Optimizing load demand forecasting in educational buildings using quantum-inspired particle swarm optimization (QPSO) with recurrent neural networks (RNNs):a seasonal approach
Abstract This study uses Quantum Particle Swarm Optimization (QPSO) optimized Recurrent Neural Networks (RNN), standard RNN, and autoregressive integrated moving average (ARIMA) models to anticipate educational building power demand accurately. Energy efficiency, cost reduction, and resource allocat...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-06-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-04301-z |
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| author | Sunawar Khan Tehseen Mazhar Tariq Shahzad Tariq Ali Muhammad Ayaz Yazeed Yasin Ghadi EL-Hadi M. Aggoune Habib Hamam |
| author_facet | Sunawar Khan Tehseen Mazhar Tariq Shahzad Tariq Ali Muhammad Ayaz Yazeed Yasin Ghadi EL-Hadi M. Aggoune Habib Hamam |
| author_sort | Sunawar Khan |
| collection | DOAJ |
| description | Abstract This study uses Quantum Particle Swarm Optimization (QPSO) optimized Recurrent Neural Networks (RNN), standard RNN, and autoregressive integrated moving average (ARIMA) models to anticipate educational building power demand accurately. Energy efficiency, cost reduction, and resource allocation depend on accurate load forecasts. The study evaluates model performance using year-long load data from seasonal, daily, and hourly fluctuations. Performance indicators, including Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE), were used to assess the models. The QPSO-optimized RNN outperformed traditional RNN and ARIMA models with the lowest MAE of 15.2, MSE of 520.15, and RMSE of 22.8. Comparative investigation shows the QPSO-RNN’s capacity to capture complicated load data patterns, especially during peak demand. This study shows that hybrid optimization can improve forecasting accuracy, making it a powerful tool for energy management in dynamic contexts. |
| format | Article |
| id | doaj-art-cb8897c8fd9f4468a475bd48020273fc |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-cb8897c8fd9f4468a475bd48020273fc2025-08-20T03:26:44ZengNature PortfolioScientific Reports2045-23222025-06-0115111910.1038/s41598-025-04301-zOptimizing load demand forecasting in educational buildings using quantum-inspired particle swarm optimization (QPSO) with recurrent neural networks (RNNs):a seasonal approachSunawar Khan0Tehseen Mazhar1Tariq Shahzad2Tariq Ali3Muhammad Ayaz4Yazeed Yasin Ghadi5EL-Hadi M. Aggoune6Habib Hamam7School of Computer Science, National College of Business Administration and Economics School of Computer Science, National College of Business Administration and Economics Department of Computer Engineering, COMSATS University IslamabadArtificial Intelligence and Sensing Technologies (AIST) Research Centre, University of TabukArtificial Intelligence and Sensing Technologies (AIST) Research Centre, University of TabukDepartment of Computer Science and Software Engineering, Al Ain UniversityArtificial Intelligence and Sensing Technologies (AIST) Research Centre, University of TabukSchool of Electrical Engineering, University of JohannesburgAbstract This study uses Quantum Particle Swarm Optimization (QPSO) optimized Recurrent Neural Networks (RNN), standard RNN, and autoregressive integrated moving average (ARIMA) models to anticipate educational building power demand accurately. Energy efficiency, cost reduction, and resource allocation depend on accurate load forecasts. The study evaluates model performance using year-long load data from seasonal, daily, and hourly fluctuations. Performance indicators, including Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE), were used to assess the models. The QPSO-optimized RNN outperformed traditional RNN and ARIMA models with the lowest MAE of 15.2, MSE of 520.15, and RMSE of 22.8. Comparative investigation shows the QPSO-RNN’s capacity to capture complicated load data patterns, especially during peak demand. This study shows that hybrid optimization can improve forecasting accuracy, making it a powerful tool for energy management in dynamic contexts.https://doi.org/10.1038/s41598-025-04301-zLoad demand forecastingTime series forecastingSeasonal adjustmentEducational buildingsEnergy management |
| spellingShingle | Sunawar Khan Tehseen Mazhar Tariq Shahzad Tariq Ali Muhammad Ayaz Yazeed Yasin Ghadi EL-Hadi M. Aggoune Habib Hamam Optimizing load demand forecasting in educational buildings using quantum-inspired particle swarm optimization (QPSO) with recurrent neural networks (RNNs):a seasonal approach Scientific Reports Load demand forecasting Time series forecasting Seasonal adjustment Educational buildings Energy management |
| title | Optimizing load demand forecasting in educational buildings using quantum-inspired particle swarm optimization (QPSO) with recurrent neural networks (RNNs):a seasonal approach |
| title_full | Optimizing load demand forecasting in educational buildings using quantum-inspired particle swarm optimization (QPSO) with recurrent neural networks (RNNs):a seasonal approach |
| title_fullStr | Optimizing load demand forecasting in educational buildings using quantum-inspired particle swarm optimization (QPSO) with recurrent neural networks (RNNs):a seasonal approach |
| title_full_unstemmed | Optimizing load demand forecasting in educational buildings using quantum-inspired particle swarm optimization (QPSO) with recurrent neural networks (RNNs):a seasonal approach |
| title_short | Optimizing load demand forecasting in educational buildings using quantum-inspired particle swarm optimization (QPSO) with recurrent neural networks (RNNs):a seasonal approach |
| title_sort | optimizing load demand forecasting in educational buildings using quantum inspired particle swarm optimization qpso with recurrent neural networks rnns a seasonal approach |
| topic | Load demand forecasting Time series forecasting Seasonal adjustment Educational buildings Energy management |
| url | https://doi.org/10.1038/s41598-025-04301-z |
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