Utilising Measured Building Data to Gain Environmental Feedback in Real-time as Early Design and Briefing Decisions are Made

It has been argued that traditional building simulation methods can be a slow process, which often fails to integrate into the decision making process of non-technical designers, such as architects, at the early design stages. Furthermore, studies have shown that predicted energy consumption of buildings through the use of building simulation is often lower than monitored energy consumption during operation.

In view of this, this paper outlines research to communicate building performance based on monitored energy data in real-time as design and briefing parameters are altered interactively. As a test case, the research focuses on school design within Greater London. An artificial neural network (ANN) has been trained to predict the energy consumption of new school designs by linking actual heating and electrical energy consumption data from the existing building stock to a range of design and building use parameters. A product of this research will be a user friendly environmental design tool that offers non-technical designers a quick sanity check at the early design stages.