An Adaptive Underfloor Heating Control with External Temperature Compensation

n/

A Multivariable Self-tuning Controller for a D-type Water Tube Industrial Boiler

n/

Economic NMPC for Multiple Buildings Connected to a Heat Pump and Thermal and Electrical Storages

This paper studies the impact of different types of energy storage integrated with a heat pump to improve energy efficiency in multiple radiant-floor buildings. In particular, the buildings and the heating generation system are decoupled through a 3-element mixing valve, which enforces a fixed flow rate but a variable temperature in the inlet water entering the building pipelines. The paper presents an optimal control formulation based on an Economic Nonlinear MPC scheme, in order to find the best compromise among different goals: make the heat pump work when it is more efficient, store electrical energy when it is cheap, store thermal energy in the tank when the heat pump is more effective, modulate the inlet water temperature to satisfy the user's comfort constraints, exploit the buildings thermal inertia. The nonlinearity of the system stems from the variable flow rate into the hot water tank due to the variable action of the mixing valve. The model is also time-varying due to the fact that the heat pump efficiency depends on external conditions. The simulation results show that the proposed optimal control algorithm is able to economically distribute energy among all storages in order to insure cost benefits (almost 20% electricity cost saving) and comfort satisfaction with the feasible computational effort. Copyright (C) 2020 The Authors

A Learning-Based Model Predictive Control Strategy for Home Energy Management Systems

This paper presents a model predictive control (MPC)-based reinforcement learning (RL) approach for a home energy management system (HEMS). The house consists of an air-to-water heat pump connected to a hot water tank that supplies thermal energy to a water-based floor heating system. Additionally, it includes a photovoltaic (PV) array and a battery storage system. The HEMS is supposed to exploit the house thermal inertia and battery storage to shift demand from peak hours to off-peak periods and earn benefits by selling excess energy to the utility grid during periods of high electricity prices. However, designing such a HEMS is challenging because the discrepancies due to model mismatch make erroneous predictions of the system dynamics, leading to a non-optimal decision making. Besides, uncertainties in the house thermodynamics, misprediction in the forecasting of PV generation, outdoor temperature, and user load demand make the problem more challenging. We solve this issue by approximating the optimal policy by a parameterized MPC scheme and updating the parameters via a compatible delayed deterministic actor-critic (with gradient Q-learning critic, i.e., CDDAC-GQ) algorithm. Simulation results show that the proposed MPC-based RL HEMS can effectively deliver a policy that satisfies both indoor thermal comfort and economic costs even in the case of inaccurate model and system uncertainties. Furthermore, we conduct a thorough comparison between the CDDAC-GQ algorithm and the conventional twin delayed deep deterministic policy gradient (TD3) algorithm, the results of which affirm the efficacy of our proposed method in addressing complex HEMS problems

Cyber Physical Systems and Internet of Things: Emerging Paradigms on Smart Cities

International audienc