The Synergy of Simulation and Time Series Forecasting for Live Performance Testing of Smart Buildings
Pages 579 - 583
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.
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- The Synergy of Simulation and Time Series Forecasting for Live Performance Testing of Smart Buildings
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Published In
December 2019
709 pages
ISBN:9781450371797
DOI:10.1145/3366030
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- JKU: Johannes Kepler Universität Linz
- @WAS: International Organization of Information Integration and Web-based Applications and Services
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Association for Computing Machinery
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Publication History
Published: 22 February 2020
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iiWAS2019
iiWAS2019: The 21st International Conference on Information Integration and Web-based Applications & Services
December 2 - 4, 2019
Munich, Germany
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