WSN system design by using an innovative neural network model to perform thermals forecasting in a urban canyon scenario

Nicolosi, Giuseppina; Tirrito, Salvatore

doi:10.1063/1.4938961

Cited by 4 publications

(1 citation statement)

References 14 publications

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“…In this context, this study classified this technology into three categories based on data used as input to control HVAC energy consumption. The first category [15,16,18,[35][36][37][38][39][40][41][42][43][44] of the control system uses temperature and humidity data. The second category [45][46][47][48] uses temperature, humidity, and an infrared camera.…”

Section: Classification Based On Sbems Technologymentioning

confidence: 99%

Systematic Review Analysis on Smart Building: Challenges and Opportunities

et al. 2022

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Smart building technology incorporates efficient and automated controls and applications that use smart energy products, networked sensors, and data analytics software to monitor environmental data and occupants’ energy consumption habits to improve buildings’ operation and energy performance. Smart technologies and controls are becoming increasingly important not only in research and development (R&D) but also in industrial and commercial domains, leading to a steady growth in their application in the building sector. This study examines the literature on SBEMS published between 2010 and 2020 with a systematic approach. It examines the trend with the annual number of the published studies before exploring the classification of publications in terms of factors such as domain of SBEMS, control approaches, smart technologies, and quality attributes. Recent developments around the smart building energy management systems (SBEMS) have focused on features that provide occupants with an interface to monitor, schedule, and modify building energy consumption profiles and allow a utility to participate in a communication grid through demand response programs and automatic self-report outage functionality. The study also explores future research avenues, especially in terms of improvements in privacy and security, and interoperability. It is also suggested that the smart building technologies’ smartness can be improved with the help of solutions such as real-time data monitoring and machine learning

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