Search Results (14)

Artificial intelligence (AI) is now the computational core of smart building automation, acting across the entire cyber–physical stack. This review surveys peer-reviewed work on the integration of AI with indoor environmental quality (IEQ) and energy performance, distinguishing itself by presenting a holistic synthesis of the complete technological evolution from IoT sensors to generative AI. We uniquely frame this progression within a human-centric architecture that integrates digital twins of both the building (DT-B) and its occupants (DT-H), providing a forward-looking perspective on occupant comfort and energy management. We find that deep reinforcement learning (DRL) agents, often developed within physics-calibrated digital twins, reduce annual HVAC demand by 10–35% while maintaining an operative temperature within ±0.5 °C and CO₂ below 800 ppm. These comfort and IAQ targets are consistent with ASHRAE Standard 55 (thermal environmental conditions) and ASHRAE Standard 62.1 (ventilation for acceptable indoor air quality); keeping the operative temperature within ±0.5 °C of the setpoint and indoor CO₂ near or below ~800 ppm reflects commonly adopted control tolerances and per-person outdoor air supply objectives. Regarding energy impacts, simulation studies commonly report higher double-digit reductions, whereas real building deployments typically achieve single- to low-double-digit savings; we therefore report simulation and field results separately. Supervised learners, including gradient boosting and various neural networks, achieve 87–97% accuracy for short-term load, comfort, and fault forecasting. Furthermore, unsupervised models successfully mine large-scale telemetry for anomalies and occupancy patterns, enabling adaptive ventilation that can cut sick building complaints by 40%. Despite these gains, deployment is hindered by fragmented datasets, interoperability issues between legacy BAS and modern IoT devices, and the computer energy and privacy–security costs of large models. The key research priorities include (1) open, high-fidelity IEQ benchmarks; (2) energy-aware, on-device learning architectures; (3) privacy-preserving federated frameworks; (4) hybrid, physics-informed models to win operator trust. Addressing these challenges is pivotal for scaling AI from isolated pilots to trustworthy, human-centric building ecosystems. Full article

Recently, there has been a growing interest in issues related to mining equipment maintenance, with particular focus on pumping systems’ continuous operation. However, despite wide applications of pump system maintenance in a wide range of industries, such as water and wastewater, aviation, petrochemical, building (HVAC system), and nuclear power plant industries, the literature on maintenance of pump systems operating in the mining industry still needs development. This study aims to review the existing literature to present an up-to-date analysis of maintenance strategies for mining pumps, with a particular focus on proactive maintenance approaches. Key aspects considered include predictive diagnostics and prognosis, health status monitoring, maintenance management, and the integration of intelligent mining systems to enhance operational reliability and efficiency in harsh mining environments. The proposed methodology includes a systematic literature review with the use of the Primo multi-search tool, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The selection criteria focused on English studies published between 2005 and 2024, resulting in 88 highly relevant papers. These papers were categorized into six groups: (a) condition/health status monitoring, (b) dewatering system operation and maintenance, (c) health diagnosis and prognosis, (d) intelligent mining (modern technologies), (e) maintenance management, and (f) operational efficiency and reliability optimization. A notable strength of this study is its use of diverse scientific databases facilitated by the multi-search tool. Additionally, a bibliometric analysis was performed, showcasing the evolution of research on pump maintenance in the mining sector over the past decade and identifying key areas such as predictive diagnostics, dewatering system optimization, and intelligent maintenance management. This study highlights the varied levels of research and practical implementation across industries, emphasizing the mining sector’s unique challenges and opportunities. Significant research gaps were identified, including the need for tailored diagnostic tools, real-time monitoring systems, and cost-effective maintenance strategies specific to harsh mining environments. Future research directions are proposed, focusing on advancing predictive maintenance technologies, integrating intelligent systems, and enhancing operational efficiency and reliability. The study concludes with a detailed discussion of the findings and their implications, offering a roadmap for innovations in pump maintenance within the mining industry. Full article

