Proactive control for solar energy exploitation: A german high-inertia building case study

Energy efficient passive designs and constructions have been extensively studied in the last decades as a way to improve the ability of a building to store thermal energy, increase its thermal mass, increase passive insulation and reduce heat losses. However, many studies show that passive thermal designs alone are not enough to fully exploit the potential for energy efficiency in buildings: in fact, harmonizing the active elements for indoor thermal comfort with the passive design of the building can lead to further improvements in both energy efficiency and comfort. These improvements can be achieved via the design of appropriate Building Optimization and Control (BOC) systems, a task which is more complex in high-inertia buildings than in conventional ones. This is because high thermal mass implies a high memory, so that wrong control decisions will have negative repercussions over long time horizons. The design of proactive control strategies with the capability of acting in advance of a future situation, rather than just reacting to current conditions, is of crucial importance for a full exploitation of the capabilities of a high-inertia building. This paper applies a simulation-assisted control methodology to a high-inertia building in Kassel, Germany. A simulation model of the building is used to proactively optimize, using both current and future information about the external weather condition and the building state, a combined criterion composed of the energy consumption and the thermal comfort index. Both extensive simulation as well as real-life experiments performed during the unstable German wintertime, demonstrate that the proposed approach can effectively deal with the complex dynamics arising from the high-inertia structure, providing proactive and intelligent decisions that no currently employed rule-based strategy can replicate.

[1]  K. A. Antonopoulos,et al.  Effect of indoor mass on the time constant and thermal delay of buildings , 2000 .

[2]  Jonathan Karlsson,et al.  A conceptual model that simulates the influence of thermal inertia in building structures , 2013 .

[3]  Sean Hay Kim,et al.  An evaluation of robust controls for passive building thermal mass and mechanical thermal energy storage under uncertainty , 2013 .

[4]  Abdullatif Ben-Nakhi,et al.  Energy conservation in buildings through efficient A/C control using neural networks , 2002 .

[5]  Ruxu Du,et al.  Design of intelligent comfort control system with human learning and minimum power control strategies , 2008 .

[6]  Dimitrios Gyalistras,et al.  Potential Assessment of Rule-Based Control for Integrated Room Automation , 2010 .

[7]  Andrew Kusiak,et al.  Modeling and optimization of HVAC energy consumption , 2010 .

[8]  Andrea Costa,et al.  Building operation and energy performance: Monitoring, analysis and optimisation toolkit , 2013 .

[9]  Viktor Dorer,et al.  Thermally activated building systems (TABS): Energy efficiency as a function of control strategy, hydronic circuit topology and (cold) generation system , 2011 .

[10]  Liu Yang,et al.  Thermal comfort and building energy consumption implications - A review , 2014 .

[11]  Luisa F. Cabeza,et al.  Improvement of the thermal inertia of building materials incorporating PCM. Evaluation in the macroscale , 2013 .

[12]  Jan-Olof Dalenbäck,et al.  Model-based controllers for indoor climate control in office buildings – Complexity and performance evaluation , 2014 .

[13]  Standard Ashrae Thermal Environmental Conditions for Human Occupancy , 1992 .

[14]  George J. Pappas,et al.  Receding-horizon supervisory control of green buildings , 2011, Proceedings of the 2011 American Control Conference.

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

[16]  Iakovos Michailidis,et al.  Intelligent energy and thermal comfort management in grid-connected microgrids with heterogeneous occupancy schedule , 2015 .

[17]  Viktor Dorer,et al.  Interaction of an air system with concrete core conditioning , 1999 .

[18]  Fabio Polonara,et al.  State of the art of thermal storage for demand-side management , 2012 .

[19]  Max Donath,et al.  American Control Conference , 1993 .

[20]  Niccolò Aste,et al.  The influence of the external walls thermal inertia on the energy performance of well insulated buildings , 2009 .

[21]  Elias B. Kosmatopoulos,et al.  A roadmap towards intelligent net zero- and positive-energy buildings , 2011 .

[22]  Shengwei Wang,et al.  Development of an adaptive Smith predictor-based self-tuning PI controller for an HVAC system in a test room , 2008 .

[23]  M Morari,et al.  Energy efficient building climate control using Stochastic Model Predictive Control and weather predictions , 2010, Proceedings of the 2010 American Control Conference.

[24]  Tianzhen Hong,et al.  An insight into actual energy use and its drivers in high-performance buildings , 2014 .

[25]  Iakovos Michailidis,et al.  Convex Design Control for Practical Nonlinear Systems , 2014, IEEE Transactions on Automatic Control.

[26]  Iakovos Michailidis,et al.  A "plug and play" computationally efficient approach for control design of large-scale nonlinear systems using cosimulation: a combination of two "ingredients" , 2014, IEEE Control Systems.

[27]  V. Ismet Ugursal,et al.  Comparison of neural network, conditional demand analysis, and engineering approaches for modeling end-use energy consumption in the residential sector , 2008 .

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

[29]  Iakovos Michailidis,et al.  A “plug-n-play” computationally efficient approach for control design of large-scale nonlinear systems using co-simulation , 2013, 52nd IEEE Conference on Decision and Control.

[30]  Kragh Berglund Plug'N'Play , 2010 .

[31]  José Domingo Álvarez,et al.  Optimizing building comfort temperature regulation via model predictive control , 2013 .

[32]  Elias B. Kosmatopoulos,et al.  Large Scale Nonlinear Control System Fine-Tuning Through Learning , 2009, IEEE Transactions on Neural Networks.

[33]  Michael Wetter,et al.  Co-simulation of innovative integrated HVAC systems in buildings , 2009 .

[34]  Eva Žáčeková,et al.  Towards the real-life implementation of MPC for an office building: Identification issues , 2014 .

[35]  Armando C. Oliveira,et al.  A field study on building inertia and its effects on indoor thermal environment , 2012 .

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

[37]  Aristides Kiprakis,et al.  A multi-objective approach for optimal prioritization of energy efficiency measures in buildings: Model, software and case studies , 2015 .

[38]  Elias B. Kosmatopoulos,et al.  Simulation-assisted building energy performance improvement using sensible control decisions , 2011, BuildSys '11.

[39]  R. Bellman Dynamic programming. , 1957, Science.

[40]  Elias B. Kosmatopoulos,et al.  An adaptive optimization scheme with satisfactory transient performance , 2009, Autom..

[41]  Michaël Kummert,et al.  CONDUCTION TRANSFER FUNCTIONS IN TRNSYS MULTIZONE BUILDING MODEL: CURRENT IMPLEMENTATION, LIMITATIONS AND POSSIBLE IMPROVEMENTS , 2012 .

[42]  Teuku Meurah Indra Mahlia,et al.  Modeling and simulation of the energy use in an occupied residential building in cold climate , 2012 .

[43]  Jonathan A. Wright,et al.  Optimization of building thermal design and control by multi-criterion genetic algorithm , 2002 .