Smart control of dynamic phase change material wall system

Abstract This work presents two different smart control algorithms to manage a novel phase change material system integrated into building walls and roofs. This system is able to move a phase change material layer with respect to the insulation layer inside the building component. With this ability, the system can increase solar benefits in winter and take profit from night free cooling in summer. During the heating season, the system places the phase change material facing outdoors during sunny hours to melt it, and it moves the phase change material back facing indoors to provide space heating when desired. In the cooling season, the phase change material is moved to the outer face of insulation at night time to enhance its solidification process, and it is moved back to face indoors during cooling peak hours. An appropriate control system, referring to the schedule of operation and placement of phase change material layer with respect to the insulation (when phase change material is facing outdoors or indoors) is critical to achieve savings and avoid malfunctioning of the system. In this work, we have developed and numerically compared two different control algorithms based on weather forecast data for space heating and cooling applications. Experimentation has been done under four different climate conditions: Athens, Madrid, Strasbourg, and Helsinki. One of the control algorithms, based on local search (Tabu), provided the set of activations of the dynamic system for a 24 h period. The other algorithm is based on model predictive control with an horizon of 2.5 and 5 h. Results proved the feasibility of the two smart control methods, as well as their capacity to improve the energy benefits of the dynamic phase change material system in days with suitable weather conditions. Moreover, the two control algorithms successfully avoided activating the system in days with non-appropriate weather conditions.

[1]  Luisa F. Cabeza,et al.  Control strategies comparison of a ventilated facade with PCM – energy savings, cost reduction and CO2 mitigation , 2016 .

[2]  Frauke Oldewurtel,et al.  Experimental analysis of model predictive control for an energy efficient building heating system , 2011 .

[3]  James B. Rawlings,et al.  Tutorial: model predictive control technology , 1999, Proceedings of the 1999 American Control Conference (Cat. No. 99CH36251).

[4]  Luisa F. Cabeza,et al.  Experimental study of using PCM in brick constructive solutions for passive cooling , 2010 .

[5]  Gracia Cuesta,et al.  Numerical Analysis of Building Envelope with Movable Phase Change Materials for Heating Applications , 2019, Applied Sciences.

[6]  Luisa F. Cabeza,et al.  Phase change materials and thermal energy storage for buildings , 2015 .

[7]  S. Wilcox,et al.  Users Manual for TMY3 Data Sets (Revised) , 2008 .

[8]  Manfred Morari,et al.  Use of model predictive control and weather forecasts for energy efficient building climate control , 2012 .

[9]  Simeng Liu,et al.  Experimental analysis of simulated reinforcement learning control for active and passive building thermal storage inventory: Part 1. Theoretical foundation , 2006 .

[10]  Kaamran Raahemifar,et al.  Application of passive wall systems for improving the energy efficiency in buildings: A comprehensive review , 2016 .

[11]  Alvaro de Gracia,et al.  Dynamic building envelope with PCM for cooling purposes – Proof of concept , 2019, Applied Energy.

[12]  R. Belmans,et al.  Reinforcement Learning Applied to an Electric Water Heater: From Theory to Practice , 2015, IEEE Transactions on Smart Grid.

[13]  José R. Vázquez-Canteli,et al.  Balancing comfort and energy consumption of a heat pump using batch reinforcement learning with fitted Q-iteration , 2017 .

[14]  Ryozo Ooka,et al.  Predictive control strategies based on weather forecast in buildings with energy storage system: A review of the state-of-the art , 2017 .

[15]  Paul Cooper,et al.  Hybrid model predictive control of a residential HVAC system with on-site thermal energy generation and storage , 2017 .

[16]  R. Lehtiniemi,et al.  Numerical and experimental investigation of melting and freezing processes in phase change material storage , 2004 .

[17]  G. Zannis,et al.  Experimental thermal characterization of a Mediterranean residential building with PCM gypsum board walls , 2013 .

[18]  Manuel Laguna,et al.  Tabu Search , 1997 .

[19]  Farrokh Janabi-Sharifi,et al.  Theory and applications of HVAC control systems – A review of model predictive control (MPC) , 2014 .

[20]  Luisa F. Cabeza,et al.  Numerical study on the thermal performance of a ventilated facade with PCM , 2013 .

[21]  Sven Leyffer,et al.  Mixed Integer Nonlinear Programming , 2011 .

[22]  Luisa F. Cabeza,et al.  Control of a PCM ventilated facade using reinforcement learning techniques , 2015 .

[23]  Luisa F. Cabeza,et al.  Model Predictive Control Strategy Applied to Different Types of Building for Space Heating , 2018, Thermal Energy Storage with Phase Change Materials.

[24]  Yang Zhao,et al.  MPC-based optimal scheduling of grid-connected low energy buildings with thermal energy storages , 2015 .

[25]  Bart De Schutter,et al.  Building day-ahead bidding functions for seasonal storage systems: A reinforcement learning approach , 2019 .

[26]  Fred W. Glover,et al.  Future paths for integer programming and links to artificial intelligence , 1986, Comput. Oper. Res..

[27]  Johan Driesen,et al.  Deep Reinforcement Learning based Optimal Control of Hot Water Systems , 2018, ArXiv.

[28]  Luisa F. Cabeza,et al.  Thermal behaviour of insulation and phase change materials in buildings with internal heat loads: experimental study , 2015 .

[29]  Nikolaos V. Sahinidis,et al.  Mixed-integer nonlinear programming 2018 , 2019, Optimization and Engineering.

[30]  B. Rudolf,et al.  World Map of the Köppen-Geiger climate classification updated , 2006 .

[31]  A. Gracia,et al.  Model predictive control applied to a heating system with PV panels and thermal energy storage , 2020 .

[32]  José Antonio Almendros-Ibáñez,et al.  A numerical study of external building walls containing phase change materials (PCM). , 2012 .