Building Automation Systems Research Articles

Building performance optimization through sensitivity Analysis, and economic insights using AI

Optimizing building designs for energy efficiency and occupant comfort presents significant challenges due to the complex and often conflicting requirements of various stakeholders. Consequently, this study conducts a multifaceted sensitivity and economic impact analysis that aims to improve building performance in terms of energy efficiency and occupant comfort by implementing machine learning techniques. Using a broad dataset comprising of 12,000 energy simulation runs for Tvedestrand Upper Secondary School in Norway, several machine learning models were employed with Multi-Layer Perceptron outperforming the others. In addition, several sensitivity analysis methods were used to explore the influence of individual parameters on building performance. The analysis reveals that ventilation rate, room depth, U-value of the facade, and heat gains significantly affect energy consumption. Economic impact analysis was also carried out to compare the cost-effectiveness of traditional HVAC systems with Building Management System (BMS) HVAC solutions. The BMS HVAC system shows significantly lower operational costs over time, with investment costs averaging around 1200 Norwegian kroner (NOK)/m2 and operational costs of approximately 150 NOK/m2 per year. Sensitivity analysis under different economic scenarios highlights the economic viability of the BMS HVAC system. This study identifies optimal building parameters that balance energy efficiency and thermal comfort, achieving total energy consumption between 11.05 and 22.51 kWh/m2 and zero discomfort hours (h > 26 °C). In sum, the findings offer valuable insights for stakeholders, enabling informed decisions about sustainable building design and energy efficiency improvements, ensuring both technical soundness and financial viability under a wide range of conditions, while using the tested tools.

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.

Green Edge: Harnessing situational awareness for sustainable systems

Green computing and Building Management Systems (BMS)are closely related through their shared goal of improving energy efficiency and reducing environmental impact. Field controllers that connect with the sensors and actuators at the field level act as an interface between field devices and enterprise applications. Data transfer, protocol conversion, data processing, computing, and control actuators based on predefined logic are the functions of the field controllers. Fast response time is one of the crucial parameters of these field controllers hence reducing the load on these field controllers by enabling Edge computing functionality is very much required. Maharudrayya Nandimath and others [16] in their “Edge Computing in IBMS (Integrated Building Management Systems). (2024 International Conference on Computer Communication and Informatics (iccci. in)) the paper suggested many of the measures to reduce the load on these field controllers. Building Management Systems are essential for optimizing the operation of modern buildings, ensuring comfort, safety, and energy efficiency. By integrating advanced technologies such as Situation Awareness (SA) and Artificial Intelligence (AI), BMS can be significantly enhanced to provide real-time monitoring, predictive maintenance, and intelligent decision-making. This paper explores the potential of combining SA and AI with BMS, leveraging insights from recent research on industrial processes, IoT cloud-edge continuum, and smart cities. The proposed system architecture includes feedback loops and contextual awareness, aiming to improve building operations and reduce energy consumption, thereby supporting global sustainability goals.

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.

OPTIMIZING HVAC EFFICIENCY AND RELIABILITY: A REVIEW OF MANAGEMENT STRATEGIES FOR COMMERCIAL AND INDUSTRIAL BUILDINGS

This study presents a comprehensive review of strategies to optimize HVAC systems in commercial and industrial buildings, focusing on enhancing energy efficiency, system reliability, and environmental sustainability. HVAC systems are major energy consumers, contributing significantly to operational costs in large buildings. With increasing energy costs, regulatory pressures, and the push for sustainability, technological advancements and management strategies have emerged to improve HVAC performance. Following PRISMA guidelines, this review analyzed 58 high-quality peer-reviewed studies published between 2010 and 2023. Key findings show that energy management systems (EMS) can reduce energy consumption by up to 30%, while Building Management Systems (BMS) enhance system reliability through centralized control and predictive maintenance. The adoption of eco-friendly refrigerants and energy-efficient designs, such as heat recovery and variable refrigerant flow (VRF) technologies, further lowers energy usage and environmental impact. Additionally, integrating renewable energy sources like solar and geothermal into HVAC systems can reduce energy consumption by as much as 40%. Green building certifications, such as LEED and BREEAM, drive the adoption of optimized HVAC technologies, delivering both environmental and financial benefits. This review underscores the crucial role of HVAC optimization in reducing energy consumption, lowering carbon footprints, and improving the operational performance of buildings, offering valuable insights for building managers, engineers, and policymakers.

