Disaggregating high-resolution gas metering data using pattern recognition

© 2018 Elsevier B.V. Growing concern about the scale and extent of the gap between predicted and actual energy performance of new and retrofitted UK homes has led to a surge in the development of new tools and technologies trying to address the problem. A vital aspect of this work is to improve ease and accuracy of measuring in-use performance to better understand the extent of the gap and diagnose its causes. Existing approaches range from low cost but basic assessments allowing very limited diagnosis, to intensively instrumented experiments that provide detail but are expensive and highly disruptive, typically requiring the installation of specialist monitoring equipment and often vacating the house for several days. A key challenge in reducing the cost and difficulty of complex methods in occupied houses is to disaggregate space heating energy from that used for other uses without installing specialist monitoring equipment. This paper presents a low cost, non-invasive approach for doing so for a typical occupied UK home where space heating, hot water and cooking are provided by gas. The method, using dynamic pattern matching of total gas consumption measurements, typical of those provided by a smart meter, was tested by applying it to two occupied houses in the UK. The findings revealed that this method was successful in detecting heating patterns in the data and filtering out coinciding use

HBIM: Low-cost sensors and environmental data in heritage buildings - A guide for practitioners and professionals

This guide is intended to introduce the heritage conservation professional to the use of low cost sensors to capture environmental data in occupied heritage buildings, for the purposes of enhancing the heritage preservation practice with the capability for real-time monitoring and analysis of the buildings state.The first part of this document is an introduction to the applications of sensors and data capture in buildings, followed by a more detailed discussion of the particular variables to be captured and the technology available. The second part is a guide to choosing equipment, deployment, and using the captured data, with recommendations for best practice

A guide for monitoring the effects of climate change on heritage building materials and elements

This report is concerned with advanced tools and methods for monitoring the effects of climate change in buildings. It addresses the expected changes, the effects on the fabric of a heritage building, and the mechanisms of deterioration. This will be addressed only using the data and measurements that is being collected as part of the HBIM process.This report was produced as a part of a Newton Fund-sponsored research project 'Heritage Building Information Modelling and Smart Heritage Buildings Performance Measurements for Sustainability

A comparison of data collection methods for spatial analysis

This report looks at three methods for capturing the geometry of buildings and their elements to be used in the generation of energy models of those buildings. A heritage building in Salford, UK, is used as a case study, receiving each data collection method. Energy models developed based upon data collected for this building is analysed for variations in geometry and predictions of energy performance

Comparison of prediction tools to determine their reliability on calculating operational heating consumption by monitoring no-fines concrete dwellings

© 2018 Elsevier B.V. Nowadays most retrofit decisions are based on reducing CO2 / heating consumption. The aim of this study was to determine the reliability of three tools (RdSAP, SAP and IES) often used to predict these reductions. Three no-fines concrete (NFC) dwellings (C1, C2, and C3) with similar floor area and construction but different occupants were monitored. Key information about the thermal performance of the fabric; the behaviour of the occupants and the energy consumption was collected before and after 110 mm of external wall insulation (EWI) was added. The target was a 30% reduction on energy consumption due to the EWI. However, only C3 decreased it by 30% as expected, C2 only by 14% due to a subtle rebound effect and C1 actually increased consumption by 75%, due to rebound effect. Steady state tools (RdSAP and SAP) were found to be inaccurate in predicting the operational energy consumption of dwellings, only dynamic performance analysis software (IES) was suitable to carry out this type of prediction accurately. However, this type of software requires highly accurate and detailed information regarding: the baseline performance of the fabric, external weather conditions and, most importantly, accurate pre- and post- heating operational habits of the occupants. Few retrofitting projects have the resources and time to gather this information. Unless those are available, the retrofit decisions should be based in a different criteria, rather than using inaccurate SAP or RdSAP energy consumption predictions. The coefficient of heat loss of the fabric of a dwelling is independent of the occupants. SAP was found quick to calculate reasonable predictions of the coefficient, by using accurate fabric data, and to show the impact of different factors on the heat loss of the fabric. Therefore, it could be claimed that the coefficient of heat loss of the fabric is a suitable alternative criteria to make pre-retrofit decisions