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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References	2023; 11 2023; 34 2024; 304 2023; 105 2023; 148 2024; 53 2024; 54 2024; 10 2024; 11 2024; 12 2024 2024; 13 2024; 16 2024; 17 2024; 18 2021; 14 2023; 64 2023; 47 2024; 6 2024; 5 2024; 91 2022; 8 2020; 91 2023; 215 2022; 15 2018; 30 2022; 10 2024; 235 2021; 41 e_1_2_12_4_1 e_1_2_12_6_1 e_1_2_12_5_1 e_1_2_12_19_1 e_1_2_12_18_1 e_1_2_12_2_1 e_1_2_12_17_1 e_1_2_12_16_1 e_1_2_12_38_1 e_1_2_12_39_1 e_1_2_12_20_1 e_1_2_12_21_1 e_1_2_12_22_1 e_1_2_12_23_1 e_1_2_12_24_1 e_1_2_12_25_1 e_1_2_12_26_1 e_1_2_12_40_1 e_1_2_12_27_1 e_1_2_12_28_1 e_1_2_12_29_1 Freedman B. (e_1_2_12_3_1) 2024; 11 e_1_2_12_30_1 e_1_2_12_31_1 e_1_2_12_32_1 e_1_2_12_33_1 e_1_2_12_34_1 e_1_2_12_35_1 e_1_2_12_36_1 e_1_2_12_37_1 e_1_2_12_15_1 e_1_2_12_14_1 e_1_2_12_13_1 e_1_2_12_12_1 e_1_2_12_8_1 e_1_2_12_11_1 e_1_2_12_7_1 e_1_2_12_10_1 e_1_2_12_9_1
References_xml	– volume: 304 year: 2024 article-title: Prediction of Soluble Solids Content Using Near‐Infrared Spectra and Optical Properties of Intact Apple and Pulp Applying Plsr and CNN publication-title: Spectrochimica Acta, Part A: Molecular and Biomolecular Spectroscopy – volume: 105 start-page: 1623 year: 2023 end-page: 1645 article-title: Energy Generation Forecasting: Elevating Performance With Machine and Deep Learning publication-title: Computing – volume: 105 start-page: 1623 issue: 8 year: 2023 end-page: 1645 article-title: Energy Generation Forecasting: Elevating Performance With Machine and Deep Learning publication-title: Computing – volume: 18 start-page: 625 year: 2024 end-page: 638 article-title: ‘A Lightweight Smartcontracts Framework for Blockchain‐Based Securecommunication in Smart Grid Applications’ publication-title: IET Generation, Transmission and Distribution – volume: 235 year: 2024 article-title: A New Framework for Electricity Price Forecasting via Multi‐Head Self‐Attention and CNN‐Based Techniques in the Competitive Electricity Market publication-title: Expert Systems With Applications – volume: 41 start-page: 311 issue: 4 year: 2021 end-page: 323 article-title: Forecasting of Solar and Wind Power Using LSTM RNN for Load Frequency Control in Isolated Microgrid publication-title: International Journal of Modelling and Simulation – volume: 54 issue: 3 year: 2024 article-title: Multilayer Cyberattacks Identification and Classification Using Machine Learning in Internet of Blockchain (IoBC)‐Based Energy Networks publication-title: Data in Brief – volume: 12 start-page: 17915 year: 2024 end-page: 17925 article-title: Neural Networks Forecast Models Comparison for the Solar Energy Generation in Amazon Basin publication-title: IEEE Access – volume: 5 year: 2024 article-title: Intelligent Modeling and Optimization of Solar Plant Production Integration in the Smart Grid Using Machine Learning Models publication-title: Advanced Energy and Sustainability Research – year: 2024 – volume: 13 start-page: 608 issue: 4 year: 2024 end-page: 617 article-title: Evaluating the Performance of the Anwaralardh Photovoltaic Power Generation Plant in Jordan: Comparative Analysis Using Artificial Neural Networks and Multiple Linear Regression Modeling publication-title: International