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.

Air pollution is a major public health problem. A significant number of epidemiological studies have found a correlation between air quality and a wide variety of adverse health impacts emphasizing a considerable role of air pollution in the disease burden in the general population ranging from subclinical effects to premature death. Health risk assessment of air quality can play a key role at individual and global health promotion and disease prevention levels. The Air Pollution Health Risk Assessment (AP-HRA) forecasts the expected health effect of policies impacting air quality under the various policy, environmental and socio-economic circumstances, making it a key tool for guiding public policy decisions. This paper presents the concept of AP-HRA and offers an outline for the proper conducting of AP-HRA for different scenarios, explaining in broad terms how the health hazards of air emissions and their origins are measured and how air pollution-related impacts are quantified. In this paper, seven widely used AP-HRA tools will be deeply explored, taking into account their spatial resolution, technological factors, pollutants addressed, geographical scale, quantified health effects, method of classification, and operational characteristics. Finally, a comparative analysis of the proposed tools will be conducted, using the SWOT (strengths, weaknesses, opportunities, and threats) method.

The natural and subsequent human disasters of March 11, 2011 in Japan have brought into focus more than ever the importance of resilience and risk mitigation in the construction of energy infrastructure. This article introduces some of the critical issues and discusses the implications of energy in alleviating or exacerbating the risks of natural disasters. Additionally, it presents a framework for considering the risks of energy systems from a broad perspective. The connection is drawn between design for sustainability and the risks associated with energy systems in natural disasters. As a result of the assessment, six criteria are proposed for energy systems to contribute to societal resilience in the face of natural disasters—they should be: (1) Continuous; (2) Robust; (3) Independent; (4) Controllable; (5) Non-hazardous; and (6) Matched to demand.