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.

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。