Building Management Research Articles

Multi-Timescale Energy Consumption Management in Smart Buildings Using Hybrid Deep Artificial Neural Networks

Demand side management is a critical issue in the energy sector. Recent events such as the global energy crisis, costs, the necessity to reduce greenhouse emissions, and extreme weather conditions have increased the need for energy efficiency. Thus, accurately predicting energy consumption is one of the key steps in addressing inefficiency in energy consumption and its optimization. In this regard, accurate predictions on a daily, hourly, and minute-by-minute basis would not only minimize wastage but would also help to save costs. In this article, we propose intelligent models using ensembles of convolutional neural network (CNN), long-short-term memory (LSTM), bi-directional LSTM and gated recurrent units (GRUs) neural network models for daily, hourly, and minute-by-minute predictions of energy consumptions in smart buildings. The proposed models outperform state-of-the-art deep neural network models for predicting minute-by-minute energy consumption, with a mean square error of 0.109. The evaluated hybrid models also capture more latent trends in the data than traditional single models. The results highlight the potential of using hybrid deep learning models for improved energy efficiency management in smart buildings.

Electrically Controlled Smart Window for Seasonally Adaptive Thermal Management in Buildings.

Smart windows offer a sustainable solution for energy-efficient buildings by adapting to various weather conditions. However, the challenge lies in achieving precise control over specific sunlight bands to effectively respond to complex weather changes and individual needs. Herein, a novel electrochromic smart window fabricated by integrating a self-assembled cellulose nanocrystals (CNCs) layer with an electrochromic poly(3,4-ethylenedioxythiophene):poly(styrene sulfonic acid) (PEDOT:PSS) layer is reported, which exhibits high near-infrared transmittance, low solar reflectivity (26%), and infrared emissivity (20%) in winter, and low near-infrared transmittance, high solar reflectivity (91%), and infrared emissivity (≈94%) in summer. Interestingly, the material offers dynamic temperature control based on its photothermal properties, maintaining the internal temperature of buildings within a comfortable range of 20-25 °C from morning to night, particularly in winter with significant daily temperature fluctuations. Simulations show that the CNC-PED device demonstrates excellent energy-saving and CO2 emission reduction capacities across various global climate zones. This study presents a feasible pathway for constructing season-adaptive smart windows, making them suitable for energy-efficient buildings.

Methodology for 3D Management of University Faculties Using Integrated GIS and BIM Models: A Case Study

Three-dimensional virtual modeling is one of the tools being rapidly implemented in the construction industry, leading to the need for strategies based on intelligent 3D models of cities and/or digital twins, which allow simulation by interacting with their real physical counterparts, anticipating the outcomes of decision making. In practice, problems arise when creating and managing these twins, as different data, models, technology, and tools must be used, and they cannot all be combined as desired due to certain incompatibilities. On the other hand, today’s traditional building management demands a more optimized process to prevent errors and enable timely reactions to failures and defects. Managing and using a large amount of complex and disparate data are required, which is why the use of CMMS-type software is common (Computerized Maintenance Management System). However, such software is rarely designed for management in a 3D format, often due to the absence of three-dimensional models of the assets. This research aims to contribute to the technological development of the digitalization of the built environment, providing a simple methodology for generating and managing 3D models of cities. To achieve this, the tools and information useful for generating an integrated GIS 3D and BIM model, and for Computer-Aided Maintenance Management in a three-dimensional format (CMMS-3D), are identified. The final model obtained is used to optimize the three-dimensional management of a classroom building on the “Campus de Las Llamas” at the University of Cantabria in Spain. The results demonstrate that it is possible to integrate digital models with simple linking mechanisms between the existing tools, thus achieving an optimal three-dimensional management model.

