Using weather data in energy time series forecasting: the benefit of input data transformations
Abstract Renewable energy systems depend on the weather, and weather information, thus, plays a crucial role in forecasting time series within such renewable energy systems. However, while weather data are commonly used to improve forecast accuracy, it still has to be determined in which input shape...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2023-11-01
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| Series: | Energy Informatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s42162-023-00299-8 |
| _version_ | 1797636624294608896 |
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| author | Oliver Neumann Marian Turowski Ralf Mikut Veit Hagenmeyer Nicole Ludwig |
| author_facet | Oliver Neumann Marian Turowski Ralf Mikut Veit Hagenmeyer Nicole Ludwig |
| author_sort | Oliver Neumann |
| collection | DOAJ |
| description | Abstract Renewable energy systems depend on the weather, and weather information, thus, plays a crucial role in forecasting time series within such renewable energy systems. However, while weather data are commonly used to improve forecast accuracy, it still has to be determined in which input shape this weather data benefits the forecasting models the most. In the present paper, we investigate how transformations for weather data inputs, i. e., station-based and grid-based weather data, influence the accuracy of energy time series forecasts. The selected weather data transformations are based on statistical features, dimensionality reduction, clustering, autoencoders, and interpolation. We evaluate the performance of these weather data transformations when forecasting three energy time series: electrical demand, solar power, and wind power. Additionally, we compare the best-performing weather data transformations for station-based and grid-based weather data. We show that transforming station-based or grid-based weather data improves the forecast accuracy compared to using the raw weather data between 3.7 and 5.2%, depending on the target energy time series, where statistical and dimensionality reduction data transformations are among the best. |
| first_indexed | 2024-03-11T12:37:44Z |
| format | Article |
| id | doaj.art-7fe36b276171407f92a995d9ef748f9e |
| institution | Directory Open Access Journal |
| issn | 2520-8942 |
| language | English |
| last_indexed | 2024-03-11T12:37:44Z |
| publishDate | 2023-11-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Energy Informatics |
| spelling | doaj.art-7fe36b276171407f92a995d9ef748f9e2023-11-05T12:30:53ZengSpringerOpenEnergy Informatics2520-89422023-11-016112310.1186/s42162-023-00299-8Using weather data in energy time series forecasting: the benefit of input data transformationsOliver Neumann0Marian Turowski1Ralf Mikut2Veit Hagenmeyer3Nicole Ludwig4Institute for Automation and Applied Informatics, Karlsruhe Institute of TechnologyInstitute for Automation and Applied Informatics, Karlsruhe Institute of TechnologyInstitute for Automation and Applied Informatics, Karlsruhe Institute of TechnologyInstitute for Automation and Applied Informatics, Karlsruhe Institute of TechnologyCluster of Excellence Machine Learning, University of TübingenAbstract Renewable energy systems depend on the weather, and weather information, thus, plays a crucial role in forecasting time series within such renewable energy systems. However, while weather data are commonly used to improve forecast accuracy, it still has to be determined in which input shape this weather data benefits the forecasting models the most. In the present paper, we investigate how transformations for weather data inputs, i. e., station-based and grid-based weather data, influence the accuracy of energy time series forecasts. The selected weather data transformations are based on statistical features, dimensionality reduction, clustering, autoencoders, and interpolation. We evaluate the performance of these weather data transformations when forecasting three energy time series: electrical demand, solar power, and wind power. Additionally, we compare the best-performing weather data transformations for station-based and grid-based weather data. We show that transforming station-based or grid-based weather data improves the forecast accuracy compared to using the raw weather data between 3.7 and 5.2%, depending on the target energy time series, where statistical and dimensionality reduction data transformations are among the best.https://doi.org/10.1186/s42162-023-00299-8Energy time seriesForecastingWeather data |
| spellingShingle | Oliver Neumann Marian Turowski Ralf Mikut Veit Hagenmeyer Nicole Ludwig Using weather data in energy time series forecasting: the benefit of input data transformations Energy Informatics Energy time series Forecasting Weather data |
| title | Using weather data in energy time series forecasting: the benefit of input data transformations |
| title_full | Using weather data in energy time series forecasting: the benefit of input data transformations |
| title_fullStr | Using weather data in energy time series forecasting: the benefit of input data transformations |
| title_full_unstemmed | Using weather data in energy time series forecasting: the benefit of input data transformations |
| title_short | Using weather data in energy time series forecasting: the benefit of input data transformations |
| title_sort | using weather data in energy time series forecasting the benefit of input data transformations |
| topic | Energy time series Forecasting Weather data |
| url | https://doi.org/10.1186/s42162-023-00299-8 |
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