Global warming, climate change and the energy crisis are trending topics around the world, especially within the energy sector. The rising cost of energy, greenhouse gas (GHG) emissions and global temperatures stem from the over-reliance on fossil fuel as the major energy resource. These challenges have highlighted the need for alternative energy resources and urgent intervention strategies like energy consumption reduction and improving energy efficiency. The heating, ventilation, and air-conditioning (HVAC) system in a building accounts for about 70% of energy consumption, and a decision to reduce energy consumption may impact the indoor environmental quality (IEQ) of the building. It is important to adequately balance the tradeoff between IEQ and energy management. Artificial intelligence (AI)-based solutions are being explored for improving building energy performance without compromising IEQ. This paper systematically reviews recent studies on AI and machine learning (ML) for building energy management and IEQ by exploring common use areas, the methods or algorithms applied and the results obtained. The overall purpose of this research is to add to the existing body of work and to highlight energy-related AI applications in buildings and the related gaps. The result shows five common application areas: thermal comfort and indoor air quality (IAQ) control; energy management and energy consumption prediction; indoor temperature prediction; anomaly detection; and HVAC controls. Gaps involving policy, real-life scenario applications, and insufficient study of the visual and acoustic comfort areas are also identified. Very few studies take into consideration the need to follow IEQ standards in the selection process and positioning of sensors in AI applications for IEQ in buildings. This study reveals a need for more systematically summarized research.

The integration of renewable energy and flexible energy sources in buildings brings numerous benefits. However, the integration of new technologies has increased the complexity and despite the progress of optimization algorithms and technologies, new research challenges emerge. With the increasing availability of data and advanced modeling tools, stakeholders in the building sector are actively seeking a more comprehensive understanding of the implementation and potential benefits of energy optimization and an extensive up-to-date survey of optimization in the context of buildings and communities is missing in the literature. This study comprehensively reviews over 180 relevant publications on the management and optimization of energy flexibility resources in buildings. The primary objective was to examine and analyze prior research, with emphasis on the used methods, objectives, and scope. The method of content analysis was used to ensure a thorough examination of the existing literature on the subject. It was concluded that multi-objective optimization is crucial to enhance the utilization of flexible resources within individual buildings and communities. Moreover, the study successfully pinpointed key challenges and opportunities for future research, such as the need for accurate data, the complexity of the optimization process, and the potential trade-offs between different objectives.

Nearly-zero energy buildings are standard for new constructions in the European Union. The challenge for decarbonized cities is retrofitting the existing buildings. Retrofitting the existing buildings provides considerable opportunities for improving occupants’ comfort and reducing global energy consumption and greenhouse gas emissions. Building retrofit is being considered one of the main approaches to achieve sustainability in the built environment at relatively low-cost and high-uptake rates. Although a broad range of retrofit technologies are readily available, methods to ascertain the most suitable set of retrofit actions for specific projects are still a practical challenge. This article first aims to develop and present a simulation-based multi-objective optimization model, named RETROSIM, to quantitatively assess technology choices in a building retrofit project. RETROSIM is a combination of a building energy simulation model and RETROSIM proprietary optimization engine. Secondly, in this study, RETROSIM is used to evaluate a building retrofit project as a case study to show the functionality of the proposed method. The study initiates with the single optimization of objective functions focusing on the building’s characteristics and performance: primary energy consumption, global costs and thermal discomfort hours. Then this methodology is used to study the interaction between these conflicting objectives and evaluate their trade-offs.