With the increasingly severe problems of global climate change and resource scarcity, sustainable development has become an important issue of common concern in various industries. The construction industry is one of the main sources of global energy consumption and carbon emissions, and sustainable buildings are an effective way to address climate change and resource scarcity. Meteorological conditions are closely related to building energy efficiency. Therefore, the research is founded upon a substantial corpus of meteorological data, employing a compressed sensing reconstruction algorithm to supplement the absent meteorological data, and subsequently integrating an enhanced density peak clustering algorithm for data mining. Finally, an intelligent, sustainable, building energy-saving design platform is designed based on this. The research results show that in the case of random defects in monthly timed data that are difficult to repair, the reconstruction error of the compressed sensing reconstruction algorithm is only 0.0403, while the improved density peak clustering algorithm has the best accuracy in both synthetic and real datasets, with an average accuracy corresponding to 0.9745 and 0.8304. Finally, in the application of the intelligent, sustainable, building energy-saving design platform, various required information such as HVAC data energy-saving design parameters, cloud cover, and temperature radiation are intuitively and fully displayed. The above results indicate that the research method can effectively explore the potential valuable information of sustainable building energy-saving design, providing a reference for the design of sustainable buildings and climate analysis. Full article

Phase change thermal energy storage (PCTES) technology has garnered significant attention in addressing thermal management challenges in building HVAC systems. However, the cooling performance of PCTES systems in humid scenarios remains unexplored, which is crucial in subtropical regions, high-humidity underground areas, and densely populated spaces. Taking the mine refuge chamber (MRC) as an example, this study focuses on a passive temperature and humidity control system by employing cold storage phase change plates (PCPs) for 96 h. First, an improved and simplified full-scale numerical model including PCPs and MRC parts is established. Then, the model is validated through the experimental results and solved using a numerical method. Finally, the influence of various factors within the system is investigated and an optimization method involving batch operation is proposed. The results indicate that (1) within 40 h, the use of cold storage PCPs leads to an indoor temperature reduction of 4.8 °C and a 7% decrease in relative humidity; (2) the PCPs show asynchronous states in sensible and latent heat transfer rates; (3) for every 50 additional PCPs, the average indoor temperature increases by 0.6 °C and the relative humidity decreases by 1.5%; (4) implementing batch operation of PCPs ensures that the indoor Heat Index drops by 10 °C, which is vital for human survival. The findings will play a crucial role in the global expansion and application (including geographical and functional aspects) of phase change thermal storage technology. Full article

Indoor environmental parameters are closely related to the energy consumption and indoor thermal comfort of office buildings. Predicting these parameters, especially indoor temperature, can contribute to the management of energy consumption and thermal comfort levels in office buildings. An accurate indoor temperature prediction model is the basis for implementing this process. To this end, this paper first discusses the input and output parameters of the model, and then it compares the prediction effects of mainstream prediction model algorithms based on data mining under the same data conditions. The superiority of the XGBoost integrated learning algorithm is verified, and a further XGBoost-based indoor temperature online prediction method is designed. The effectiveness of the method is validated using actual data from a commercial office building in Haidian District, Beijing. Finally, optimization methods for the prediction method are discussed with regard to the scheduler mechanism proposed in this paper. Overall, this work can assist building operators in optimizing HVAC equipment running strategies, thus improving the indoor thermal comfort and energy efficiency of the building. Full article

Energy refurbishment of school buildings is a priority regarding both energy consumption in buildings and improving comfort conditions for sensitive young occupants. During 2016–18, a group of teachers and associates from the Faculty of Architecture, Mechanical Engineering, and Electrical Engineering from the University in Belgrade participated in the project “Energy efficiency in public buildings” in cooperation with GIZ (Deutsche Gesellschaft für Internationale Zusammenarbeit), University of Belgrade, Faculty of Architecture and Ministry of Mining and Energy of the Republic of Serbia. During 2016 and 2017, a comprehensive survey and database of public buildings were conducted by the local community. The focus of the research was the facilities of children’s institutions, and detailed data were collected to determine the current building stock conditions, energy consumption, and possible improvements. This paper presents the methodology of the project based on defining the typology of buildings, determining the representatives of the characteristic periods of construction, and analyzing their energy performance. Five possible scenarios were considered: designed condition, existing state, and three levels of a building improvement. The main goal of this project was to ascertain the entire fund for school buildings, indicate the potential for energy savings of this type of public building at the national level, and use this as a starting point for developing strategic decisions and further energy efficiency policies. This paper presents the complete results of the research on school buildings in Serbia, their energy performance, and possible energy savings. Key findings show that a great majority of schools are in a poor state in terms of their energy efficiency, but at the same time, there is a large potential for improvement of building envelope, HVAC, and lighting systems, which can cut the current energy need for heating to up to 80%. Full article

