期刊
SUSTAINABILITY
卷 9, 期 6, 页码 -出版社
MDPI
DOI: 10.3390/su9060982
关键词
chaos; inverse model; support vector; model predictive control; building simulation
资金
- National Research Foundation of Korea (NRF) grant - Korea government (MSIP) [2015R1C1A1A01052976]
- National Research Foundation of Korea [2015R1C1A1A01052976] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
This paper presents an inverse model using chaotic behaviour. The chaos time series inverse model, which uses coupling methods between an inverse model and chaos theory can reconstruct a deterministic and low-dimensional phase space by transforming irregular behaviours of nonlinear time-varying systems into a strange attractor (e.g., a Rossler attractor or a Lorenz attractor), and it can then predict future states. For this study, the author used a training dataset measured in an existing high-rise building and examined the predictive abilities of the chaos time series inverse model modelled into phase spaces with strange attractors in comparison with those of the Support Vector Regression (SVR) out of the inverse model. The paper discusses the effective use of the chaos time series inverse model for multi-step ahead prediction.
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