A machine-learning-based approach to predict residential annual space heating and cooling loads considering occupant behaviour

Abstract Energy consumption for space heating and cooling typically accounts for more than 40% of residential household energy consumption. An accurate and fast prediction of space heating and cooling loads aids energy conservation and carbon emission reduction by relieving the simulation burden for optimisation design, which consider various building characteristics combinations. This study aims to develop machine learning based load prediction model for residential building, five machine-learning models have been utilised for the prediction of residential building space heating and cooling load intensities, with occupant behaviour innovatively accounted as predictor variable. Their prediction performances are compared with each other. The five machine-learning models used in this study are linear kernel support vector regression, polynomial kernel support vector regression, Gaussian radial basis function kernel support vector regression, linear regression, and artificial neural networks. The results indicate that the Gaussian radial basis function kernel support vector regression is the best-performing model, with training time of less than 35s as well as less than 4% normalised mean absolute error and normalised root-mean-square error for both cooling and heating load prediction. The sample size of training and validation set for Gaussian radial basis function kernel support vector regression model is suggested as 200 samples. A data-driven machine-learning-based prediction model is an alternative to complex simulation tools in aiding the decision making of both building design and retrofit processes.

[1]  Álvaro Herrero,et al.  Testing and Validation , 2011 .

[2]  Jiejin Cai,et al.  Applying support vector machine to predict hourly cooling load in the building , 2009 .

[3]  Yuhong Yang,et al.  Cross-validation for selecting a model selection procedure , 2015 .

[4]  Max Kuhn,et al.  Applied Predictive Modeling , 2013 .

[5]  Jack Chin Pang Cheng,et al.  Identifying the influential features on the regional energy use intensity of residential buildings based on Random Forests , 2016 .

[6]  Jack Chin Pang Cheng,et al.  Estimation of the building energy use intensity in the urban scale by integrating GIS and big data technology , 2016 .

[7]  Antonio Paone,et al.  The Impact of Building Occupant Behavior on Energy Efficiency and Methods to Influence It: A Review of the State of the Art , 2018 .

[8]  Song Yang,et al.  Comparative Study on Machine Learning for Urban Building Energy Analysis , 2015 .

[9]  Kevin M. Smith,et al.  Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy , 2014 .

[10]  Tengfang T. Xu,et al.  Cool roofs in China: Policy review, building simulations, and proof-of-concept experiments , 2014 .

[11]  Tanveer Ahmad,et al.  A comprehensive overview on the data driven and large scale based approaches for forecasting of building energy demand: A review , 2018 .

[12]  Tiberiu Catalina,et al.  Multiple regression model for fast prediction of the heating energy demand , 2013 .

[13]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[14]  Lim Chin Haw,et al.  The effect of building envelope on the thermal comfort and energy saving for high-rise buildings in hot–humid climate , 2016 .

[15]  Xiaohui Zhou,et al.  Adaptive learning based data-driven models for predicting hourly building energy use , 2018 .

[16]  Paul Raftery,et al.  A review of methods to match building energy simulation models to measured data , 2014 .

[17]  Shahaboddin Shamshirband,et al.  Estimating building energy consumption using extreme learning machine method , 2016 .

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

[19]  Ron Kohavi,et al.  A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.

[20]  Jiejin Cai,et al.  Predicting hourly cooling load in the building: A comparison of support vector machine and different artificial neural networks , 2009 .

[21]  Amarjeet Singh,et al.  I-BLEND, a campus-scale commercial and residential buildings electrical energy dataset , 2019, Scientific Data.

[22]  Pedro J. Mago,et al.  Building hourly thermal load prediction using an indexed ARX model , 2012 .

[23]  Dominic T. J. O'Sullivan,et al.  The suitability of machine learning to minimise uncertainty in the measurement and verification of energy savings , 2018 .

[24]  Tong Qiu,et al.  Analysis of climate adaptive energy-saving technology approaches to residential building envelope in Shanghai , 2018, Journal of Building Engineering.

[25]  Kadir Kavaklioglu,et al.  Robust modeling of heating and cooling loads using partial least squares towards efficient residential building design , 2018, Journal of Building Engineering.

[26]  Angela Lee,et al.  The impact of occupants’ behaviours on building energy analysis: A research review , 2017 .

[27]  Kurt Hornik,et al.  Support Vector Machines in R , 2006 .

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

[29]  Borong Lin,et al.  Residential heating energy consumption modeling through a bottom-up approach for China's Hot Summer–Cold Winter climatic region , 2015 .

[30]  Di Wu,et al.  Machine Learning for Building Energy and Indoor Environment: A Perspective , 2017, ArXiv.

[31]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[32]  Phuong H. Nguyen,et al.  A relevant data selection method for energy consumption prediction of low energy building based on support vector machine , 2017 .