Design of a 3D Intelligent Building Management Framework that Integrates Geographic Information Systems and Building Information Models

Design of a 3D Intelligent Building Management Framework that Integrates Geographic Information Systems and Building Information Models

Urine luck: Environmental assessment of yellow water management in buildings for urban agriculture

The increasing global demand for agricultural production poses challenges to maintain the needs for critical fertilizers such as nitrogen. This study explores the potential of human urine as a source of renewable nitrogen for fertilizer production. Through a life cycle assessment, three different urine management strategies were compared: (S1) an artificial wetland, (S2) an on-site lab-scale aerobic reactor for nitrogen recovery, and (S3) a centralized wastewater treatment plant. While scenario S2 had the highest impacts in 6 out of 8 categories, an advantage in marine eutrophication was identified. S2 showed high energy demand (750 kg MJ-eq) and ecotoxicity (602 kg 1.4-DCB-eq.) mainly due to energy requirements. Nitrogen production exceeded 2.3 times the yearly nitrogen demands of the building tomato production. Upscaling S2 reduces impacts up to 2 times, lowering the payback time from 29 to 13 years. Therefore, implementing large-scale nitrogen recovery systems in cities is encouraged.

A Comprehensive Review of Sensor-Based Smart Building Monitoring and Data Gathering Techniques

In an era where buildings are increasingly becoming multifaceted entities, the paradigm of smart buildings has witnessed significant evolution. This advancement integrates sophisticated communication technologies, the Internet of Things (IoT), artificial intelligence (AI), and data analytics. Intending to design an effective smart building monitoring system, this research paper explores and compares various solutions for measuring building parameters by identifying a broad spectrum of review articles considering building occupant behavior, sensor deployment, and implementation complexity. The objective of our paper is to compile diverse information on various sensors used for monitoring building conditions and provide a comprehensive overview of data structuring and processing, all within a single article. Additionally, this paper addresses the challenges of combining data from decentralized systems and the need for managerial tools to optimize user experiences. The findings contribute to the advancement of smart building management, offering valuable insights for improving building performance and user experience as well as evaluating future research directions in this field. This review is designed to serve as an introduction for anyone venturing into the field of building monitoring.

Occupancy and equipment usage prototype schedules for building energy simulations of office building types in China

The accuracy of occupancy and energy-consuming equipment schedules significantly influences building energy simulations. Existing standards provide generalized schedules that do not fully capture variations across different office building types and periods. This study utilizes questionnaire data from office buildings across four Chinese cities to extract refined prototypes for occupancy and equipment usage schedules using hierarchical clustering analysis. Specific schedules are developed for summer workdays, winter workdays, summer weekends, and winter weekends, covering HVAC, lighting, and office equipment. For instance, the extracted lighting schedule increases annual energy consumption intensity by 25.35% compared to standard schedules. Additionally, XGBoost models identify key factors influencing equipment usage; for HVAC, floor number emerges as most significant. The study's prototypes offer more realistic inputs for building energy simulations, enhancing accuracy and guiding energy-efficient building design and management strategies in China.

Optimizing Solar Power Generation and Integration in Automotive Systems Through IoT

Objective: This study presents an Internet of Things (IoT)- based system with edge intelligence that predicts power production with over 98% accuracy and monitors substations and smart solar installations, ensuring reliable power distribution in industrial IoT environments. This system enhances sustainability, safety, and energy management in smart buildings by reducing power fluctuations by 30% and improving decision-making, leading to a 95% reduction in energy management costs. Method: An IoT-enabled power monitoring system was implemented for smart solar panels and substations, incorporating edge intelligence for instantaneous prediction and decision-making. An IoT-enabled solar charging station was deployed for smart homes and Industry 4.0 applications. The cloud was used for analyzing sensor data, with a response time of less than 1 second. Findings: The proposed framework increased the efficiency and reliability of power production and distribution by 25% across commercial, residential, and industrial contexts. It significantly mitigated power fluctuations, reducing downtime by 40% and achieving a 95% cost reduction in energy management compared to traditional systems. IoT integration improved safety and sustainability metrics by 20% in smart buildings. Novelty: The framework integrates edge intelligence with IoT in smart solar systems and substations, providing a sophisticated control system that enhances power distribution decision-making. It facilitates real-time monitoring and prediction of power production with over 98% accuracy, emphasizing sustainability, safety, and energy management improvements of up to 30% in smart buildings. Keywords: IoT-based control system, Smart solar systems, Edge intelligence, Power substations, Load management, Energy sustainability

