Machine learning approaches for estimating commercial building energy consumption

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State of the Art of Machine Learning Models in Energy Systems, a Systematic Review (2019, background): “ML models also provide an understanding on energy system functionality in the context of complex human interactions [22,23].”
Tackling Climate Change with Machine Learning (2019, unknown): “tial energy consumption at the neighborhood level. Using ﬁve commonly accessible features of buildings and climate, Robinson et al. predict commercial building energy use across large American cities [315]. Beyond energy prediction, buildings’ features can be used by ML algorithms to pinpoint which ”
Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks (2018, methodology): “Previous work has employed simple linear regression [12, 15, 16], multi-variate linear regression [15], non-linear regression [15, 12], support vector machines [11, 12, 17], Gaussian Process regression [18], multi-layered perceptron neural networks [19, 12, 11, 16, 17], and autoregressive neural net”
A review of the-state-of-the-art in data-driven approaches for building energy prediction (2020, methodology): “[89] utilized a gradient boosting regression model to predict annual energy consumption for different types of commercial buildings located in different regions.”