Multi-Timescale Energy Consumption Management in Smart Buildings Using Hybrid Deep Artificial Neural Networks
Demand side management is a critical issue in the energy sector. Recent events such as the global energy crisis, costs, the necessity to reduce greenhouse emissions, and extreme weather conditions have increased the need for energy efficiency. Thus, accurately predicting energy consumption is one of...
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MDPI AG
2024-11-01
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| Series: | Journal of Low Power Electronics and Applications |
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| Online Access: | https://www.mdpi.com/2079-9268/14/4/54 |
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| author | Favour Ibude Abayomi Otebolaku Jude E. Ameh Augustine Ikpehai |
| author_facet | Favour Ibude Abayomi Otebolaku Jude E. Ameh Augustine Ikpehai |
| author_sort | Favour Ibude |
| collection | DOAJ |
| description | Demand side management is a critical issue in the energy sector. Recent events such as the global energy crisis, costs, the necessity to reduce greenhouse emissions, and extreme weather conditions have increased the need for energy efficiency. Thus, accurately predicting energy consumption is one of the key steps in addressing inefficiency in energy consumption and its optimization. In this regard, accurate predictions on a daily, hourly, and minute-by-minute basis would not only minimize wastage but would also help to save costs. In this article, we propose intelligent models using ensembles of convolutional neural network (CNN), long-short-term memory (LSTM), bi-directional LSTM and gated recurrent units (GRUs) neural network models for daily, hourly, and minute-by-minute predictions of energy consumptions in smart buildings. The proposed models outperform state-of-the-art deep neural network models for predicting minute-by-minute energy consumption, with a mean square error of 0.109. The evaluated hybrid models also capture more latent trends in the data than traditional single models. The results highlight the potential of using hybrid deep learning models for improved energy efficiency management in smart buildings. |
| format | Article |
| id | doaj-art-b83f032a2dc74f2daf26b5f7425a084a |
| institution | OA Journals |
| issn | 2079-9268 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Low Power Electronics and Applications |
| spelling | doaj-art-b83f032a2dc74f2daf26b5f7425a084a2025-08-20T02:00:42ZengMDPI AGJournal of Low Power Electronics and Applications2079-92682024-11-011445410.3390/jlpea14040054Multi-Timescale Energy Consumption Management in Smart Buildings Using Hybrid Deep Artificial Neural NetworksFavour Ibude0Abayomi Otebolaku1Jude E. Ameh2Augustine Ikpehai3School of Computing and Digital Technologies, Sheffield Hallam University, Sheffield S1 2NU, UKSchool of Computing and Digital Technologies, Sheffield Hallam University, Sheffield S1 2NU, UKSchool of Computing and Digital Technologies, Sheffield Hallam University, Sheffield S1 2NU, UKSchool of Engineering and Built Environment, Sheffield Hallam University, Sheffield S1 2LX, UKDemand side management is a critical issue in the energy sector. Recent events such as the global energy crisis, costs, the necessity to reduce greenhouse emissions, and extreme weather conditions have increased the need for energy efficiency. Thus, accurately predicting energy consumption is one of the key steps in addressing inefficiency in energy consumption and its optimization. In this regard, accurate predictions on a daily, hourly, and minute-by-minute basis would not only minimize wastage but would also help to save costs. In this article, we propose intelligent models using ensembles of convolutional neural network (CNN), long-short-term memory (LSTM), bi-directional LSTM and gated recurrent units (GRUs) neural network models for daily, hourly, and minute-by-minute predictions of energy consumptions in smart buildings. The proposed models outperform state-of-the-art deep neural network models for predicting minute-by-minute energy consumption, with a mean square error of 0.109. The evaluated hybrid models also capture more latent trends in the data than traditional single models. The results highlight the potential of using hybrid deep learning models for improved energy efficiency management in smart buildings.https://www.mdpi.com/2079-9268/14/4/54smart buildingsenergy consumptionhybrid deep learningenergy forecastingbuilding energy management systems |
| spellingShingle | Favour Ibude Abayomi Otebolaku Jude E. Ameh Augustine Ikpehai Multi-Timescale Energy Consumption Management in Smart Buildings Using Hybrid Deep Artificial Neural Networks Journal of Low Power Electronics and Applications smart buildings energy consumption hybrid deep learning energy forecasting building energy management systems |
| title | Multi-Timescale Energy Consumption Management in Smart Buildings Using Hybrid Deep Artificial Neural Networks |
| title_full | Multi-Timescale Energy Consumption Management in Smart Buildings Using Hybrid Deep Artificial Neural Networks |
| title_fullStr | Multi-Timescale Energy Consumption Management in Smart Buildings Using Hybrid Deep Artificial Neural Networks |
| title_full_unstemmed | Multi-Timescale Energy Consumption Management in Smart Buildings Using Hybrid Deep Artificial Neural Networks |
| title_short | Multi-Timescale Energy Consumption Management in Smart Buildings Using Hybrid Deep Artificial Neural Networks |
| title_sort | multi timescale energy consumption management in smart buildings using hybrid deep artificial neural networks |
| topic | smart buildings energy consumption hybrid deep learning energy forecasting building energy management systems |
| url | https://www.mdpi.com/2079-9268/14/4/54 |
| work_keys_str_mv | AT favouribude multitimescaleenergyconsumptionmanagementinsmartbuildingsusinghybriddeepartificialneuralnetworks AT abayomiotebolaku multitimescaleenergyconsumptionmanagementinsmartbuildingsusinghybriddeepartificialneuralnetworks AT judeeameh multitimescaleenergyconsumptionmanagementinsmartbuildingsusinghybriddeepartificialneuralnetworks AT augustineikpehai multitimescaleenergyconsumptionmanagementinsmartbuildingsusinghybriddeepartificialneuralnetworks |