[33]  Afonso C. C. Lemonge,et al.  Comparison of machine learning techniques for predicting energy loads in buildings , 2017 .

[34]  Yu-Wei David Chiu,et al.  Machine Learning with R Cookbook , 2015 .

[35]  B. Dong,et al.  A survey on energy consumption and energy usage behavior of households and residential building in urban China , 2017 .

[36]  Matthew J. Eckelman,et al.  Predictive modeling for US commercial building energy use: A comparison of existing statistical and machine learning algorithms using CBECS microdata , 2018 .

[37]  Marilyn A. Brown,et al.  Machine learning approaches for estimating commercial building energy consumption , 2017 .

[38]  Frédéric Magoulès,et al.  Parallel Support Vector Machines Applied to the Prediction of Multiple Buildings Energy Consumption , 2010 .

[39]  Jui-Sheng Chou,et al.  Modeling heating and cooling loads by artificial intelligence for energy-efficient building design , 2014 .

[40]  Lynne E. Parker,et al.  Energy and Buildings , 2012 .

[41]  Yong Shi,et al.  A review of data-driven approaches for prediction and classification of building energy consumption , 2018 .

[42]  Farshad Kheiri,et al.  A review on optimization methods applied in energy-efficient building geometry and envelope design , 2018, Renewable and Sustainable Energy Reviews.

[43]  Yixiang Wang,et al.  A new calculation method for shape coefficient of residential building using Google Earth , 2014 .

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

[45]  Max Kuhn,et al.  The caret Package , 2007 .

[46]  Arno Schlueter,et al.  A review on occupant behavior in urban building energy models , 2018, Energy and Buildings.

[47]  B. Dong,et al.  Applying support vector machines to predict building energy consumption in tropical region , 2005 .

[48]  Krzysztof Gajowniczek,et al.  Short Term Electricity Forecasting Using Individual Smart Meter Data , 2014, KES.

[49]  Theodoros Damoulas,et al.  Towards data-driven energy consumption forecasting of multi-family residential buildings: Feature selection via the Lasso , 2014 .

[50]  Tuğçe Kazanasmaz,et al.  Comparative study of a building energy performance software (KEP-IYTE-ESS) and ANN-based building heat load estimation , 2014 .

[51]  Fu Xiao,et al.  A short-term building cooling load prediction method using deep learning algorithms , 2017 .

[52]  Tianzhen Hong,et al.  Advances in research and applications of energy-related occupant behavior in buildings ☆ , 2016 .

[53]  Frédéric Magoulès,et al.  Vapnik's learning theory applied to energy consumption forecasts in residential buildings , 2008, Int. J. Comput. Math..

[54]  M. Liu,et al.  Low carbon heating and cooling of residential buildings in cities in the hot summer and cold winter zone - A bottom-up engineering stock modeling approach , 2019, Journal of Cleaner Production.

[55]  T. Agami Reddy,et al.  Literature review on calibration of building energy simulation programs : Uses, problems, procedures, uncertainty, and tools , 2006 .

[56]  M. Mukaka,et al.  Statistics corner: A guide to appropriate use of correlation coefficient in medical research. , 2012, Malawi medical journal : the journal of Medical Association of Malawi.

[57]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[58]  Zeyu Wang,et al.  A review of artificial intelligence based building energy use prediction: Contrasting the capabilities of single and ensemble prediction models , 2017 .

[59]  Nora El-Gohary,et al.  A review of data-driven building energy consumption prediction studies , 2018 .

[60]  Hoon Heo,et al.  Prediction of building energy consumption using an improved real coded genetic algorithm based least squares support vector machine approach , 2015 .

[61]  S. Barles,et al.  Studying construction materials flows and stock: A review , 2017 .

[62]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[63]  Joaquim Melendez,et al.  Short-term load forecasting in a non-residential building contrasting models and attributes , 2015 .

[64]  Daniela M. Witten,et al.  An Introduction to Statistical Learning: with Applications in R , 2013 .

[65]  Amaryllis Audenaert,et al.  Improving the energy performance of residential buildings: A literature review , 2015 .

[66]  Rahul Khanna,et al.  Efficient Learning Machines , 2015, Apress.

[67]  Athanasios Tsanas,et al.  Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools , 2012 .

[68]  Zhanming Chen,et al.  Characteristics of residential energy consumption in China: Findings from a household survey , 2014 .

[69]  Fan Zhang,et al.  Time series forecasting for building energy consumption using weighted Support Vector Regression with differential evolution optimization technique , 2016 .

[70]  Michael A. Gerber,et al.  EnergyPlus Energy Simulation Software , 2014 .

[71]  Robert Kabacoff.  R in Action , 2011 .