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A forecasting strategy based on wireless sensing for thermal comfort optimization in smart buildings
Author(s) -
Viani Federico,
Polo Alessandro
Publication year - 2017
Publication title -
microwave and optical technology letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.304
H-Index - 76
eISSN - 1098-2760
pISSN - 0895-2477
DOI - 10.1002/mop.30855
Subject(s) - thermal comfort , building automation , wireless , ventilation (architecture) , building management system , computer science , air conditioning , hvac , measure (data warehouse) , indoor air quality , reliability engineering , energy (signal processing) , simulation , real time computing , engineering , architectural engineering , automotive engineering , control (management) , telecommunications , artificial intelligence , mechanical engineering , data mining , meteorology , statistics , physics , mathematics , environmental engineering , thermodynamics
Abstract The prediction of thermal comfort in smart buildings is addressed in this article to support the energy manager in the management of the heating, ventilation, and air conditioning systems. An efficient control is fundamental to meet the user satisfaction as well as the energy cost reduction. The proposed method based on a customized support vector regression technique predicts and suggests to the energy managers the indoor target temperature required to obtain the desired comfort throughout the building. A network of low‐cost wireless sensors has been deployed to measure the indoor conditions of a real indoor scenario. The prediction performance has been experimentally validated and the obtained results have pointed out a temperature prediction error lower than 1°C and the achievement of the thermal comfort requirements.