The automated fault detection and diagnostics (AFDD) of heating, ventilation, and air conditioning (HVAC) using data mining and machine learning models have recently received substantial attention from researchers and practitioners. Various models have been developed over the years for AFDD of complete HVAC or its sub-systems. However, HVAC complexities, which partly have roots in its close coupling nature and interrelated dependencies, mean that understanding the relationship between faults and the suitability of the techniques remains an unanswered question. The literature analysis and interactive visualization of the data collected from the past implementation of AFDD models can provide useful insight to further explore this question by applying artificial intelligence (AI). Association rule mining (ARM) is deployed by this paper, using the frequent pattern (FP) growth algorithm to generate frequent fault sets for most common HVAC faults from the body of AFDD models developed in the literature to represent the status quo. A new model is developed for common HVAC faults and the techniques most frequently used to detect and diagnose them. A recommender system is developed using the ARM model to extract knowledge from the body of knowledge of HVAC data-driven AFDD in the form of rule-sets that reflect the associations. Findings of this review paper can significantly help civil and building engineers, as well as facility managers, in better management of building HVAC systems. Full article

The chiller is the major energy consuming HVAC component in a building. Currently, huge chiller data is easy to obtain due to Internet of Things (IoT) technology development. In order to optimize the chiller system, this study presents a data mining technique that utilizes the available chiller data. The data mining techniques used are prediction model, clustering analysis, and association rules mining (ARM) analysis. The dataset was collected every minute for a year from a water-cooled chiller at an institutional building in Taiwan and from meteorological data. The power consumption prediction model was built using deep neural networks with 0.955 of $R^2$, 4.470 of MAE, and 6.716 of RMSE. Clustering analysis was performed using the k-means algorithm and ARM analysis was performed using Apriori algorithm. Each cluster identifies those operational parameters that have strong association rules with high performance. The operational parameters from ARM were simulated using the prediction model. The simulation result shows that the ARM operational parameters can successfully save the energy consumption by 22.36 MWh or 18.17% in a year. Full article

Mechanical Engineering (ME) includes the design, manufacturing, assembly, and maintenance of mechanical subsystems for Architecture, Engineering, Construction, and Owner-Operator (AECO) projects. The intense adoption of information and communication technology in the AECO started with building product modelling, which was originally pioneered in the ME domain (i.e., automotive industry). The complexity and limited openness of product models paved the way for Building Information Modelling (BIM). Today, BIM workflows require an exchange of interoperable architecture, structure, and MEP/HVAC models and their seamless integration into a shared BIM model. Many specialized ME systems exist (i.e., medical gases and vacuum) for which BIM is not mature enough and where the role of BIM has not yet been studied. Therefore, a comprehensive cross-disciplinary analysis on the mutual influence of the BIM and the ME domain is needed for researchers and professionals. It identifies research fields and trends at the intersection of BIM and ME and analyzes their scope, limitations, and requirements for future extensions of BIM for better integration with ME. The analysis is based on an extensive literature search considering the interdisciplinary nature of ME. The initial collection of papers has undergone a rigorous bibliometric analysis that used a text mining approach for validation. Results show the field “Industry 4.0” as the most prosperous BIM influencing research field, followed by “Energy optimisation” and “Environmental Product Declaration”, while identifying “Geometric optimisation” and “Reinforced material” as the trendiest. Finally, conclusions on the impact of BIM on ME were drawn and 11 research opportunities were identified. This paper provides directions for studies where research is focused on the integration of ME systems in BIM workflows and on the extension of BIM capability to model future ME systems. Full article

The rapid increase in the number of online resources and academic articles has created great challenges for researchers and practitioners to efficiently grasp the status quo of building energy-related research. Rather than relying on manual inspections, advanced data analytics (such as text mining) can be used to enhance the efficiency and effectiveness in literature reviews. This article proposes a text mining-based approach for the automatic identification of major research trends in the field of building energy management. In total, 5712 articles (from 1972 to 2019) are analyzed. The word2vec model is used to optimize the latent Dirichlet allocation (LDA) results, and social networks are adopted to visualize the inter-topic relationships. The results are presented using the Gephi visualization platform. Based on inter-topic relevance and topic evolutions, in-depth analysis has been conducted to reveal research trends and hot topics in the field of building energy management. The research results indicate that heating, ventilation, and air conditioning (HVAC) is one of the most essential topics. The thermal environment, indoor illumination, and residential building occupant behaviors are important factors affecting building energy consumption. In addition, building energy-saving renovations, green buildings, and intelligent buildings are research hotspots, and potential future directions. The method developed in this article serves as an effective alternative for researchers and practitioners to extract useful insights from massive text data. It provides a prototype for the automatic identification of research trends based on text mining techniques. Full article