Journal of Renewable Energy Development – volume: 11 issue: 5 year: 2024 article-title: Renewable and Non‐Renewable Energy Sources publication-title: Environmental Science – volume: 14 start-page: 8240 issue: 24 year: 2021 article-title: Renewable Energy in the Sustainable Development of Electrical Power Sector: A Review publication-title: Energies – volume: 53 issue: 4 year: 2024 article-title: Cyberattack Patterns in Blockchain‐Based Communication Networks for Distributed Renewable Energy Systems: A Study on Big Datasets publication-title: Data in Brief – volume: 16 start-page: 2894 issue: 7 year: 2024 article-title: A Comparative Study of AI Methods on Renewable Energy Prediction for Smart Grids: Case of Turkey publication-title: Sustainability – volume: 47 start-page: 1267 issue: 1 year: 2023 end-page: 1281 article-title: Predictive Multimodal Deep Learning‐Based Sustainable Renewable and Non‐Renewable Energy Utilization publication-title: Computer Systems Science and Engineering – volume: 10 start-page: 2666 issue: 15 year: 2022 article-title: Hybrid Deep Learning Applied on Saudi Smart Grids for Short‐Term Load Forecasting publication-title: Mathematics – volume: 17 start-page: 352 issue: 2 year: 2024 article-title: Predicting Steam Turbine Power Generation: A Comparison of Long Short‐Term Memory and Willans Line Model publication-title: Energies – volume: 8 start-page: 8574 year: 2022 end-page: 8588 article-title: Ultra‐Short‐Term Wind Power Prediction Based on PVMD‐ESMA‐DELM publication-title: Energy Reports – volume: 91 year: 2020 article-title: ‘Autonomic Performance Prediction Framework for Data Warehouse Queries Using Lazy Learning Approach’ publication-title: Applied Soft Computing – volume: 30 start-page: 1 year: 2018 end-page: 30 article-title: ‘Smart Grid Communication and Information Technologies in the Perspective of Industry 4.0: Opportunities and Challenges’ publication-title: Computer Science Review – volume: 6 start-page: 1 year: 2024 end-page: 15 article-title: A Blockchain‐Based Resilient Andsecure Framework for Events Monitoring and Control Indistributed Renewable Energy Systems publication-title: IET Blockchain – volume: 215 year: 2023 article-title: A Customized Residual Neural Network and Bi‐Directional Gated Recurrent Unit‐Based Automatic Speech Recognition Model publication-title: Expert Systems With Applications – volume: 11 start-page: 62865 issue: 5 year: 2023 end-page: 62878 article-title: ‘A MapReduce Based Approach for Secure Batch Satellite Image Encryption’ publication-title: IEEE Access – volume: 12 start-page: 19553 year: 2024 end-page: 19568 article-title: Multi‐Task Learning of the Patchtcn‐TST Model for Short‐Term Multi‐Load Energy Forecasting Considering Indoor Environments in a Smart Building publication-title: IEEE Access – volume: 10 start-page: 2249 issue: 2 year: 2024 end-page: 2269 article-title: Cloud‐Load Forecasting via Decomposition‐Aided Attention Recurrent Neural Network Tuned by Modified Particle Swarm Optimization publication-title: Complex & Intelligent Systems – volume: 17 start-page: 1053 issue: 5 year: 2024 article-title: Deep Learning for Wind and Solar Energy Forecasting in Hydrogen Production publication-title: Energies – volume: 148 