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State-space models for building control: how deep should you go?
Journal of Building Performance Simulation ( IF 2.2 ) Pub Date : 2020-09-14 , DOI: 10.1080/19401493.2020.1817149
Baptiste Schubnel 1 , Rafael E. Carrillo 1 , Paolo Taddeo 2 , Lluc Canal Casals 2, 3 , Jaume Salom 2 , Yves Stauffer 1 , Pierre-Jean Alet 1
Affiliation  

Power consumption in buildings show nonlinear behaviours that linear models cannot capture, whereas recurrent neural networks (RNNs) can. This ability makes RNNs attractive alternatives for the model-predictive control (MPC) of buildings. However, RNNs are nonlinear and non-smooth functions which makes their use challenging in optimization problems. Therefore, this work systematically investigates whether using RNNs for building control provides net gains in MPC. It compares over 2 months of simulated operation the representation power and control performance of two architectures: an RNN architecture and a linear state-space (LSS) model with a nonlinear regressor to estimate energy consumption. The results show that RNNs yield an identification error 69% lower than LSS, but the LSS models yield control laws that achieve 10% lower objective function with a computational time three times lower than the RNNs. Thus, on balance, well-designed LSS models with nonlinear regressors are best in most cases of MPC.



中文翻译:

用于建筑物控制的状态空间模型:您应该走多深?

建筑物中的功耗显示出线性模型无法捕获的非线性行为,而递归神经网络(RNN)可以捕获。此功能使RNN成为建筑物的模型预测控制(MPC)的有吸引力的替代方案。但是,RNN是非线性和非平滑函数,这使得它们在优化问题中的使用具有挑战性。因此,这项工作系统地调查了使用RNN进行建筑物控制是否可以在MPC中提供净收益。它比较了两个月以上的模拟操作的两种架构的表示能力和控制性能:RNN架构和带有非线性回归器的线性状态空间(LSS)模型,以估算能耗。结果表明,RNN的识别误差比LSS低69%,但是LSS模型产生的控制律可以使目标函数降低10%,而计算时间却比RNN低三倍。因此,总的来说,在大多数MPC情况下,设计良好的具有非线性回归的LSS模型是最好的。

更新日期:2020-09-15
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