Energy efficient heating and ventilation of a factory hall by monitoring the indoor air climate

Background Different usage scenarios and design guidelines were considered when planning a factory building and its ventilation system. Accordingly, it often makes sense to analyze the actual operating conditions again during subsequent operations in order to optimally adapt the air supply to the respective conditions in terms of demand-responsive ventilation. The aim is to ensure good indoor air quality and thermal comfort while significantly reducing energy consumption. Methods For this purpose, in addition to the sensors in the building management system, approximately 120 wireless sensors were installed in the occupied areas to measure the air temperature, operating temperature, humidity, and CO2. In this way the spatial and temporal distributions of temperature, humidity, and CO2 in the hall were visualized and evaluated to reduce the volume flows of the ventilation systems. The recorded data and findings were used to optimize ventilation systems by gaining a deeper understanding of indoor air flow. Results As part of the investigation, a considerable reduction in the air volume flows and thus the required energy consumption of the air handling units was achieved while maintaining the same thermal comfort. It was shown that the temperature field and indoor air quality were not negatively affected by the change in the air volume flow over a wide range. Conclusions This was made possible by the well-designed ventilation system, which achieves a low-pulse, slightly stratified indoor airflow, and a structural envelope with very good thermal insulation.

Optimizing business strategies for carbon energy management in buildings: a machine learning approach in economics and management

Optimizing business strategies for carbon energy management in buildings: a machine learning approach in economics and management

Web Server Based Electrical Control System Analysis for Smart Buildings

Energy management in smart buildings still faces challenges in optimizing energy use, particularly for high-load devices, such as HVAC systems and lighting. Conventional control systems are often inadequate for optimizing energy usage based on the operational needs of the building. This study aims to develop and analyze a web server-based electrical energy control system that can be accessed in real time to improve the energy efficiency of smart buildings. This study employed a quasi-experimental method by implementing a web server-based control system in a smart building and comparing the energy consumption before and after the application of the system. The results show that the system reduces the energy consumption by up to 25%, particularly for HVAC systems and lighting. The most significant energy savings occurred during off-peak hours, when the system automatically reduced power for unnecessary devices. The implications of this research suggest that a web server-based control system not only enhances energy efficiency and reduces operational costs, but also provides greater flexibility in energy management through more adaptive and responsive remote control. This research contributes to the development of more sustainable energy management technologies for smart buildings, with wide potential applications in commercial and institutional building scenarios

Reinforcement Learning Model-Based and Model-Free Paradigms for Optimal Control Problems in Power Systems: Comprehensive Review and Future Directions

This paper reviews recent works related to applications of reinforcement learning in power system optimal control problems. Based on an extensive analysis of works in the recent literature, we attempt to better understand the gap between reinforcement learning methods that rely on complete or incomplete information about the model dynamics and data-driven reinforcement learning approaches. More specifically we ask how such models change based on the application or the algorithm, what the currently open theoretical and numerical challenges are in each of the leading applications, and which reinforcement-based control strategies will rise in the following years. The reviewed research works are divided into “model-based” methods and “model-free” methods in order to highlight the current developments and trends within each of these two groups. The optimal control problems reviewed are energy markets, grid stability and control, energy management in buildings, electrical vehicles, and energy storage.

Holistic maintenance management for higher learning institution buildings in Tanzania

