Energy demand forecasting using convolutional neural network and modified war strategy optimization algorithm
Predicting the electricity demand is a key responsibility for the energy industry and governments in order to provide an effective and dependable energy supply. Traditional projection techniques frequently rely on mathematical models, which are limited in their ability to recognize complex patterns...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-03-01
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| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S240584402403384X |
| _version_ | 1827294041422692352 |
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| author | Huanhuan Hu Shufen Gong Bahman Taheri |
| author_facet | Huanhuan Hu Shufen Gong Bahman Taheri |
| author_sort | Huanhuan Hu |
| collection | DOAJ |
| description | Predicting the electricity demand is a key responsibility for the energy industry and governments in order to provide an effective and dependable energy supply. Traditional projection techniques frequently rely on mathematical models, which are limited in their ability to recognize complex patterns and correlations in data. Machine learning has emerged as a viable tool for estimating electricity in the last decade. In this study, the Modified War Strategy Optimization-Based Convolutional Neural Network (MWSO-CNN) has been provided for electricity demand prediction. To increase the precision of electricity demand prediction, the MWSO-CNN approach integrates the benefits of the modified war strategy optimization technique and the convolutional neural network. To improve efficiency, the modified war strategy optimization technique is employed to adjust the hyperparameters of the CNN algorithm. The suggested MWSO-CNN approach is tested on a real-world electricity demand dataset, and the findings show that it outperforms many state-of-the-art machine learning techniques for predicting electricity demand. In general, the suggested MWSO-CNN approach could offer a successful and cost-effective strategy for predicting energy consumption, which will benefit both the energy business and society as a whole. |
| first_indexed | 2024-04-24T13:50:20Z |
| format | Article |
| id | doaj.art-57c147e4130c4ff694e89a811dd2bd07 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-04-24T13:50:20Z |
| publishDate | 2024-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-57c147e4130c4ff694e89a811dd2bd072024-04-04T05:04:41ZengElsevierHeliyon2405-84402024-03-01106e27353Energy demand forecasting using convolutional neural network and modified war strategy optimization algorithmHuanhuan Hu0Shufen Gong1Bahman Taheri2College of Big Data and Artificial Intelligence, Chizhou University, Chizhou, 247100, Anhui, ChinaCollege of Big Data and Artificial Intelligence, Chizhou University, Chizhou, 247100, Anhui, China; Corresponding author.Science and Research Branch, Islamic Azad University, Tehran, Iran; College of Technical Engineering, The Islamic University, Najaf, Iraq; Corresponding author.Science and Research Branch, Islamic Azad University, Tehran, Iran.Predicting the electricity demand is a key responsibility for the energy industry and governments in order to provide an effective and dependable energy supply. Traditional projection techniques frequently rely on mathematical models, which are limited in their ability to recognize complex patterns and correlations in data. Machine learning has emerged as a viable tool for estimating electricity in the last decade. In this study, the Modified War Strategy Optimization-Based Convolutional Neural Network (MWSO-CNN) has been provided for electricity demand prediction. To increase the precision of electricity demand prediction, the MWSO-CNN approach integrates the benefits of the modified war strategy optimization technique and the convolutional neural network. To improve efficiency, the modified war strategy optimization technique is employed to adjust the hyperparameters of the CNN algorithm. The suggested MWSO-CNN approach is tested on a real-world electricity demand dataset, and the findings show that it outperforms many state-of-the-art machine learning techniques for predicting electricity demand. In general, the suggested MWSO-CNN approach could offer a successful and cost-effective strategy for predicting energy consumption, which will benefit both the energy business and society as a whole.http://www.sciencedirect.com/science/article/pii/S240584402403384XElectricity demand predictionModified war strategy optimizationConvolutional neural networkHyperparametersEnergy consumptionCost-effective strategy |
| spellingShingle | Huanhuan Hu Shufen Gong Bahman Taheri Energy demand forecasting using convolutional neural network and modified war strategy optimization algorithm Heliyon Electricity demand prediction Modified war strategy optimization Convolutional neural network Hyperparameters Energy consumption Cost-effective strategy |
| title | Energy demand forecasting using convolutional neural network and modified war strategy optimization algorithm |
| title_full | Energy demand forecasting using convolutional neural network and modified war strategy optimization algorithm |
| title_fullStr | Energy demand forecasting using convolutional neural network and modified war strategy optimization algorithm |
| title_full_unstemmed | Energy demand forecasting using convolutional neural network and modified war strategy optimization algorithm |
| title_short | Energy demand forecasting using convolutional neural network and modified war strategy optimization algorithm |
| title_sort | energy demand forecasting using convolutional neural network and modified war strategy optimization algorithm |
| topic | Electricity demand prediction Modified war strategy optimization Convolutional neural network Hyperparameters Energy consumption Cost-effective strategy |
| url | http://www.sciencedirect.com/science/article/pii/S240584402403384X |
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