Machine learning approaches for estimating commercial building energy consumption

Abstract Building energy consumption makes up 40% of the total energy consumption in the United States. Given that energy consumption in buildings is influenced by aspects of urban form such as density and floor-area-ratios (FAR), understanding the distribution of energy intensities is critical for city planners. This paper presents a novel technique for estimating commercial building energy consumption from a small number of building features by training machine learning models on national data from the Commercial Buildings Energy Consumption Survey (CBECS). Our results show that gradient boosting regression models perform the best at predicting commercial building energy consumption, and can make predictions that are on average within a factor of 2 from the true energy consumption values (with an r 2 score of 0.82). We validate our models using the New York City Local Law 84 energy consumption dataset, then apply them to the city of Atlanta to create aggregate energy consumption estimates. In general, the models developed only depend on five commonly accessible building and climate features, and can therefore be applied to diverse metropolitan areas in the United States and to other countries through replication of our methodology.

[1]  Sang Hoon Lee,et al.  Commercial Building Energy Saver: An energy retrofit analysis toolkit , 2015 .

[2]  J. Friedman.  Greedy function approximation: A gradient boosting machine. , 2001 .

[3]  Kelvin K. W. Yau,et al.  Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks , 2007 .

[4]  Zhengwei Li,et al.  Methods for benchmarking building energy consumption against its past or intended performance: An overview , 2014 .

[5]  Andrew Higgins,et al.  Statistical modelling of district-level residential electricity use in NSW, Australia , 2013, Sustainability Science.

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

[7]  Wenwen Zhang,et al.  Trends in Automobile Energy use and GHG Emissions in Suburban and Inner City Neighborhoods: Lessons from Metropolitan Phoenix, USA , 2016 .

[8]  Frank Southworth,et al.  The geography of metropolitan carbon footprints , 2009 .

[9]  V. I. Hanby,et al.  UK office buildings archetypal model as methodological approach in development of regression models for predicting building energy consumption from heating and cooling demands , 2013 .

[10]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

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

[12]  Travis Walter,et al.  Getting Real with Energy Data : Using the Buildings Performance Database to Support Data-Driven Analyses and Decision-Making , 2014 .

[13]  Melek Yalcintas,et al.  An energy benchmarking model based on artificial neural network method utilizing US Commercial Buildings Energy Consumption Survey (CBECS) database , 2007 .

[14]  David Brownstone,et al.  SPECIAL REPORT 298: DRIVING AND THE BUILT ENVIRONMENT: THE EFFECTS OF COMPACT DEVELOPMENT ON MOTORIZED TRAVEL, ENERGY USE, AND CO2 EMISSIONS Key Relationships Between the Built Environment and VMT , 2008 .

[15]  Ilkka Norros,et al.  Practical lognormal framework for household energy consumption modeling , 2015 .

[16]  C. Kennedy,et al.  Comparing High and Low Residential Density: Life-Cycle Analysis of Energy Use and Greenhouse Gas Emissions , 2006 .

[17]  P. Newman,et al.  CITIES AND AUTOMOBILE DEPENDENCE: AN INTERNATIONAL SOURCEBOOK , 1989 .

[18]  Shih-Chieh Kao,et al.  High‐resolution ensemble projections of near‐term regional climate over the continental United States , 2016 .

[19]  Kara M. Kockelman,et al.  Life-cycle energy implications of different residential settings: Recognizing buildings, travel, and public infrastructure , 2014 .

[20]  M. Ha-Duong,et al.  Climate change 2014 - Mitigation of climate change , 2015 .

[21]  Wenwen Zhang,et al.  Estimating Household Travel Energy Consumption in Conjunction with a Travel Demand Forecasting Model , 2017 .

[22]  O. Keene,et al.  The log transformation is special. , 1995, Statistics in medicine.

[23]  Linda Steg,et al.  Opportunities and insights for reducing fossil fuel consumption by households and organizations , 2016, Nature Energy.

[24]  Jelena Srebric,et al.  Impact of occupancy rates on the building electricity consumption in commercial buildings , 2017 .

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

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

[27]  D. Gyalistras,et al.  Climate warming impact on degree-days and building energy demand in Switzerland , 2006 .

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

[29]  Jianlei Niu,et al.  Study on performance of energy-efficient retrofitting measures on commercial building external walls in cooling-dominant cities , 2013 .

[30]  H. Madsen,et al.  Modelling the heat consumption in district heating systems using a grey-box approach , 2006 .

[31]  Shengwei Wang,et al.  Development of prediction models for next-day building energy consumption and peak power demand using data mining techniques , 2014 .

[32]  Steve Hankey,et al.  Impacts of urban form on future US passenger-vehicle greenhouse gas emissions , 2010 .

[33]  Vijay Modi,et al.  Spatial distribution of urban building energy consumption by end use , 2012 .

[34]  Constantine Kontokosta,et al.  Predicting Building Energy Efficiency Using New York City Benchmarking Data , 2012 .

[35]  Marilyn A. Brown,et al.  Modeling climate-driven changes in U.S. buildings energy demand , 2015, Climatic Change.

[36]  Michael D. Sohn,et al.  Big-data for building energy performance: Lessons from assembling a very large national database of building energy use , 2015 .

[37]  Subhrajit Guhathakurta,et al.  Impact of Urban Form on Energy Use in Central City and Suburban Neighborhoods: Lessons from the Phoenix Metropolitan Region☆ , 2015 .