A Framework for Industry 4.0 Related Green Concept Integration of Services Component in Commercial Buildings

The effects of global warming are far-reaching and can affect and threaten every aspect of human existence. Human activities such as burning fossil fuels and deforestation have mainly contributed to the emission of these greenhouse gases into the atmosphere. Construction activities and practices are one such human activity. Building services are the aspects of a building that ensure the occupants are comfortable and secure within the building. However, building services use resources such as energy and water to create such comfort. The 4.0 era of industry has introduced dimensions to Green Building Concepts and practices of creating structures and processes that are environmentally friendly, responsible and resource efficient. This research, therefore, seeks to develop a framework for the integration of Industry 4.0-related green concepts into services in commercial buildings. A cross-sectional survey design was adopted in this research to provide information concerning integrating green concepts into building services in commercial buildings in Nigeria. Data were collected with questionnaires from 106 built environment professionals who also use commercial buildings in the study area of Abuja. Statistical tools for frequency, percentage, mean score, relative important index (RII), independent samples t test, Mann–Whitney U test and the factor reduction method based on eigen values were used to analyze the data. The results indicated that quality indoor air conditioning is the most critical satisfaction parameter for users within commercial buildings, with an average mean score of 3.81. The aesthetic effect of installed services on the building façade and high-quality building services components was ranked high, with an average mean score of 3.33 for each. The results also indicated that the lack of relevant technology and inadequate training of service personnel hindered the growth of green building concepts in Nigeria. These factors had a mean score of 4.35 each. Professionals ranked energy efficient/saving bulbs, e.g., CFLs, remote controlled, sensored lights, natural daylight, solar photovoltaic panels and building management systems, as the most effective green components that can be incorporated into commercial buildings. These factors and others were combined to create a framework for integrating green concepts for services into commercial buildings. When the construction industry and government in Nigeria adopt this framework, it can promote more integration of green concepts into commercial building services.

Estimating Air Change Rate in Mechanically Ventilated Classrooms Using a Single CO2 Sensor and Automated Data Segmentation.

With a growing emphasis on indoor air quality (IAQ) in educational environments, CO2 monitoring in classrooms has become commonplace. CO2 data can be used to estimate outdoor air change rate (ACH) based on the mass balance principle, which can be further linked to human health, performance, and building energy consumption. This study used a novel machine learning method to automatically segment CO2 concentration time series data into build-up, equilibrium, and decay periods, and then estimated classroom ACH using the corresponding CO2 mass balance equations. This method, applied to 40 classrooms in two mechanically ventilated K-6 schools, generated up to ten ACH estimates per day per classroom. A comparison with ACH calculated using the mechanical ventilation rates with 100% outdoor air reported by the building automation system during the study period reveals a slight underestimation by the decay and build-up methods, while the equilibrium method produced closer estimates. These differences may be attributed to uncertainties in occupancy, activity, CO2 emission rates, and air mixing. This research underscores the potential of leveraging CO2 data for more comprehensive IAQ assessments and highlights the challenges associated with accurately estimating ACH in real-world settings.