PurposeThere have been claims of inadequate maintenance practice in Tanzania’s higher learning institutions (HLIs), primarily due to the traditional conventional approach where maintenance is only addressed during the usage stage. This study aimed to investigate whether universities and colleges are adopting a holistic approach to maintenance, where it is considered from the inception stage.Design/methodology/approachStructured questionnaires and semi-structured interviews were employed to gather quantitative and qualitative data, respectively. Through purposive sampling, 11 estate managers were selected, with eight of them responding to questionnaires. A comprehensive literature review was also conducted. Quantitative data were analyzed using the Statistical Package for Social Sciences (IBM SPSSTM) Version 20.0, while qualitative data were examined using Nvivo 12 Plus and in-depth analysis.FindingsThe study revealed that, maintenance in HLIs is only marginally considered in a holistic manner. In cases where it is “considered,” this is often by default rather than by design. This has led to maintenance challenges, such as the accelerated deterioration of components, the incompatibility of new maintenance materials with the old ones and difficulties in adapting spaces to meet emerging user requirements. The most frequently occurring words in the word cloud, “considered” and “maintenance”, underscore the necessity of adopting a holistic approach to maintenance management.Research limitations/implicationsThe study was confined to HLIs in the Dar es Salaam region.Practical implicationsThe findings from this study offer valuable insights for maintenance experts implementing holistic approaches in Tanzanian universities and colleges. Top management should focus on proper planning (including maintenance plans and budgets) and decision-making regarding maintenance management. Additionally, the Government of Tanzania may consider implementing policies requiring designing teams to adopt a holistic approach to maintenance before approving building permits.Originality/valueTo the best of the authors’ knowledge, this is the first study to examine whether universities and colleges consider maintenance from a holistic perspective.

Buildings as Batteries

Aggregators are increasingly using portfolios of various end-user resources such as local renewable generation, batteries, EVs, and demand-side management to participate in the electricity markets as virtual power plants. Whilst the smart control of energy within buildings is well established for cost optimization, the prospect of an aggregator collaborating with the energy managers of buildings to trade electricity in the wholesale markets is under researched. This paper uses a state-of-the-art architectural simulator to model the thermal inertia of a typical high-rise building in London and represent the thermodynamics of its hourly occupancy and weather responses. The building is simulated as a virtual battery by manipulating the HVAC settings. Price arbitrage on the electricity wholesale market and contracting with the local distribution network for flexible reserve services are both feasible and profitable. It is concluded that aggregators can apparently use buildings as additional assets within their trading portfolios. JEL Classification: L85 and L94.

Calibration of a hybrid model for HVAC systems for fault data generation

The application of automated fault detection and diagnosis algorithms has proven to be an effective way to increase the energy efficiency of buildings. While data-driven strategies have demonstrated their potential in developing such algorithms, they require a set of historical data that represents both healthy and faulty equipment operation, which is not a common scenario in real facilities. This article presents a methodology for the generation of synthetic data on both healthy and faulty operation for building heating and air conditioning equipment, which is especially useful for supervision and failure detection in the case of replication and expansion of buildings in tertiary or residential building planning programmes. This work investigates the automatic calibration of a typical air-handling unit hybrid model, composed of the detailed parametric representation of the system components and its control sequence coupled with a simplified thermal load. The model is then extended with fault models that resemble commonly encountered component faults to generate operation data in faulty scenarios. The proposed framework is validated in a real use case with the calibration of an air-handling unit system and its control sequence using 15 days of data gathered from the building management system. The results obtained from the injection of seven typical faults are then compared with the fault-free simulations to highlight and validate their impact on key operating variables. The results prove the capabilities of this methodology to support database generation in the fields of fault detection and advanced maintenance of buildings’ air conditioning systems for building replicas.

Ensino do Building Information Modeling (BIM) em Instituições de Ensino Superior no Brasil

Building Information Modeling (BIM) has emerged as a necessary tool in the architecture, engineering, construction and operations scenario, offering an innovative and integrated approach not only for project development, but also contributing to the construction management and maintenance of buildings. . In the Brazilian context, academia has dedicated special attention to BIM, reflected in the production of theses and dissertations carried out over the last few years. This research aimed to identify the main trends and pedagogical approaches used in the productions found, presenting their strengths and advances verified in the use of this methodology, as well as the weaknesses or limiting points, based on Brazilian academic production on teaching BIM in the period from 2013 to 2020, resulting from the analysis of theses and dissertations published in the CAPES Catalog of Theses and Dissertations, which address the topic of Construction Information Modeling or Building Information Modeling in the dimension of its teaching. classifies as applied, exploratory, with a qualitative approach, using the bibliographic research method and application of the content analysis technique. It is hoped that, through the results obtained, the adoption of the BIM methodology in undergraduate courses in architecture and urbanism and related areas can be encouraged in the country.

