Extraction of supervisory building control rules from model predictive control of windows in a mixed mode building

Abstract

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.