Machine learning approaches for estimating commercial building energy consumption. (15th December 2017) - Robinson, Caleb; Dilkina, Bistra; Hubbs, Jeffrey; Zhang, Wenwen; Guhathakurta, Subhrajit; Brown, Marilyn A.; Pendyala, Ram M.
Highlights: Machine learning models were used to estimate commercial building energy consumption. CBECS was used to train a US-wide model with five commonly available features. Validation of the model on city-specific building data was performed for New York City. The gradient boosting model performs best compared to Linear, SVM, and other methods. Availability of more building features results in more accurate models. 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.