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
Online Access:Get full text
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Summary: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.
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ISSN:2050-0505
2050-0505
DOI:10.1002/ese3.1981