OPTIMIZING ENERGY EFFICIENCY: A COMPREHENSIVE ANALYSIS OF BUILDING DESIGN PARAMETERS

This study explores the critical role of financial incentives, energy performance certifications, government policies, and advanced technologies in driving the adoption of energy-efficient building practices. Financial incentives, such as tax credits, rebates, and grants, are shown to significantly reduce the upfront costs associated with energy-efficient technologies, making these innovations more accessible to developers and building owners. The research highlights the effectiveness of energy performance certifications, like LEED and Energy Star, in enhancing the market value and energy performance of buildings by providing a structured framework for evaluating sustainability. Government policies and building codes, including the International Energy Conservation Code (IECC) and ASHRAE standards, are identified as key drivers of energy efficiency, compelling developers to meet stringent performance standards. Additionally, the integration of renewable energy technologies, such as solar panels and geothermal heat pumps, alongside smart building automation systems, plays a vital role in reducing energy consumption and improving operational efficiency. However, regional disparities in the availability of financial incentives and enforcement of policies present challenges to widespread adoption. The study concludes that expanding financial support, strengthening policy enforcement, and increasing public and industry awareness of the long-term financial and environmental benefits of energy-efficient buildings are essential for accelerating the transition to sustainable building practices. By addressing these challenges, the construction industry can make significant strides toward reducing energy consumption, lowering greenhouse gas emissions, and achieving global sustainability goals.

A-177 Using the Building Research Establishment Environmental Assessment Method to maximise the Environmental Social Governance benefits of building a pathology hub laboratory

Abstract Background Organisations focusing on Environmental Social Governance (ESG) factors do not just benefit society and the planet, they also enjoy better performance across key profitability metrics, driven by higher growth and productivity, lower operational costs and enhanced return on investment. SYNLAB’s new Synnovis Pathology Hub, built in partnership with the National Health Service (NHS), is a high-tech purpose-built laboratory building with construction completing in Spring 2024. It will serve more than 1.7 million people in the southeast London region delivering both routine and special diagnostics in support of a 15-year NHS pathology contract. The redevelopment of the site presented an opportunity to closely align SYNLAB company strategy with the delivery of its ESG ambitions, as well as achieve financial and operational sustainability. Methods BREEAM (Building Research Establishment Environmental Assessment Method) is a standardised framework within which SYNLAB could collaborate with the building landlord and Hub delivery partners. BREEAM assessment categories are: energy, land use and ecology, water, health and wellbeing, pollution, transport, materials, waste and management. Applying the intent of SYNLAB’s ESG strategy to the hub build programme, setting this expectation with stakeholders, and using BREEAM as a platform on which to track and report ESG benefits ensured ESG and related deliverables were high on the agenda to be captured and delivered. Results The hub is on track to achieve the BREEAM Excellent rating which means it will rank within the top 10% of commercial buildings in the UK in terms of ESG benefits. Contributors to this achievement include: Recycling over 85% of materials during construction, saving more than 500 tonnes of materials from going to waste; Incorporation of changing rooms and 56 cycle rack spaces to encourage healthy and sustainable travel, helping to keep London’s air clean and reduce CO2e emissions; An Energy Management System within the Building Management System providing detailed area by area analysis and control. Along with water metering per floor, this helps SYNLAB to understand usage and focus process improvements; An energy efficient air handling system, a key area of opportunity for laboratory energy efficiency; Conformity with EU Taxonomy for Sustainable Activities, for example by limiting the flow rate of water to prevent wastage; Reuse of materials within the building, for example by relocating seating, private call areas, glass walls; Reuse of materials externally through donations to charitable organisations, for example televisions and fridges were given to a local care home, improving the residents’ quality of life; Working with local schools and wider the community. Conclusions Incorporating BREEAM ESG criteria from the early stages of planning in construction projects provide a higher likelihood of stakeholder buy-in, adoption and delivery. Pathology networks are considered key in delivering the national ambitions set out within the Long-Term Plan for the NHS and there is increased government and social pressure driving healthcare businesses to adopt ESG. There is the opportunity within Pathology networking and Pathology new builds to harness these benefits from the outset and lay the long term foundations for ESG within healthcare for future sustainability.

Optimizing Sustainable Urban Mobility: Integration Of Electric Vehicle Charging Infrastructure Into Smart Buildings

