A review of machine learning techniques for building electrical energy consumption prediction

The ongoing energy transition, essential for mitigating global warming, stands to benefit significantly from advances in building energy consumption prediction. With the rise of big data, data-driven models have become increasingly effective in forecasting, with machine learning emerging as the most efficient method for constructing these predictive models. While previous reviews have typically listed various machine learning models for energy consumption prediction, they have often lacked a theoretical perspective explaining why certain models are suitable for different aspects of this domain. In contrast, this review introduces machine learning techniques based on their application phases, covering preprocessing techniques such as feature selection, extraction, and clustering, as well as state-of-the-art predictive models. We provide a comparative theoretical analysis of various models, examining their strengths, weaknesses, and suitability for different forecasting tasks. Additionally, we discuss spatial–temporal considerations in energy consumption forecasting, including the role of Graph Neural Networks and multitask learning. Furthermore, we address a significant challenge in the field, the difficulty of accurately predicting high-fluctuation electricity consumption, and propose potential solutions to tackle this issue. • Explanation of machine learning models from theory to practical application. • Comparison of strengths and weaknesses of different models. • Covering preprocessing techniques: feature selection, extraction, and clustering. • Spatial–temporal concerns: Graph Neural Networks and Multitask learning. • Challenges in predicting high-fluctuation electricity consumption and solutions.