A simplified building thermal model for the optimization of energy consumption: Use of a random number generator

Abstract This paper proposes a new method for an optimal control of the heating system at the building scale. This control is a new approach of energy planning that aims to decrease the heating consumption/expenses over a defined prediction horizon while respecting the occupants’ thermal comfort. It employs a simplified building thermal model to simulate the building thermal behavior taking into consideration the weather predictions. This approach is based on the application of Monte Carlo method, i.e., a random generator for the heating system scenarios. The aim is to determine the optimal heating plan for the prediction horizon that fulfills the constraints regarding the thermal comfort of occupants and the minimization of the energy consumption/expenses together with achieving a load shedding.

[1]  Kiam Heong Ang,et al.  PID control system analysis and design , 2006, IEEE Control Systems.

[2]  Christian Ghiaus,et al.  Optimal temperature control of intermittently heated buildings using Model Predictive Control: Part I – Building modeling , 2012 .

[3]  Mesut Avci,et al.  Demand Response-Enabled Model Predictive HVAC Load Control in Buildings using Real-Time Electricity Pricing , 2013 .

[4]  Bernard Friedland,et al.  Control System Design: An Introduction to State-Space Methods , 1987 .

[5]  J. Mikael Eklund,et al.  Comparison of apartment building heating control systems , 2011, 2011 24th Canadian Conference on Electrical and Computer Engineering(CCECE).

[6]  Francisco Rodríguez,et al.  A comparison of thermal comfort predictive control strategies , 2011 .

[7]  Thomas Weng,et al.  Occupancy-driven energy management for smart building automation , 2010, BuildSys '10.

[8]  Kody M. Powell,et al.  Reduced-order residential home modeling for model predictive control , 2014 .

[9]  Yun Li,et al.  PID control system analysis, design, and technology , 2005, IEEE Transactions on Control Systems Technology.

[10]  N. Eskin,et al.  Analysis of annual heating and cooling energy requirements for office buildings in different climates in Turkey , 2008 .

[11]  Petre Stoica,et al.  Decentralized Control , 2018, The Control Systems Handbook.

[12]  Clark W Gellings,et al.  The Smart Grid: Enabling Energy Efficiency and Demand Response , 2020 .

[13]  Benjamin Paris,et al.  Heating control schemes for energy management in buildings , 2010 .

[14]  Marc A. Rosen,et al.  District heating and cooling: Review of technology and potential enhancements , 2012 .

[15]  Niels Kjølstad Poulsen,et al.  Economic Model Predictive Control for building climate control in a Smart Grid , 2012, 2012 IEEE PES Innovative Smart Grid Technologies (ISGT).

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

[17]  Aaron P. Wemhoff,et al.  Calibration of HVAC equipment PID coefficients for energy conservation , 2012 .

[18]  Rita Streblow,et al.  EVALUATION OF INTERACTIONS BETWEEN BUILDINGS AND DISTRICT HEATING NETWORKS , 2013 .

[19]  B. Favre.  Etude de stratégies de gestion énergétique des bâtiments par l'application de la programmation dynamique , 2013 .

[20]  Céline Rouquette.  Bilan énergétique de la France pour 2011 , 2012 .

[21]  Sean P. Meyn,et al.  Building thermal model reduction via aggregation of states , 2010, Proceedings of the 2010 American Control Conference.

[22]  Li Lan,et al.  Method for the determination of optimal work environment in office buildings considering energy consumption and human performance , 2014 .

[23]  David Q. Mayne,et al.  Constrained model predictive control: Stability and optimality , 2000, Autom..

[24]  Thierry Talbert,et al.  A procedure for modeling buildings and their thermal zones using co-simulation and system identification , 2014 .

[25]  Youming Chen,et al.  A new approach for measuring predicted mean vote (PMV) and standard effective temperature (SET , 2003 .

[26]  Lukas Ferkl,et al.  Model-based energy efficient control applied to an office building , 2014 .

[27]  Burcin Becerik-Gerber,et al.  Towards Optimization of Building Energy and Occupant Comfort Using Multi-Agent Simulation , 2011 .

[28]  William D'haeseleer,et al.  Control of heating systems in residential buildings: Current practice , 2008 .

[29]  Francisco Rodríguez,et al.  Thermal comfort control using a non-linear MPC strategy: A real case of study in a bioclimatic building , 2014 .

[30]  Marouf Pirouti,et al.  Modelling and analysis of a district heating network , 2013 .

[31]  V. Zavala.  Real-Time Optimization Strategies for Building Systems† , 2013 .

[32]  Josh Wall,et al.  Trial results from a model predictive control and optimisation system for commercial building HVAC , 2014 .

[33]  José Luis Guzmán,et al.  Efficient building energy management using distributed model predictive control , 2014 .

[34]  Gail Brager,et al.  Climate, Comfort & Natural Ventilation: A new adaptive comfort standard for ASHRAE Standard 55 , 2001 .