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Machine Learning Recommendations for Control of Complex Building Systems Using Weather Forecasts
| Content Provider | Semantic Scholar |
|---|---|
| Author | Westermann, Paul David, Nigel A. Evins, Ralph |
| Copyright Year | 2018 |
| Abstract | We present a machine learning model used to provide recommendations on chiller operation based on the prediction of cooling demand using a weather forecast. A long short term memory (LSTM) formulation was used, and achieved favourable results compared to a standard approach. The model captured the data to a reasonable extent (R = 0.70), but was unable to predict very high loads at unexpected times. The model is intended to be used as an aid to a human operator, not as a replacement, and it is likely that many of these unexpected events could be overridden by the operator. Overall, the predictive model reduced the number of occasions in which a chiller was operating unnecessarily by 80.5%, or 469 hours. This demonstrates the power of data-driven predictive control to assist in the efficient operation of complex building systems, saving money, energy and operator time. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.ibpsa.org/proceedings/eSimPapers/2018/1-1-A-2.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
