First results of remote building characterisation based on smart meter measurement data

Abstract In European households, 79% of the energy is consumed for space heating and cooling. The remote detection of possible retrofitting targets can help to increase the renovation rate and hence contribute to the realization of the 2000 W society. Here, a new method to characterize buildings based on smart meter monitoring data and a simplified physical simulation model is presented. The aim of this method is to estimate the time dependent demand of heating energy based on weather data applying these simplified building models. The method has been successfully applied on simulation and real-world smart meter monitoring data. The annual space energy demand was excellently reproduced with a deviation of less than 1% and 8% for simulation and real-world buildings, respectively. The recovered relevant building parameters deviate less than 1% for the reference case. The successful application of the algorithm on in-silico and real-world data monitoring data indicates the vast potential of this automated modelling technique on heat load prediction and energy-efficient operation of buildings. Furthermore, the derived heat demand profile may help utilities and facility managers in the future to identify better operation schedules of small areas and districts.

[1]  M. Hadi Amini,et al.  A novel multi-time-scale modeling for electric power demand forecasting: From short-term to medium-term horizon , 2017 .

[2]  Tanveer Ahmad,et al.  Supervised based machine learning models for short, medium and long-term energy prediction in distinct building environment , 2018, Energy.

[3]  Gültekin Özdemir,et al.  A hierarchical soft computing model for parameter estimation of curve fitting problems , 2018, Soft Computing.

[4]  Madeleine Gibescu,et al.  Deep learning for estimating building energy consumption , 2016 .

[5]  Benjamin C. M. Fung,et al.  A decision tree method for building energy demand modeling , 2010 .

[6]  Fast Simulation Platform for Retrofitting Measures in Residential Heating , 2018, Springer Proceedings in Energy.

[7]  Drury B. Crawley,et al.  EnergyPlus: Energy simulation program , 2000 .

[8]  David R. Riley,et al.  Multi-linear Regression Models to Predict the Annual Energy Consumption of an Office Building with Different Shapes , 2015 .

[9]  Zhiqiang John Zhai,et al.  Review on stochastic modeling methods for building stock energy prediction , 2017 .

[10]  Ali Razban,et al.  ARC algorithm: A novel approach to forecast and manage daily electrical maximum demand , 2018 .

[11]  Erdal Aydemir,et al.  Breeder hybrid algorithm approach for natural gas demand forecasting model , 2017 .

[12]  J. McCall,et al.  Genetic algorithms for modelling and optimisation , 2005 .

[13]  Elie Azar,et al.  Evaluation of tree-based ensemble learning algorithms for building energy performance estimation , 2018 .

[14]  Kumar Saurav,et al.  Gray-Box Approach for Thermal Modelling of Buildings for Applications in District Heating and Cooling Networks , 2017, e-Energy.

[15]  K. Lomas,et al.  Automated dynamic thermal simulation of houses and housing stocks using readily available reduced data , 2019, Energy and Buildings.

[16]  Riccardo Bonetto,et al.  Machine Learning Approaches to Energy Consumption Forecasting in Households , 2017, ArXiv.

[17]  Martin Heine Kristensen,et al.  Bottom-up modelling methodology for urban-scale analysis of residential space heating demand response , 2019, Applied Energy.

[18]  Rita Streblow,et al.  Development and validation of grey-box models for forecasting the thermal response of occupied buildings , 2016 .

[19]  A Aleksandra Sretenovic,et al.  Support vector machine for the prediction of heating energy use , 2018 .

[20]  Rizwan Ahmad,et al.  Intelligent techniques for forecasting electricity consumption of buildings , 2018, Energy.

[21]  Lianhui Li,et al.  A VVWBO-BVO-based GM (1,1) and its parameter optimization by GRA-IGSA integration algorithm for annual power load forecasting , 2018, PloS one.

[22]  Sousso Kelouwani,et al.  Comparison and Simulation of Building Thermal Models for Effective Energy Management , 2015 .

[23]  Elisa Guelpa,et al.  Demand side management in district heating networks: A real application , 2019, Energy.

[24]  Fabian Ochs,et al.  The Reference Framework for System Simulations of the IEA SHC Task 44 / HPP Annex 38 Part B: Buildings and Space Heat Load , 2014 .

[25]  Melvin Robinson,et al.  Prediction of residential building energy consumption: A neural network approach , 2016 .

[26]  Timothy J McCarthy,et al.  Linear regression models for prediction of annual heating and cooling demand in representative Australian residential dwellings , 2017 .

[27]  Ruth Kerrigan,et al.  Data Driven Approaches for Prediction of Building Energy Consumption at Urban Level , 2015 .

[28]  Elisa Guelpa,et al.  Compact physical model for simulation of thermal networks , 2019, Energy.

[29]  Antonio F. Gómez-Skarmeta,et al.  Data driven modeling for energy consumption prediction in smart buildings , 2017, 2017 IEEE International Conference on Big Data (Big Data).

[30]  Lee-Ing Tong,et al.  Forecasting energy consumption using a grey model improved by incorporating genetic programming , 2011 .