Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Extraction of supervisory building control rules from model predictive control of windows in a mixed mode building
Rule extraction is a promising technique for developing or fine-tuning supervisory control strategies in buildings. Three data mining techniques are examined that extract rules from offline model predictive control (MPC) results for a mixed mode building operated during the cooling season: generalized linear models (GLM), classification and regression trees (CART), and adaptive boosting. All rules were able to recover approximately 90% of the original optimizer energy savings under open loop tests, but the GLM-based rules saw significant performance degradation under simulated tests. CART and boost rules only degraded in performance by a few percentage points, still retaining the vast majority of optimizer savings (84% and 93% for the CART and boost rules, respectively). The results demonstrate that the proposed rule extraction techniques may allow building automation systems to achieve near-optimal supervisory control strategies without online MPC systems, although further research is required to broadly test applicability to more complex cases.
Extraction of supervisory building control rules from model predictive control of windows in a mixed mode building
Rule extraction is a promising technique for developing or fine-tuning supervisory control strategies in buildings. Three data mining techniques are examined that extract rules from offline model predictive control (MPC) results for a mixed mode building operated during the cooling season: generalized linear models (GLM), classification and regression trees (CART), and adaptive boosting. All rules were able to recover approximately 90% of the original optimizer energy savings under open loop tests, but the GLM-based rules saw significant performance degradation under simulated tests. CART and boost rules only degraded in performance by a few percentage points, still retaining the vast majority of optimizer savings (84% and 93% for the CART and boost rules, respectively). The results demonstrate that the proposed rule extraction techniques may allow building automation systems to achieve near-optimal supervisory control strategies without online MPC systems, although further research is required to broadly test applicability to more complex cases.
Extraction of supervisory building control rules from model predictive control of windows in a mixed mode building
May-Ostendorp, Peter T. (Autor:in) / Henze, Gregor P. (Autor:in) / Rajagopalan, Balaji (Autor:in) / Corbin, Charles D. (Autor:in)
Journal of Building Performance Simulation ; 6 ; 199-219
01.05.2013
21 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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