start-page: 366 year: 2023 end-page: 375 article-title: Evaluating Various Machine Learning Algorithms for Automated Inspection of Culverts publication-title: Engineering Analysis With Boundary Elements – volume: 34 year: 2023 article-title: A Large Comparison of Normalization Methods on Time Series publication-title: Big Data Research – volume: 91 start-page: 222 year: 2024 end-page: 236 article-title: Efficient Solar Power Generation Forecasting for Greenhouses: A Hybrid Deep Learning Approach publication-title: Alexandria Engineering Journal – volume: 11 start-page: 676 issue: 3 year: 2023 article-title: Solar Energy Production Forecasting Based on a Hybrid CNN‐LSTM‐Transformer Model publication-title: Mathematics – volume: 64 start-page: 127 issue: 1 year: 2023 end-page: 144 article-title: Hybrid Power Generation Forecasting Using CNN Based BILSTM Method for Renewable Energy Systems publication-title: Automatika – volume: 15 start-page: 2243 issue: 6 year: 2022 article-title: An Hour‐Ahead PV Power Forecasting Method Based on an RNN‐LSTM Model for Three Different PV Plants publication-title: Energies – ident: e_1_2_12_40_1 doi: 10.1016/j.cosrev.2018.08.001 – ident: e_1_2_12_9_1 doi: 10.1016/j.eswa.2022.119293 – ident: e_1_2_12_21_1 doi: 10.3390/en17051053 – ident: e_1_2_12_11_1 doi: 10.1016/j.saa.2023.123402 – ident: e_1_2_12_39_1 doi: 10.1109/ACCESS.2023.3279719 – ident: e_1_2_12_2_1 doi: 10.3390/en14248240 – ident: e_1_2_12_15_1 doi: 10.32604/csse.2023.037735 – ident: e_1_2_12_28_1 doi: 10.3390/en15062243 – ident: e_1_2_12_22_1 doi: 10.1080/02286203.2020.1767840 – ident: e_1_2_12_26_1 doi: 10.1109/ACCESS.2024.3358339 – ident: e_1_2_12_16_1 doi: 10.1007/s40747-023-01265-3 – ident: e_1_2_12_35_1 – ident: e_1_2_12_32_1 doi: 10.1016/j.enganabound.2023.01.007 – ident: e_1_2_12_19_1 doi: 10.1080/00051144.2022.2118101 – ident: e_1_2_12_5_1 – volume: 11 issue: 5 year: 2024 ident: e_1_2_12_3_1 article-title: Renewable and Non‐Renewable Energy Sources publication-title: Environmental Science – ident: e_1_2_12_23_1 doi: 10.1016/j.egyr.2022.06.079 – ident: e_1_2_12_34_1 doi: 10.61435/ijred.2024.60156 – ident: e_1_2_12_37_1 doi: 10.1049/gtd2.13103 – ident: e_1_2_12_38_1 doi: 10.1016/j.asoc.2020.106216 – ident: e_1_2_12_6_1 – ident: e_1_2_12_12_1 doi: 10.1016/j.aej.2024.02.004 – ident: e_1_2_12_25_1 doi: 10.1051/e3sconf/202450101023 – ident: e_1_2_12_17_1 doi: 10.3390/math11030676 – ident: e_1_2_12_8_1 doi: 10.1016/j.dib.2024.110212 – ident: e_1_2_12_33_1 doi: 10.1007/s00607-023-01164-y – ident: e_1_2_12_20_1 doi: 10.1007/s00607-023-01164-y – ident: e_1_2_12_27_1 doi: 10.1002/aesr.202300160 – ident: e_1_2_12_4_1 – ident: e_1_2_12_29_1 – ident: e_1_2_12_10_1 doi: 10.1016/j.dib.2024.110461 – ident: e_1_2_12_14_1 doi: 10.1109/ACCESS.2024.3355448 – ident: e_1_2_12_24_1 doi: 10.3390/en17020352 – ident: e_1_2_12_36_1 doi: 10.1016/j.eswa.2023.121207 – ident: e_1_2_12_7_1 doi: 10.1049/blc2.12081 – ident: e_1_2_12_13_1 doi: 10.3390/su16072894 – ident: e_1_2_12_30_1 – ident: e_1_2_12_31_1 doi: 10.1016/j.bdr.2023.100407 – ident: e_1_2_12_18_1 doi: 10.3390/math10152666
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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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