Abstract
The emerging concept of smart buildings, which requires the incorporation of sensors and big data (BD) and utilizes artificial intelligence (AI), promises to usher in a new age of urban energy efficiency. By using AI technologies in smart buildings, energy consumption can be reduced through better control, improved reliability, and automation. This paper is an in-depth review of recent studies on the application of artificial intelligence (AI) technologies in smart buildings through the concept of a building management system (BMS) and demand response programs (DRPs). In addition to elaborating on the principles and applications of the AI-based modeling approaches widely used in building energy use prediction, an evaluation framework is introduced and used for assessing the recent research conducted in this field and across the major AI domains, including energy, comfort, design, and maintenance. Finally, the paper includes a discussion on the open challenges and future directions of research on the application of AI in smart buildings.
Highlights
Multiple linear regression (MLR) is a machine learning model based on statistical linear regression, and it is widely used in several studies to predict the monthly heating and cooling demands of residential buildings
This will help in balancing the energy supply and demand in smart buildings that are equipped with hybrid micro-grids
Improving the comfort level of residents is associated with the use of large amounts of energy, and this stresses the need for establishing an optimal balance between quality of life and energy savings in buildings
Summary
Artificial intelligence (AI) can be defined as a set of computerized systems that perform tasks usually associated with human beings. It reaches or exceeds human intelligence as it achieves human-like levels of perception, reasoning, interacting, and learning. Human beings have the inherent ability to learn from their environment and respond to new situations by integrating new experiences with their knowledge base. By the late 1990s, the reduced uncertainty in decision-making was achieved by statistical learning methods linked to AI This era saw the advent of fuzzy logic [5,6], an AI concept that was successfully applied in many aspects of life, from the running of washing machines to the operation of high-speed trains [7]. It is this which drives all research and innovations in this field
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