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The Challenges of Monitoring Energy Consumption to Assess Behavioural Changes in Occupants During Renovation Projects from a Low Budget Point of View
Author(s) -
Carlos Jimenez-Bescos
Publication year - 2016
Publication title -
energy procedia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.474
H-Index - 81
ISSN - 1876-6102
DOI - 10.1016/j.egypro.2016.09.166
Subject(s) - metre , electricity meter , energy consumption , smart meter , data collection , electricity , data logger , stairs , engineering , environmental science , computer science , automotive engineering , electrical engineering , civil engineering , statistics , power (physics) , physics , mathematics , quantum mechanics , astronomy , operating system
This study presents the challenges of monitoring energy consumption to assess behavioral changes in occupants during renovation projects and how to develop a strategy to overcome those challenges. Three buildings containing seventeen flats in total were monitored during a period of over two years, monitoring energy consumption and internal temperature of the flats. The energy consumption for the flats was capture by the use of a Current Cost smart meter placed at the ground floor of each block of flats in a cover under stairs. Wireless sensors were clamped to each flat. The smart meters were not connected to the internet or a computer, but instead the data was stored in the internal memory of the equipment and manually downloaded to a computer at each data collection visit. For the purpose of double checking, meter readings were collected at the electricity meter to be able to compared the accuracy and success of the data collected by the Current Cost smart meters. The internal temperature of every flat was collected by mean of a LogTag temperature data. From comparing the Current Cost smart meter data versus the utility meter readings a huge range of overestimation and underestimation of the actual energy consumption was observed. Regarding the internal temperature monitoring, long periods of lost data was identified. The main factors contributing to these data lost were: Long periods between data collection, wireless signal drop offs, unplugging, tampering and removal of equipment. A normalized energy index, based on utility meter readings, internal temperature and outdoor conditions, was developed to overcome the loss of data and assess the effectiveness of the technology and the behavioral change in the occupants. The normalised energy index provides a methodology to evaluate technology and behavioural changes effect between flats and across seasons

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