A Unified Methodology to Predict Wi-Fi Network Usage in Smart Buildings
| Autor: | Flavia Bernardini, Guilherme Henrique Apostolo, Luiz Magalhaes, Débora C. Muchaluat-Saade |
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| Rok vydání: | 2021 |
| Předmět: |
General Computer Science
Occupancy Computer science 020209 energy 02 engineering and technology computer.software_genre energy saving HVAC 0202 electrical engineering electronic engineering information engineering General Materials Science Building automation business.industry Wireless network General Engineering Intelligent decision support system 020206 networking & telecommunications Access point occupancy prediction machine learning Management system smart buildings Wi-Fi networks lcsh:Electrical engineering. Electronics. Nuclear engineering Data mining business lcsh:TK1-9971 computer Efficient energy use |
| Zdroj: | IEEE Access, Vol 9, Pp 11455-11469 (2021) |
| ISSN: | 2169-3536 |
| DOI: | 10.1109/access.2020.3048891 |
| Popis: | People usually spend several hours per day inside buildings, and they require great amounts of energy and resources to operate. Although there are numerous studies about smart buildings, there is still a need for new intelligent techniques for efficient smart building management. This paper proposes the use of Wi-Fi network association information as a basis for the design of intelligent systems for smart buildings. We propose a unified experimental methodology to evaluate machine learning (ML) models on their capacity to accurately predict Wi-Fi access point demand for energy-efficient smart buildings. The evaluation involves the use of multiple classification and regression models using a variety of configurations and algorithms. We conducted an experimental analysis using our proposed methodology to determine which ML models provide the best performance results using data collected from a large scale Wi-Fi network located at Fluminense Federal University (UFF) over a period of 6 months. The proposed methodology enables the user to evaluate and to create ML models for energy efficient smart building management systems. We achieved 86.69% accuracy for occupancy prediction using classification techniques and RMSPE (Root Mean Squared Percentage Error) of 0.29 for occupancy count prediction using regression techniques. |
| Databáze: | OpenAIRE |
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