Forecasting Shifts in Europe's Renewable and Fossil Fuel Markets Using Deep Learning Methods
ABSTRACT Accurate forecasts of renewable and nonrenewable energy output are essential for meeting global energy needs and resolving environmental issues. Energy sources like the sun and wind are variable, making forecasting difficult. Changes in weather, demand, and energy policy exacerbate this unp...
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| Format: | Article |
| Language: | English |
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Wiley
2025-01-01
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| Series: | Energy Science & Engineering |
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| Online Access: | https://doi.org/10.1002/ese3.1981 |
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| author | Yonghong Liu Muhammad S. Saleem Javed Rashid Sajjad Ahmad Muhammad Faheem |
| author_facet | Yonghong Liu Muhammad S. Saleem Javed Rashid Sajjad Ahmad Muhammad Faheem |
| author_sort | Yonghong Liu |
| collection | DOAJ |
| description | ABSTRACT Accurate forecasts of renewable and nonrenewable energy output are essential for meeting global energy needs and resolving environmental issues. Energy sources like the sun and wind are variable, making forecasting difficult. Changes in weather, demand, and energy policy exacerbate this unpredictability. These challenges will be addressed by the bidirectional gated recurrent unit (Bi‐GRU) model, which forecasts power‐generating outcomes more efficiently. The investigation is done over a health data set from 2000 to 2023, including the energy states of the United Kingdom, Finland, Germany, and Switzerland. The comparison of our model (Bi‐GRU) performance with other popular models, including bidirectional long short‐term memory (Bi‐LSTM), ensemble techniques combining convolutional neural networks (CNN) and Bi‐LSTM, and CNNs, make the study more interesting. The performance remains better with a mean absolute percentage error (MAPE) of 2.75%, root mean square error (RMSE) of 0.0414, mean squared error (MSE) of 0.0017, and authentify that Bi‐GRU performs much better than others. This model's superior prediction accuracy significantly enhances our ability to forecast renewable and nonrenewable energy outputs in European states, contributing to more effective energy management strategies. |
| format | Article |
| id | doaj-art-aaeda103360f4c45a535f1dfc19bdd38 |
| institution | Kabale University |
| issn | 2050-0505 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Energy Science & Engineering |
| spelling | doaj-art-aaeda103360f4c45a535f1dfc19bdd382025-01-21T11:38:24ZengWileyEnergy Science & Engineering2050-05052025-01-0113111913910.1002/ese3.1981Forecasting Shifts in Europe's Renewable and Fossil Fuel Markets Using Deep Learning MethodsYonghong Liu0Muhammad S. Saleem1Javed Rashid2Sajjad Ahmad3Muhammad Faheem4School of Computer Science Chengdu University Chengdu ChinaDepartment of Mathematics University of Okara Okara PakistanMLC Research Lab Okara PakistanDepartment of Mathematics University of Okara Okara PakistanSchool of Technology and Innovations University of Vaasa Vaasa FinlandABSTRACT Accurate forecasts of renewable and nonrenewable energy output are essential for meeting global energy needs and resolving environmental issues. Energy sources like the sun and wind are variable, making forecasting difficult. Changes in weather, demand, and energy policy exacerbate this unpredictability. These challenges will be addressed by the bidirectional gated recurrent unit (Bi‐GRU) model, which forecasts power‐generating outcomes more efficiently. The investigation is done over a health data set from 2000 to 2023, including the energy states of the United Kingdom, Finland, Germany, and Switzerland. The comparison of our model (Bi‐GRU) performance with other popular models, including bidirectional long short‐term memory (Bi‐LSTM), ensemble techniques combining convolutional neural networks (CNN) and Bi‐LSTM, and CNNs, make the study more interesting. The performance remains better with a mean absolute percentage error (MAPE) of 2.75%, root mean square error (RMSE) of 0.0414, mean squared error (MSE) of 0.0017, and authentify that Bi‐GRU performs much better than others. This model's superior prediction accuracy significantly enhances our ability to forecast renewable and nonrenewable energy outputs in European states, contributing to more effective energy management strategies.https://doi.org/10.1002/ese3.1981Bi‐GRUEuropean countriesinternet of energy thingsnonrenewable energyrenewable energysmart grid |
| spellingShingle | Yonghong Liu Muhammad S. Saleem Javed Rashid Sajjad Ahmad Muhammad Faheem Forecasting Shifts in Europe's Renewable and Fossil Fuel Markets Using Deep Learning Methods Energy Science & Engineering Bi‐GRU European countries internet of energy things nonrenewable energy renewable energy smart grid |
| title | Forecasting Shifts in Europe's Renewable and Fossil Fuel Markets Using Deep Learning Methods |
| title_full | Forecasting Shifts in Europe's Renewable and Fossil Fuel Markets Using Deep Learning Methods |
| title_fullStr | Forecasting Shifts in Europe's Renewable and Fossil Fuel Markets Using Deep Learning Methods |
| title_full_unstemmed | Forecasting Shifts in Europe's Renewable and Fossil Fuel Markets Using Deep Learning Methods |
| title_short | Forecasting Shifts in Europe's Renewable and Fossil Fuel Markets Using Deep Learning Methods |
| title_sort | forecasting shifts in europe s renewable and fossil fuel markets using deep learning methods |
| topic | Bi‐GRU European countries internet of energy things nonrenewable energy renewable energy smart grid |
| url | https://doi.org/10.1002/ese3.1981 |
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