Estimating the potential for thermal load management in buildings at a large scale: overcoming challenges towards a replicable methodology

Abstract A large share of the energy is consumed in buildings but at the individual scale, the energy consumption is rarely optimized; as a consequence, energy is wasted, electricity consumption is characterized by peaks and renewable energy integration is hindered. The quantification of the impact of a better energy management is a first step towards the deployment of load management techniques. This paper addresses the estimation of the thermal load management potential in buildings at a large scale. It presents a replicable methodology consisting of three steps to address the challenges of data collection, model development and thermal load optimization. The current work aims at addressing, within a single tool, various energy vectors, building types and stakeholders expectations. Application of this methodology has been initiated on an urban case study (Geneva, Switzerland) and preliminary results are presented in the paper.

[1]  Rp Rick Kramer,et al.  Simplified thermal and hygric building models： A literature review , 2012 .

[2]  Alistair B. Sproul,et al.  Optimisation of energy management in commercial buildings with weather forecasting inputs: A review , 2014 .

[3]  Rita Streblow,et al.  Low order thermal network models for dynamic simulations of buildings on city district scale , 2014 .

[4]  Dominique Marchio,et al.  Smart-E : A Tool for Energy Demand Simulation And Optimization At the City Scale , 2015, Building Simulation Conference Proceedings.

[5]  Dejan Mumovic,et al.  A review of bottom-up building stock models for energy consumption in the residential sector , 2010 .

[6]  Diane Perez,et al.  A framework to model and simulate the disaggregated energy flows supplying buildings in urban areas , 2014 .

[7]  V. I. Ugursal,et al.  A residential end‐use energy consumption model for Canada , 1998 .

[8]  Juha Jokisalo,et al.  Calculation method and tool for assessing energy consumption in the building stock , 2014 .

[9]  François Maréchal,et al.  Clustering Urban Areas for Optimizing the Design and the Operation of District Energy Systems , 2014 .

[10]  Luc Girardin.  A GIS-based Methodology for the Evaluation of Integrated Energy Systems in Urban Area , 2012 .

[11]  Frédéric Magoulès,et al.  A review on the prediction of building energy consumption , 2012 .

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

[13]  R. Belmans,et al.  Impact of residential demand response on power system operation: A Belgian case study , 2014 .

[14]  Xinhua Xu,et al.  A grey‐box model of next‐day building thermal load prediction for energy‐efficient control , 2008 .

[15]  Hans Christian Gils,et al.  Assessment of the theoretical demand response potential in Europe , 2014 .

[16]  Andrea Gasparella,et al.  Energy audit of schools by means of cluster analysis , 2015 .

[17]  Bing Liu,et al.  U.S. Department of Energy Commercial Reference Building Models of the National Building Stock , 2011 .

[18]  Y. Shimoda,et al.  Residential end-use energy simulation at city scale , 2004 .

[19]  Anil K. Jain.  Data clustering: 50 years beyond K-means , 2010, Pattern Recognit. Lett..

[20]  Sylvain Robert,et al.  State of the art in building modelling and energy performances prediction: A review , 2013 .

[21]  Yu Joe Huang,et al.  A Bottom-Up Engineering Estimate of the Aggregate Heating and Cooling Loads of the Entire U.S. Building Stock , 2000 .

[22]  Jonas Allegrini,et al.  A review of modelling approaches and tools for the simulation of district-scale energy systems , 2015 .

[23]  F. Sioshansi.  Demand-side management: The third wave , 1995 .

[24]  Rita Streblow,et al.  Demand side management for city districts , 2015 .

[25]  François Maréchal,et al.  Particle Swarm Optimization and Kalman Filtering for Demand Prediction of Commercial Buildings , 2015 .

[26]  Shem Heiple,et al.  Using building energy simulation and geospatial modeling techniques to determine high resolution building sector energy consumption profiles , 2008 .

[27]  Michalis Vazirgiannis,et al.  On Clustering Validation Techniques , 2001, Journal of Intelligent Information Systems.

[28]  Marjorie Musy,et al.  Application of sensitivity analysis in building energy simulations: combining first and second order elementary effects Methods , 2012, ArXiv.

[29]  David J. Spiegelhalter,et al.  Handling uncertainty in housing stock models , 2012 .

[30]  Filip Johnsson,et al.  Building-stock aggregation through archetype buildings: France, Germany, Spain and the UK , 2014 .

[31]  Mark Jennings,et al.  A review of urban energy system models: Approaches, challenges and opportunities , 2012 .

[32]  Evangelos Vrettos,et al.  Predictive Control of buildings for Demand Response with dynamic day-ahead and real-time prices , 2013, 2013 European Control Conference (ECC).

[33]  Johan Åkesson,et al.  Grey-Box Building Models for Model Order Reduction and Control , 2013 .

[34]  Goran Strbac,et al.  Demand side management: Benefits and challenges ☆ , 2008 .

[35]  Claire Bergaentzlé,et al.  Demand-side management and European environmental and energy goals: an optimal complementary approach , 2014 .

[36]  Dominique Marchio,et al.  Development and validation of a gray box model to predict thermal behavior of occupied office buildings , 2014 .

[37]  Christoph F. Reinhart,et al.  Urban building energy modeling – A review of a nascent field , 2015 .

[38]  M. Behrangrad.  A review of demand side management business models in the electricity market , 2015 .

[39]  Shengwei Wang,et al.  Simplified building model for transient thermal performance estimation using GA-based parameter identification , 2006 .

[40]  A. Rasheed,et al.  CITYSIM: Comprehensive Micro-Simulation of Resource Flows for Sustainable Urban Planning , 2009 .

[41]  Dirk Saelens,et al.  Quality of grey-box models and identified parameters as function of the accuracy of input and observation signals , 2014 .

[42]  Lino Guzzella,et al.  EKF based self-adaptive thermal model for a passive house , 2014 .

[43]  Laure Itard,et al.  Energy performance and comfort in residential buildings: Sensitivity for building parameters and occupancy , 2015 .

[44]  Ali M. Malkawi,et al.  A new methodology for building energy performance benchmarking: An approach based on intelligent clustering algorithm , 2014 .

[45]  Celina Filippín,et al.  Evaluation of heating energy consumption patterns in the residential building sector using stepwise selection and multivariate analysis , 2013 .

[46]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.

[47]  Jérôme Henri Kämpf,et al.  On the modelling and optimisation of urban energy fluxes , 2009 .