Machine learning approaches for estimating commercial building energy consumption
| Autor: | Marilyn A. Brown, Bistra Dilkina, Ram M. Pendyala, Caleb Robinson, Subhrajit Guhathakurta, Jeffrey Hubbs, Wenwen Zhang |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Consumption (economics)
Engineering business.industry 020209 energy Mechanical Engineering Distribution (economics) Regression analysis 02 engineering and technology Building and Construction Energy consumption 010501 environmental sciences Management Monitoring Policy and Law Machine learning computer.software_genre 01 natural sciences Metropolitan area Energy accounting General Energy 0202 electrical engineering electronic engineering information engineering Artificial intelligence Gradient boosting business computer Energy (signal processing) 0105 earth and related environmental sciences |
| Zdroj: | Applied Energy. 208:889-904 |
| ISSN: | 0306-2619 |
| DOI: | 10.1016/j.apenergy.2017.09.060 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect