This article delves into the realm of energy consumption prediction, highlighting the complexities and interconnectedness of various variables affecting energy usage in buildings. The authors emphasize the need for efficient modeling techniques that can handle nonlinear relationships without expending excessive time and resources. They discuss the emergence of artificial intelligence (AI) techniques as a solution, focusing on three primary methods: Genetic Programming (GP), Artificial Neural Networks (ANN), and Evolutionary Polynomial Regression (EPR). The article reviews previous literature on AI-based models for energy prediction, showcasing their applications, advantages, and limitations. The authors also explore various machine learning techniques, including deep learning models, support vector regression, and random forests, among others, to predict energy consumption in residential and non-residential buildings.
Energy Prediction Revolutionized
The data-driven method is a sound technique to handle the complexity of building energy-related applications.
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