Deep learning-based forecasting of electricity consumption
Abstract Building energy management systems (BEMS) are integrated computerized systems that track and manage the energy use of many pieces of building-related machinery and equipment, including lighting, power systems, and HVAC systems. Modern buildings must have BEMSs in order to reduce energy usag...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-03-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-024-56602-4 |
| _version_ | 1797247377318346752 |
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| author | Momina Qureshi Masood Ahmad Arbab Sadaqat ur Rehman |
| author_facet | Momina Qureshi Masood Ahmad Arbab Sadaqat ur Rehman |
| author_sort | Momina Qureshi |
| collection | DOAJ |
| description | Abstract Building energy management systems (BEMS) are integrated computerized systems that track and manage the energy use of many pieces of building-related machinery and equipment, including lighting, power systems, and HVAC systems. Modern buildings must have BEMSs in order to reduce energy usage while maintaining comfort. Not only for energy-saving purposes, BEMS is essential in enhancing the quality of the energy supply, which helps to gain a better understanding of how energy is used and the building's energy usage. When the dynamics of a building's energy usage are known, it is possible to determine which changes are most likely to reduce consumption. Numerous connected devices, operating modes, energy usage, and environmental factors can all be monitored and controlled in real-time using BEMS. Changing operating times and setting points to maximize comfort and efficiency is made simple by this. In this paper, we have primarily addressed the two significant issues of model optimization and electricity consumption forecasts. Future forecasting has been done using the LSTM based time series approach. We generated data on the amount of electricity consumed by a hospital facility and tested our suggested methodologies on actual data. The findings gained demonstrated that the strategies were successful with both types of data. On actual data, the trend in electricity consumption can be accurately predicted. Several model optimizers enhanced the suggested methods' performance as well. Our objective function gain accuracy result of 95%. |
| first_indexed | 2024-04-24T19:57:43Z |
| format | Article |
| id | doaj.art-04624474552641e3901c9351970deedf |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-24T19:57:43Z |
| publishDate | 2024-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-04624474552641e3901c9351970deedf2024-03-24T12:15:08ZengNature PortfolioScientific Reports2045-23222024-03-0114111110.1038/s41598-024-56602-4Deep learning-based forecasting of electricity consumptionMomina Qureshi0Masood Ahmad Arbab1Sadaqat ur Rehman2Department of Computer Systems Engineering, University of Engineering and Technology PeshawarDepartment of Computer Systems Engineering, University of Engineering and Technology PeshawarSchool of Sciences Engineering and Environment University of SalfordAbstract Building energy management systems (BEMS) are integrated computerized systems that track and manage the energy use of many pieces of building-related machinery and equipment, including lighting, power systems, and HVAC systems. Modern buildings must have BEMSs in order to reduce energy usage while maintaining comfort. Not only for energy-saving purposes, BEMS is essential in enhancing the quality of the energy supply, which helps to gain a better understanding of how energy is used and the building's energy usage. When the dynamics of a building's energy usage are known, it is possible to determine which changes are most likely to reduce consumption. Numerous connected devices, operating modes, energy usage, and environmental factors can all be monitored and controlled in real-time using BEMS. Changing operating times and setting points to maximize comfort and efficiency is made simple by this. In this paper, we have primarily addressed the two significant issues of model optimization and electricity consumption forecasts. Future forecasting has been done using the LSTM based time series approach. We generated data on the amount of electricity consumed by a hospital facility and tested our suggested methodologies on actual data. The findings gained demonstrated that the strategies were successful with both types of data. On actual data, the trend in electricity consumption can be accurately predicted. Several model optimizers enhanced the suggested methods' performance as well. Our objective function gain accuracy result of 95%.https://doi.org/10.1038/s41598-024-56602-4BEMSLSTMElectricity demand forecastingAnomaly detectionEnergy consumptionFuture forecasting |
| spellingShingle | Momina Qureshi Masood Ahmad Arbab Sadaqat ur Rehman Deep learning-based forecasting of electricity consumption Scientific Reports BEMS LSTM Electricity demand forecasting Anomaly detection Energy consumption Future forecasting |
| title | Deep learning-based forecasting of electricity consumption |
| title_full | Deep learning-based forecasting of electricity consumption |
| title_fullStr | Deep learning-based forecasting of electricity consumption |
| title_full_unstemmed | Deep learning-based forecasting of electricity consumption |
| title_short | Deep learning-based forecasting of electricity consumption |
| title_sort | deep learning based forecasting of electricity consumption |
| topic | BEMS LSTM Electricity demand forecasting Anomaly detection Energy consumption Future forecasting |
| url | https://doi.org/10.1038/s41598-024-56602-4 |
| work_keys_str_mv | AT mominaqureshi deeplearningbasedforecastingofelectricityconsumption AT masoodahmadarbab deeplearningbasedforecastingofelectricityconsumption AT sadaqaturrehman deeplearningbasedforecastingofelectricityconsumption |