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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Published in:	Energy science & engineering Vol. 13; no. 1; pp. 119 - 139
Main Authors:	Liu, Yonghong, Saleem, Muhammad S., Rashid, Javed, Ahmad, Sajjad, Faheem, Muhammad
Format:	Journal Article
Language:	English
Published:	London John Wiley & Sons, Inc 01.01.2025 Wiley
Subjects:	Accuracy Alternative energy sources Artificial neural networks Bi‐GRU Climate change Coal Datasets Deep learning Demand side management Electricity generation Emissions Energy consumption Energy industry Energy management Energy policy Energy resources Energy sources European countries Forecasting Fossil fuels Hydroelectric power internet of energy things Literature reviews Machine learning Neural networks nonrenewable energy Nuclear energy renewable energy Renewable resources Root-mean-square errors smart grid Weather forecasting Wind power United Kingdom > UK Switzerland Germany
ISSN:	2050-0505, 2050-0505
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Abstract	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. This study employs a bidirectional gated recurrent unit model to enhance the accuracy of energy output predictions for renewable and nonrenewable sources across the United Kingdom, Finland, Germany, and Switzerland. The findings reveal critical insights into energy production trends up to 2030, guiding strategic planning for energy management in Europe's transition toward sustainability.
AbstractList	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. 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. This study employs a bidirectional gated recurrent unit model to enhance the accuracy of energy output predictions for renewable and nonrenewable sources across the United Kingdom, Finland, Germany, and Switzerland. The findings reveal critical insights into energy production trends up to 2030, guiding strategic planning for energy management in Europe's transition toward sustainability. 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.
Author	Liu, Yonghong Faheem, Muhammad Ahmad, Sajjad Rashid, Javed Saleem, Muhammad S.
Author_xml	– sequence: 1 givenname: Yonghong surname: Liu fullname: Liu, Yonghong organization: Chengdu University – sequence: 2 givenname: Muhammad S. surname: Saleem fullname: Saleem, Muhammad S. organization: University of Okara – sequence: 3 givenname: Javed orcidid: 0000-0003-3416-9720 surname: Rashid fullname: Rashid, Javed organization: University of Okara – sequence: 4 givenname: Sajjad surname: Ahmad fullname: Ahmad, Sajjad organization: University of Okara – sequence: 5 givenname: Muhammad surname: Faheem fullname: Faheem, Muhammad email: muhammad.faheem@uwasa.fi organization: VTT Technical Research Centre of Finland Ltd
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Snippet	ABSTRACT Accurate forecasts of renewable and nonrenewable energy output are essential for meeting global energy needs and resolving environmental issues.... Accurate forecasts of renewable and nonrenewable energy output are essential for meeting global energy needs and resolving environmental issues. Energy sources... ABSTRACT Accurate forecasts of renewable and nonrenewable energy output are essential for meeting global energy needs and resolving environmental issues....
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SubjectTerms	Accuracy Alternative energy sources Artificial neural networks Bi‐GRU Climate change Coal Datasets Deep learning Demand side management Electricity generation Emissions Energy consumption Energy industry Energy management Energy policy Energy resources Energy sources European countries Forecasting Fossil fuels Hydroelectric power internet of energy things Literature reviews Machine learning Neural networks nonrenewable energy Nuclear energy renewable energy Renewable resources Root-mean-square errors smart grid Weather forecasting Wind power
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Title	Forecasting Shifts in Europe's Renewable and Fossil Fuel Markets Using Deep Learning Methods
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