A review and analysis of regression and machine learning models on commercial building electricity load forecasting

By Baran Yildiz, J. Bilbao, A. Sproul. 2017

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Modeling and forecasting building energy consumption: A review of data-driven techniques (2019, methodology): “Ensemble models have gained in interest for BECMF in the past few years.”
A review of the-state-of-the-art in data-driven approaches for building energy prediction (2020, methodology): “[11] showed the way to apply PCA to tackle the multicollinearity problem in original input variables, and gave a detailed description about how to determine the dimension of reduced feature space.”
A review of machine learning in building load prediction (2021, unknown): “Bilbao and Sproul concluded that dry-bulb temperature is the most pronounced climate variable used in previous regression studies; occupancy and temporal parameters are also important variables [18].”
A novel stacked generalization ensemble-based hybrid LGBM-XGB-MLP model for Short-Term Load Forecasting (2021, methodology): “LF reveals a cost-effective, ef ﬁ cient, and reliable technique within the energy management framework [4].”