Calibrating building energy models using supercomputer trained machine learning agents
| Autor: | Lynne E. Parker, Richard E. Edwards, Joshua New, Jibonananda Sanyal |
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| Rok vydání: | 2014 |
| Předmět: |
Computer Networks and Communications
Computer science business.industry Big data Energy modeling Machine learning computer.software_genre Supercomputer Computer Science Applications Theoretical Computer Science Software Computational Theory and Mathematics Calibration Artificial intelligence business computer |
| Zdroj: | Concurrency and Computation: Practice and Experience. 26:2122-2133 |
| ISSN: | 1532-0626 |
| Popis: | Building energy modeling BEM is an approach to model the energy usage in buildings for design and retrofit purposes. EnergyPlus is the flagship Department of Energy software that performs BEM for different types of buildings. The input to EnergyPlus can often extend in the order of a few thousand parameters that have to be calibrated manually by an expert for realistic energy modeling. This makes it challenging and expensive thereby making BEM unfeasible for smaller projects. In this paper, we describe the 'Autotune' research that employs machine learning algorithms to generate agents for the different kinds of standard reference buildings in the US building stock. The parametric space and the variety of building locations and types make this a challenging computational problem necessitating the use of supercomputers. Millions of EnergyPlus simulations are run on supercomputers that are subsequently used to train machine learning algorithms to generate agents. These agents, once created, can then run in a fraction of the time thereby allowing cost-effective calibration of building models. Published 2014. This article is a US Government work and is in the public domain in the USA. |
| Databáze: | OpenAIRE |
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