Machine Learning Theory in Building Energy Modeling and Optimization: A Bibliometric Analysis

Abstract

In recent decades, the machine learning theory has been developed in the field of artificial intelligence (AI), as it excludes all shortcomings of manpower, performs complex calculations without rest, and provides prediction benefits for projects. Machine learning models and algorithms extract natural models from the data set, which offers increased problem insight, better decisions, and more accurate predictions. Machine learning has a variety of methods, including supervised, unsupervised, and reinforcement learning, and has been used for building energy modeling in recent years. In this review paper, machine learning in building energy modeling was examined to demonstrate the publications in this area and the relationship between these topics. This paper investigated machine learning methods for building energy modeling using bibliometric analysis and data mining. Therefore, the objective of this research was to give insight into the status of machine learning uses for energy systems in the construction industry. Scientometric software was used for analysis. Deep learning is also a cutting-edge topic of machine learning in 2018 onwards, so a brief explanation in this research was provided to explore a proper connection between machine learning, deep learning, and construction energy modeling.

Statistical Building Energy Model from Data Collection, Place-Based Assessment to Sustainable Scenarios for the City of Milan

“…In Figure 9, typical tasks in the energy sectors tackled by machine learning approaches are shown, and a review is discussed in a recent paper [22]; however, it is worth noting that the field is rapidly evolving, and new techniques may emerge as better options for specific problems.…”

“…In Figure 9, typical tasks in the energy sectors tackled by machine learning proaches are shown, and a review is discussed in a recent paper [22]; however, it is w noting that the field is rapidly evolving, and new techniques may emerge as better op for specific problems. Predicting energy consumption at the urban/territorial scale with data-driven mo typically involves collecting data on a variety of factors that can impact a city's en usage, such as the age and size of buildings, the type of heating and cooling systems u the weather, and the population density.…”

Fundamentals of different wind turbines for electricity generation and their modelling methods using different algorithms

“…Only Alessandro in 2010 [120] and Jaohindy in 2013 [121] used the accelerated rotation speed for the Darrieus rotor. In this method, the torque applied to the turbine causes proportional acceleration and motion [122] . The rotor motion will be obtained by coupling the Lagrangian equations of CFD and the Euler equations of the rigid body.…”