Distributed sensor networks are at the heart of smart buildings, providing greater detail and valuable insights into their energy consumption patterns. The problem is particularly complex for older buildings retrofitted with Building Energy Management Systems (BEMS) where extracting useful knowledge from large sensor data streams without full understanding of the underlying system variables is challenging. This paper presents an application of Q-Analysis, a computationally simple topological approach for summarizing large sensor data sets and revealing useful relationships between different variables. Q-Analysis can be used to extract novel structural features called Q-vectors. The Q-vector magnitude visualizations are shown to be very effective in providing insights on macro behaviors, i.e., building floor behaviors in the present case, which are not evident from the use of unsupervised learning algorithms applied on individual terminal units. It has been shown that the building floors exhibited distinct behaviors that are dependent on the set-point distribution, but independent of the time and season of the year. Full article

Early fault detection and diagnosis in heating, ventilation and air conditioning (HVAC) systems may reduce the damage of equipment, improving the reliability and safety of smart buildings, generating social and economic benefits. Data models for fault detection and diagnosis are increasingly used for extracting knowledge in the supervisory tasks. This article proposes an autonomic cycle of data analysis tasks (ACODAT) for the supervision of the building’s HVAC systems. Data analysis tasks incorporate data mining models for extracting knowledge from the system monitoring, analyzing abnormal situations and automatically identifying and taking corrective actions. This article shows a case study of a real building’s HVAC system, for the supervision with our ACODAT, where the HVAC subsystems have been installed over the years, providing a good example of a heterogeneous facility. The proposed supervisory functionality of the HVAC system is capable of detecting deviations, such as faults or gradual increment of energy consumption in similar working conditions. The case study shows this capability of the supervisory autonomic cycle, usually a key objective for smart buildings. Full article

Targeting existing residential buildings for retrofit improvement presents significant prospects for global reduction of energy-usage and carbon footprints. Energy consumption of the existing single-family building in the hot-humid climate zone needs to be targeted for improvement due to their potential energy-savings and sizeable market share. This paper proposes and evaluates energy savings and cost-effectiveness of a whole building retrofit package for single-family residential buildings built between 1950 and 1970. The study outlined a survey conducted using the clustering data mining technique on Florida Single-Family Residential (SFR) homes to determine the essence of the building envelope, patterns of construction, and Heating, Ventilation, and Air-Conditioning (HVAC) systems. The evaluation of the energy efficiency measures (EEMs) effectiveness is performed utilizing Autodesk Revit and a Six-Step modeled framework. This framework consists of baseline model development, validation of the model with an actual case study building, identification of potential EMMs, evaluation of EEMs individually and incorporating the selected EMMs into retrofit package and maximizing the energy-saving and cost-effectiveness of the proposed retrofit package. The study develops proposed retrofit improvement package detailing replacement & improvement EEMs, implementation cost, annual energy savings (kWh), cost-saving ($), and payback period (years) for each individual EEM as well as the combined/total energy improvement package. The paper further explored the usage of solar photovoltaics (PV) energy generation options to offset the remaining energy-use after the implementation of the proposed retrofit package. Full article

Subsurface mining operations are continuously getting deeper and more complex due to depletion of shallow deposits. This fact inevitably brings more expensive, high-tech oriented and most importantly energy intensive subsurface mining operations to come alive. Accordingly, while big mining companies are developing sensible extraction methods to exploit orebodies located at great depths, they are also seeking to cut down their costs and carbon footprint. A large percentage of the energy needed by a subsurface mine is due to the mine ventilation and air conditioning reasons. In fact, for mines deeper than 2 km, mine air conditioning becomes a must. Yet, as there are not many alternatives developed, most of the modern mines are subjected to deploy tens of megawatts worth of cooling plants using massive refrigeration units. This does not only create a large financial burden during the project stage but also results in heavy energy demands during the operation. This paper aims to investigate a natural, alternative deep-mine lake cooling system by providing a detailed ‘front-end-loading’ design conducted for a real-life, Canadian example. Full article