Efficient deterministic renewable energy forecasting guided by multiple-location weather data

Electricity generated from renewable energy sources has been established as an efficient remedy for both energy shortages and the environmental pollution stemming from conventional energy production methods. At current stage, hydropower remains the primary contributor to electricity generation among...

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Vydáno v:	Neural computing & applications Ročník 37; číslo 17; s. 10647 - 10674
Hlavní autoři:	Symeonidis, Charalampos, Nikolaidis, Nikos
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London Springer London 01.06.2025 Springer Nature B.V
Témata:	Alternative energy sources Artificial Intelligence Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Datasets Electric power grids Electricity Energy resources Forecasting Image Processing and Computer Vision Meteorological data Power plants Probability and Statistics in Computer Science Production methods Renewable energy sources Renewable resources Root-mean-square errors S.I.: Timely Advances of Deep Learning with applications and Data Driven Modeling Solar energy Special Issue on Timely Advances of Deep Learning with applications and Data Driven Modeling Time series Weather forecasting Wind power Deep learning Deterministic RES forecasting Time-series forecasting
ISSN:	0941-0643, 1433-3058
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Abstract	Electricity generated from renewable energy sources has been established as an efficient remedy for both energy shortages and the environmental pollution stemming from conventional energy production methods. At current stage, hydropower remains the primary contributor to electricity generation among renewable energy sources. Nonetheless, solar and wind power are also recognized as dominant and exceptionally promising renewable energy sources. The accurate forecasting of the energy generation of those sources facilitates their integration into electric grids, by minimizing the negative impact of uncertainty regarding their management and operation. This paper proposes a novel methodology for deterministic wind and solar energy generation forecasting for multiple generation sites, utilizing multi-location weather forecasts. The method employs a U-shaped temporal convolutional auto-encoder (UTCAE) architecture for temporal processing of weather-related and energy-related time-series across each site. The multi-sized kernels convolutional spatiotemporal attention (MKST-Attention), inspired by the multi-head scaled dot product attention mechanism, is also proposed aiming to efficiently transfer temporal patterns from weather data to energy data, without a priori knowledge of the locations of the power stations and the locations of provided weather data. The conducted experimental evaluation on a day-ahead solar and wind energy forecasting scenario on five datasets demonstrated that the proposed method achieves top results, outperforming all competitive time-series forecasting state-of-the-art methods. In particular, in the AEMO-H dataset, encompassing hourly wind energy generation data alongside weather data from 22 power stations, the method attained the best mean absolute error (MAE), root mean square error (RMSE) and coefficient of determination ( R 2 ) scores at each station. Additionally, it recorded the highest MAE of 0.098, the highest RMSE of 0.138 and the highest R 2 score of 0.791, averaged across all energy stations.
AbstractList	Electricity generated from renewable energy sources has been established as an efficient remedy for both energy shortages and the environmental pollution stemming from conventional energy production methods. At current stage, hydropower remains the primary contributor to electricity generation among renewable energy sources. Nonetheless, solar and wind power are also recognized as dominant and exceptionally promising renewable energy sources. The accurate forecasting of the energy generation of those sources facilitates their integration into electric grids, by minimizing the negative impact of uncertainty regarding their management and operation. This paper proposes a novel methodology for deterministic wind and solar energy generation forecasting for multiple generation sites, utilizing multi-location weather forecasts. The method employs a U-shaped temporal convolutional auto-encoder (UTCAE) architecture for temporal processing of weather-related and energy-related time-series across each site. The multi-sized kernels convolutional spatiotemporal attention (MKST-Attention), inspired by the multi-head scaled dot product attention mechanism, is also proposed aiming to efficiently transfer temporal patterns from weather data to energy data, without a priori knowledge of the locations of the power stations and the locations of provided weather data. The conducted experimental evaluation on a day-ahead solar and wind energy forecasting scenario on five datasets demonstrated that the proposed method achieves top results, outperforming all competitive time-series forecasting state-of-the-art methods. In particular, in the AEMO-H dataset, encompassing hourly wind energy generation data alongside weather data from 22 power stations, the method attained the best mean absolute error (MAE), root mean square error (RMSE) and coefficient of determination (R2) scores at each station. Additionally, it recorded the highest MAE of 0.098, the highest RMSE of 0.138 and the highest R2 score of 0.791, averaged across all energy stations. Electricity generated from renewable energy sources has been established as an efficient remedy for both energy shortages and the environmental pollution stemming from conventional energy production methods. At current stage, hydropower remains the primary contributor to electricity generation among renewable energy sources. Nonetheless, solar and wind power are also recognized as dominant and exceptionally promising renewable energy sources. The accurate forecasting of the energy generation of those sources facilitates their integration into electric grids, by minimizing the negative impact of uncertainty regarding their management and operation. This paper proposes a novel methodology for deterministic wind and solar energy generation forecasting for multiple generation sites, utilizing multi-location weather forecasts. The method employs a U-shaped temporal convolutional auto-encoder (UTCAE) architecture for temporal processing of weather-related and energy-related time-series across each site. The multi-sized kernels convolutional spatiotemporal attention (MKST-Attention), inspired by the multi-head scaled dot product attention mechanism, is also proposed aiming to efficiently transfer temporal patterns from weather data to energy data, without a priori knowledge of the locations of the power stations and the locations of provided weather data. The conducted experimental evaluation on a day-ahead solar and wind energy forecasting scenario on five datasets demonstrated that the proposed method achieves top results, outperforming all competitive time-series forecasting state-of-the-art methods. In particular, in the AEMO-H dataset, encompassing hourly wind energy generation data alongside weather data from 22 power stations, the method attained the best mean absolute error (MAE), root mean square error (RMSE) and coefficient of determination ( R 2 ) scores at each station. Additionally, it recorded the highest MAE of 0.098, the highest RMSE of 0.138 and the highest R 2 score of 0.791, averaged across all energy stations.
Author	Nikolaidis, Nikos Symeonidis, Charalampos
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SubjectTerms	Alternative energy sources Artificial Intelligence Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Datasets Electric power grids Electricity Energy resources Forecasting Image Processing and Computer Vision Meteorological data Power plants Probability and Statistics in Computer Science Production methods Renewable energy sources Renewable resources Root-mean-square errors S.I.: Timely Advances of Deep Learning with applications and Data Driven Modeling Solar energy Special Issue on Timely Advances of Deep Learning with applications and Data Driven Modeling Time series Weather forecasting Wind power
Title	Efficient deterministic renewable energy forecasting guided by multiple-location weather data
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