Despite the tightening of energy performance standards for buildings in various countries and the increased use of efficient and renewable energy technologies, it is clear that the sector needs to change more rapidly to meet the Net Zero Emissions (NZE) scenario by 2050. One of the problems that have been analyzed intensively in recent years is that buildings in operation use much more energy than they were designed to. This problem, known as the energy performance gap, is found in many countries and buildings and is often attributed to the poor management of building energy systems. The application of Artificial Intelligence (AI) to Building Energy Management Systems (BEMS) has untapped potential to address this problem and lead to more sustainable buildings. This paper reviews different AI-based models that have been proposed for different applications and different buildings with the intention to reduce energy consumption. It compares the performance of the different AI-based models evaluated in the reviewed papers by presenting the accuracy and error rates of model performance and identifies where the greatest potential for energy savings could be achieved, and to what extent. The review showed that offices have the greatest potential for energy savings (up to 37%) when they employ AI models for HVAC control and optimization. In residential and educational buildings, the lower intelligence of the existing BEMS results in smaller energy savings (up to 23% and 21%, respectively).

Different optimization methods generate a large variety of solutions that are suitable for achieving the sets of chosen objectives. The selection of appropriate design variables and objective functions are essential elements in the optimization process. However, the existing methodologies seem to be too complicated and designers seek to use simpli-fied methods in order to evaluate the overall performance of the buildings. The advancement of information technology enables the use of Building Information Modelling and energy simulation tools for the integrated building design in an early stage. Thus, this article introduces a new evaluation method for analysis of the effective solution of the building performance and aims at determining the influence of a complex criteria (construction duration, construction cost, annual bill-based costs, annual primary energy demand, annual CO2 emissions of energy use, CO2 emissions of construction materials and works, thermal comfort) for the final decision making. The findings of the article have confirmed that the high level of the building energy efficiency is not directly proportional to the overall investments, i.e. additional invest-ments do not bring benefits from economic and environmental points of view.

Calibration of the energy model of a building is one of the essential tasks required to determine the efficiency of building management systems, and both their own and other systems’ improvement potential. In order to make the building energy model as accurate as possible, it is necessary to collect comprehensive data on its operation and sometimes to assess the missing information. This paper represents the process of developing an energy model for an administrative building and its calibration procedure, using detailed long-term measurement and building management system (BMS) data. Indoor air temperature, CO₂ concentration, and relative humidity were experimentally measured and evaluated separately. Such dual application of data reduces the inaccuracy of the assumptions made and assesses the model’s accuracy. The DesignBuilder software developed the building model. During the development of the model, it was observed that the actual energy consumption needs to be assessed, as the assumptions made during the design about the operation and management of HVAC systems often do not coincide with the actual situation. After integrating BMS information on HVAC management into the building model, the resulting discrepancy between the model results and the actual heat consumption was 6.5%. Such a model can be further used to optimize management decisions and assess energy savings potential.

Despite stricter global energy codes, performance standards, and advanced renewable technologies, the building sector must accelerate its transition to zero carbon emissions. Many studies show that new buildings, especially non-residential ones, often fail to meet projected performance levels due to poor maintenance and management of HVAC systems. The application of predictive AI models offers a cost-effective solution to enhance the efficiency and sustainability of these systems, thereby contributing to more sustainable building operations. The study aims to enhance the control of a variable air volume (VAV) system using machine learning algorithms. A novel ventilation control model, AI-VAV, is developed using a hybrid extreme learning machine (ELM) algorithm combined with simulated annealing (SA) optimisation. The model is trained on long-term monitoring data from three office buildings, enhancing robustness and avoiding the data reliability issues seen in similar models. Sensitivity analysis reveals that accurate occupancy prediction is achieved with 8500 to 10,000 measurement steps, resulting in potential additional energy savings of up to 7.5% for the ventilation system compared to traditional VAV systems, while maintaining CO₂ concentrations below 1000 ppm, and up to 12.5% if CO₂ concentrations are slightly above 1000 ppm for 1.5% of the time.

This paper presents a systematic mapping (SM) study with the aim to determine how Building Information Modeling (BIM) methodologies and technologies contribute to energy-related analyses over the course of the entire building life cycle. The method adopted in the study is based on a set of seven research questions. We used a mixed technique combining co-citation analysis and bibliographic coupling in order to analyze the publications’ datasets for the period 2010–2020. The main advantage and novelty of this study are that the joint dataset from the Scopus and Web of Science databases was used to develop the keyword map. The main findings of this study indicate that many BIM-based applications can be used to analyze the building energy performance at all stages of the building life cycle. However, the applications of BIM in conjunction with other information technologies are limited and are still in the initial stage. In the future, the main improvements should be focused on process, model, system, tool, use and information modeling. The most promising long-term solution is an open BIM framework based on open standards, which allows the integration of BIM and energy simulation tools and satisfies specific data exchange requirements.

Air-to-air heat pumps are used in today’s ventilation systems increasingly often as they provide heating and cooling for buildings. The energy transformation modes of these units are subject to constant change due to the varying outdoor air state, including temperature and humidity. When choosing how to operate and control energy transformers, it is important to be able to adapt effectively to the changing outside air conditions. Nowadays, modern commercial heat pumps offer two levels of control flexibility: a compressor with a variable speed and an electronic expansion valve. This combination of control elements has boosted the seasonal energy efficiency of heat pumps. For a long time, cycle control possibilities have been dominated by electronic controls. The authors of this paper aim to present an additional element to the traditional heat pump controls, which provides a third level of control over the cycle. To achieve the objective, experimental investigations of a heat pump integrated into a ventilation unit have been carried out under real-life conditions. The experiments involved varying the operating modes of the unit by adjusting the compressor speed, the position of the expansion valve, and the volume of the system loop. The study examined the performance characteristics of the heat pump and found that the performance of a variable-volume heat pump is comparable to that of a conventionally operated typical constant-volume heat pump system. In addition, the study found that by adding a third level of volume control to the active heating circuit, in combination with conventional controls, the heat pump’s heat output range could be extended by 69.62%. The study determined the variation of the heat pump cycle in the p-h diagram with the variation of the loop volume. The benefits and drawbacks of a heat pump with a variable-volume loop are discussed in this study.