Assessing the Operational Energy Profiles of UK educational buildings: findings from detailed surveys and modelling compared to measured consumption

摘要

This paper presents the preliminary findings from the first stage of a physical survey and modelling Case Study conducted to obtain Modelled and Actual Energy Consumption Profiles for a UK multi-storey mixed use educational building. The purpose of the study is to provide an insight into how accurately current models and software can predict the actual energy consumption in such a building, with a view to informing the development of Operational and Asset Ratings for Buildings in the EU as part of the EPBD Article 7 requirements. The study also briefly discusses the potential problems inherent in the use of modelling techniques for assessing the energy performance of buildings. The data obtained through this study allowed predicted energy usage profiles to be compiled from the level of individual items of equipment through to the whole building. These data were subsequently analysed using standard spreadsheet and building energy simulation software. The results of the analysis enabled predicted energy consumption profiles for both heating/cooling and electrical energy use to be obtained, as well as a UK iSBEM asset-type compliance rating. The predicted profiles and compliance rating were then compared to the monitored actual energy consumption profiles obtained over the same period. The main conclusions were that, despite the time needed to undertake the physical survey, the level of detail of this study and survey were insufficient to predict the energy consumptions of the building with confidence. It was seen that the various approaches gave a reasonable estimate of the gas consumption using ECOTECT, and a reasonable estimate of the electrical consumption using iSBEM. However, overall it was felt that the models were too inaccurate to be used with any confidence. This results of this Case Study also support the view that for prediction of electrical consumption then statistical measures, such as benchmarks, are likely to enable more confident predictions of energy use by genericactivity type.