The Synergy of Simulation and Time Series Forecasting for Live Performance Testing of Smart Buildings

Abstract

Differences in requirements for reliability in buildings imply the different needs for calculation of expected building behaviour. In this paper we examine four techniques for calculating expected behaviour of buildings. Two of them are simulation techniques, namely, a white box EnergyPlus model and a æ static tool as per the requirements of the Danish government. The other two are machine learning techniques, namely an ARIMA model, and an long short-term memory artificial recurrent neural network, used in deep learning. We compare and contrast these four techniques based on their accuracy of forecast, as well as execution time to forecast a new data point. Furthermore, we provide an algorithm for selection of forecasting technique based on terms such as availability, accuracy, and execution time requirements, to facilitate real time threshold generation in light of building performance testing.