foldvik Hovem, Finn Dag Olsen, Tom Eirik Chiesa, Matteo Bianchi, Filippo Maria Probabilistic forecasts of wind power generation in regions with complex topography using deep learning methods: An Arctic case |
topic_facet |
Machine Learning cs.LG Data Analysis, Statistics and Probability physics.data-an FOS Computer and information sciences FOS Physical sciences |
description |
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. : 16 pages, 8 Figures, 4 Tables |
format |
Report |
author |
Eikeland, Odin Foldvik Hovem, Finn Dag Olsen, Tom Eirik Chiesa, Matteo Bianchi, Filippo Maria |
author_facet |
Eikeland, Odin Foldvik Hovem, Finn Dag Olsen, Tom Eirik Chiesa, Matteo Bianchi, Filippo Maria |
author_sort |
Eikeland, Odin Foldvik |
title |
Probabilistic forecasts of wind power generation in regions with complex topography using deep learning methods: An Arctic case |
title_short |
Probabilistic forecasts of wind power generation in regions with complex topography using deep learning methods: An Arctic case |
title_full |
Probabilistic forecasts of wind power generation in regions with complex topography using deep learning methods: An Arctic case |
title_fullStr |
Probabilistic forecasts of wind power generation in regions with complex topography using deep learning methods: An Arctic case |
title_full_unstemmed |
Probabilistic forecasts of wind power generation in regions with complex topography using deep learning methods: An Arctic case |
title_sort |
probabilistic forecasts of wind power generation in regions with complex topography using deep learning methods: an arctic case |
publisher |
arXiv |
publishDate |
2022 |
url |
https://dx.doi.org/10.48550/arxiv.2203.07080 https://arxiv.org/abs/2203.07080 |
geographic |
Arctic Norway |
geographic_facet |
Arctic Norway |
genre |
Arctic Northern Norway |
genre_facet |
Arctic Northern Norway |
op_rights |
Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.48550/arxiv.2203.07080 |
_version_ |
1766341024666353664 |