Automatic Indoor Thermal Comfort Monitoring Based on BIM and IoT Technology

Building Information Modeling (BIM) and Internet of Thing (IoT) integration technologies can improve operational efficiency in the operational phase of construction projects. Currently, research on the integration of BIM and IoT has yet to ensure secure data transmission and lacks real-time data processing capabilities. This study builds a framework to collect and analyze BIM and IoT data in real time. The framework is verified to be effective through a case study in an office building. The monitoring system can automatically calculate the Predicted Mean Vote (PMV) value, upload and update real-time temperature and humidity data, and visualize thermal comfort through heat maps. The proposed integration approach offers building management strategies to enhance thermal comfort in office environments, fostering a more inclusive and accommodating workspace that acknowledges the diverse cultural backgrounds of occupants.

Intelligent Monitoring and Visualization System for High Building Nighttime Utilization Based on Image Processing

The rise of complex high-rise buildings has made building management increasingly challenging, especially the nighttime supervision of university laboratories. Idle occupation increases the risk of accidents and undermines campus sustainability. Effective occupancy detection is essential for optimizing campus building safety and energy efficiency. Environmental sensors for occupancy detection offer limited coverage and are costly, making them unsuitable for campuses. Surveillance cameras, as part of campus infrastructure, provide wide coverage. On this basis, we designed a detection algorithm that uses light brightness to assess nighttime building use. Experimental results showed that the algorithm achieves an average accuracy of 98.67%, enabling large-scale nighttime occupancy detection without the need for installing additional sensors, significantly improving the efficiency of campus building management. In addition, to address the limitations of indoor space representation in geographic information system (GIS) management models, this paper developed a comprehensive 3D GIS model based on a “building–floor–room” hierarchical structure, utilizing oblique photogrammetry and laser scanning technology. This study combined the detection results with real-world 3D data for visualization, providing a new perspective for the 3D spatiotemporal refinement of complex high-rise buildings, and providing a reference framework for the detection and analysis of other types of building environments.

A comparative analysis of intelligent energy modelling approaches for a grid-tied PVT system application

Renewable energy resources are a long-term answer to the current chronic energy dilemma. Solar photovoltaic (PV) systems are the most often used form of a practical solution for power supply and energy management in buildings. A PV-thermal (PVT) system is essential to lowering the demand for grid energy by capturing electrical and thermal energy from sunlight, optimising energy usage and encouraging sustainable energy practices. The most significant difficulty faced by developing countries, such as South Africa, is a lack of power supply to meet demand while limiting grid overload. An alternative source of energy is a critical component. This research compares two intelligent energy modelling methodologies: optimal control scheme-based open loop and model predictive control (MPC) in a PVT application. The primary goal of the modelling is to limit the electricity required from the grid while stressing the system’s energy efficiency and cost-effectiveness. The open loop approach uses mathematical optimisation techniques to establish the best control strategy for the PVT system. The ideal set-points for various system parameters are found by presenting the problem as an optimisation task to reduce grid power usage. The optimal control technique delivers a global optimisation solution based on the system dynamics and constraints. The MPC technique employs a predictive control technique of the PVT system to create optimal control actions in a receding horizon manner. The MPC algorithm anticipates future system behaviour and generates control actions that minimise grid power drawn by iteratively solving an optimisation problem at each time step. This robust online control scheme enables real-time modifications and responses to system disruptions that effectively and dynamically coordinate system behaviour.

Foreign Trade Representative Strategy In Building The Image Of Indonesian Products In Australia

Trade relations between Indonesia and Australia have been around for a long time. This is proven by the commitment of the two countries to improve trade relations. However, in 2020 trade between the two countries experienced a significant decline due to the Covid-19 pandemic. As representatives of Indonesian trade in Australia, the trade attaché and the Indonesia Trade Promotion Center (ITPC) are crucial institutions in bridging trade between the two countries. So, this research aims to discuss the strategy of Indonesian trade representatives in Australia in building the image of Indonesian products. This research uses qualitative research methods and data collection with literature studies such as previous research, books, and official documents, the concept used is economic diplomacy in analyzing problems. The results of this research show that the strategy of Indonesian trade representatives in Australia was carried out in several stages, namely economic salesmanship, economic networking, image building, and regulatory management.