Artificial Intelligence Evolution in Smart Buildings for Energy Efficiency

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.

Day-Ahead Residential Electricity Demand Response Model Based on Deep Neural Networks for Peak Demand Reduction in the Jordanian Power Sector

“…This relationship lies at the heart of DR systems to determine the price set by utilities to achieve economic benefits in the power market and technical benefits related to the operation of the power system [19]. To capture the price elasticity of demand on 24 h under the assumption that the rescheduling of the production does not go beyond a 24-h interval, the PEDM is formulated as [19,20]:…”

Smart Campuses: Extensive Review of the Last Decade of Research and Current Challenges

“…1). For example, ranging from the smallest to the largest, literature presents concepts of so-called smart buildings ( [6], [7]), smart neighbourhoods and communities ( [8], [9]), smart cities [10], smart regions [11], and so on. In each case, using information technologies and sensors, smart systems attempt to provide innovative and constantly adapting services, make a better use of infrastructures and equipment, and promote communications and citizen engagement, among others.…”

Multi-View Data Analysis Techniques for Monitoring Smart Building Systems

“…In order to accomplish the desired functionality, these systems work collectively. Buildings in today's urban societies generate up to 40% of the total carbon dioxide emissions [1,2]. Along with helping us in our everyday activities, smart systems also play an essential role in energy-saving [1].…”

“…Buildings in today's urban societies generate up to 40% of the total carbon dioxide emissions [1,2]. Along with helping us in our everyday activities, smart systems also play an essential role in energy-saving [1]. The majority of the energy used in these systems is wasted due to operational problems [3].…”

“…There have been many studies, for example, as shown by the reviews of Farzaneh et al [1] for a wider area of smart buildings and by Mbiydzenyuy et al [4] in district heating (DH), that utilize data mining and Machine Learning (ML) to analyze and interpret this large amount of data. Data generated from the sensors have specific characteristics such as heterogeneity, streaming nature, concept drift, etc.…”