Forecasting of Energy Demands for Smart Home Applications
The utilization of energy is on the rise in current trends due to increasing consumptions by households. Smart buildings, on the other hand, aim to optimize energy, and hence, the aim of the study is to forecast the cost of energy consumption in smart buildings by effectively addressing the minimal...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-02-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/14/4/1045 |
| _version_ | 1797396279171481600 |
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| author | Dhowmya Bhatt Danalakshmi D A. Hariharasudan Marcin Lis Marlena Grabowska |
| author_facet | Dhowmya Bhatt Danalakshmi D A. Hariharasudan Marcin Lis Marlena Grabowska |
| author_sort | Dhowmya Bhatt |
| collection | DOAJ |
| description | The utilization of energy is on the rise in current trends due to increasing consumptions by households. Smart buildings, on the other hand, aim to optimize energy, and hence, the aim of the study is to forecast the cost of energy consumption in smart buildings by effectively addressing the minimal energy consumption. However, smart buildings are restricted, with limited power access and capacity associated with Heating, Ventilation and Air Conditioning (HVAC) units. It further suffers from low communication capability due to device limitations. In this paper, a balanced deep learning architecture is used to offer solutions to address these constraints. The deep learning algorithm considers three constraints, such as a multi-objective optimization problem and a fitness function, to resolve the price management problem and high-level energy consumption in HVAC systems. The study analyzes and optimizes the consumption of power in smart buildings by the HVAC systems in terms of power loss, price management and reactive power. Experiments are conducted over various scenarios to check the integrity of the system over various smart buildings and in high-rise buildings. The results are compared in terms of various HVAC devices on various metrics and communication protocols, where the proposed system is considered more effective than other methods. The results of the Li-Fi communication protocols show improved results compared to the other communication protocols. |
| first_indexed | 2024-03-09T00:49:04Z |
| format | Article |
| id | doaj.art-e02a30f4282249eab7d4812deff4cb19 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T00:49:04Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-e02a30f4282249eab7d4812deff4cb192023-12-11T17:22:24ZengMDPI AGEnergies1996-10732021-02-01144104510.3390/en14041045Forecasting of Energy Demands for Smart Home ApplicationsDhowmya Bhatt0Danalakshmi D1A. Hariharasudan2Marcin Lis3Marlena Grabowska4Faculty of Information Technology, Delhi-NCR Campus, SRM Institute of Science and Technology, Delhi-Meerut Road, Modinagar, Ghaziabad 201204, IndiaFaculty of Electrical and Electronics Engineering, GMR Institute of Technology, GMR Nagar, Rajam 532127, Andhra Pradesh, IndiaFaculty of English, Kalasalingam Academy of Research and Education, Anand Nagar, Krishnankoil 626126, IndiaFaculty of Applied Sciences, WSB University in Dabrowa Górnicza, Zygmunta Cieplaka 1c, 41-300 Dąbrowa Górnicza, PolandThe Faculty of Management, Czestochowa University of Technology, Dabrowskiego 69, 42-201 Czestochowa, PolandThe utilization of energy is on the rise in current trends due to increasing consumptions by households. Smart buildings, on the other hand, aim to optimize energy, and hence, the aim of the study is to forecast the cost of energy consumption in smart buildings by effectively addressing the minimal energy consumption. However, smart buildings are restricted, with limited power access and capacity associated with Heating, Ventilation and Air Conditioning (HVAC) units. It further suffers from low communication capability due to device limitations. In this paper, a balanced deep learning architecture is used to offer solutions to address these constraints. The deep learning algorithm considers three constraints, such as a multi-objective optimization problem and a fitness function, to resolve the price management problem and high-level energy consumption in HVAC systems. The study analyzes and optimizes the consumption of power in smart buildings by the HVAC systems in terms of power loss, price management and reactive power. Experiments are conducted over various scenarios to check the integrity of the system over various smart buildings and in high-rise buildings. The results are compared in terms of various HVAC devices on various metrics and communication protocols, where the proposed system is considered more effective than other methods. The results of the Li-Fi communication protocols show improved results compared to the other communication protocols.https://www.mdpi.com/1996-1073/14/4/1045HVAC systemsdeep learningenergy utilizationsmart buildings |
| spellingShingle | Dhowmya Bhatt Danalakshmi D A. Hariharasudan Marcin Lis Marlena Grabowska Forecasting of Energy Demands for Smart Home Applications Energies HVAC systems deep learning energy utilization smart buildings |
| title | Forecasting of Energy Demands for Smart Home Applications |
| title_full | Forecasting of Energy Demands for Smart Home Applications |
| title_fullStr | Forecasting of Energy Demands for Smart Home Applications |
| title_full_unstemmed | Forecasting of Energy Demands for Smart Home Applications |
| title_short | Forecasting of Energy Demands for Smart Home Applications |
| title_sort | forecasting of energy demands for smart home applications |
| topic | HVAC systems deep learning energy utilization smart buildings |
| url | https://www.mdpi.com/1996-1073/14/4/1045 |
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