The integration of electric vehicle (EV) charging infrastructure into smart buildings is a crucial step toward enhancing sustainable urban mobility. This paper explores the benefits, challenges, strategies, and best practices associated with this integration, with a focus on optimizing EV charging accessibility and promoting a cleaner transportation ecosystem. Traditional vehicles powered by fossil fuels contribute significantly to pollution and greenhouse gas emissions, highlighting the urgent need for sustainable alternatives. EVs offer reduced emissions, lower operating costs, and potential energy independence through renewable sources, making them a promising solution for urban mobility challenges. Despite these advantages, the widespread adoption of EVs faces hurdles, particularly regarding charging infrastructure availability and accessibility in urban areas. Range anxiety among EV users due to insufficient charging stations is a significant concern hindering the transition to electric transportation. The paper emphasizes strategic placement of charging stations, integration with renewable energy sources like solar panels, and utilization of smart grid technologies to optimize EV charging operations. Case studies such as Tesla Gigafactory Shanghai and Googleplex showcase successful implementations, demonstrating scalability, user-centric design, and seamless integration with building management systems. Key challenges such as funding constraints, technical interoperability, and grid integration are addressed, proposing solutions like innovative financing models, standardization efforts, and advanced grid management technologies. By addressing these challenges and implementing sustainable solutions, the integration of EV charging infrastructure into smart buildings can significantly contribute to reducing environmental impact, promoting cleaner transportation options, and fostering a more sustainable and resilient urban environment

House FIRSTLIFE in student contest and in Living Lab

Abstract The paper comments on selected measurements and observations related to the FIRSTLIFE house demonstration unit, which was prepared by the team of the Czech Technical University for the international competition Solar Decathlon Europe 21/22 with its final at the Solar Campus in Wuppertal, Germany. The paper comments on the measurement results obtained during the competition in the field of building physics (thermal and moisture comfort, measurement of air permeability, measurement of sound insulation of the perimeter wall and dynamic co-heating test). The second part of the paper describes selected follow-up activities. For the purposes of the Living Lab, eight of the competition houses remained on the Solar Campus for at least another 3 years. This allowed long-term monitoring and targeted experiments to be carried out and some of the earlier measurements to be repeated under different climatic boundary conditions. For this phase, additional sensors were added in the Czech house, such as a meteorological station and temperature and heat flow sensors in the perimeter wall. Part of the data comes directly from the building management system. The paper comments on first results from Living Lab period.

Использование цифровых технологий в управлении эксплуатацией зданий и сооружений

In the article, the authors discuss the potential of digital transformation in the operation of buildings and structures. They propose a shift from a reactive to a preventive approach, which could reduce costs and improve efficiency during the service life of the buildings. To achieve this, they suggest using modern information technologies for detecting defects and controlling their spread. The article emphasizes the importance of collecting real-time data about buildings using digital twins of buildings. This information is collected from information models and sensors installed at the site. Instead of conducting regular inspections, it is suggested to use photo and video systems with artificial intelligence to detect defects in individual building elements. Artificial intelligence can also analyze all this data and predict the future condition of the building. Analyzing data from sensors and identifying defects based on survey information are two different tasks that require different types of neural networks. The training of these networks can be done using different methods, depending on the specific needs of the task. Based on this information, the authors propose a unified building management system that uses digital twins and artificial intelligence for various purposes. A key part of this system is continuous monitoring of building conditions and making predictions about future events. In the conclusion, the authors discuss the impact of their proposed system on building operations and structures. They assess how it can improve efficiency and reduce maintenance costs.

Innovation Ecosystems and Green Building Techniques for a Sustainable Future: Leveraging Advanced Technologies

The global movement towards environmental stewardship and sustainability largely depends on innovation ecosystems, green building techniques, and sustainable development. This study explores their future potential, focusing on how advanced technologies, eco-friendly materials, and sustainable practices could mitigate climate change and enhance environmental quality. Innovation ecosystems are crucial for advancing green technology, circular economy principles, and sustainable urban design by fostering collaboration among corporations, governments, academic institutions, and civil society. The study also investigates how the effectiveness and impact of these ecosystems can be enhanced through digital transformation technologies such as blockchain, the Internet of Things (IoT), and artificial intelligence (AI). Employing green building techniques is essential for reducing a building’s environmental impact throughout its lifecycle. This study highlights recent advancements in green technology, including sophisticated building management systems, energy-efficient HVAC systems, improved insulation materials, and the integration of renewable energy sources. The establishment of sustainability standards and the promotion of environmentally friendly practices rely heavily on certifications like Leadership in Energy and Environmental Design (LEED) and the Building Research Establishment Environmental Assessment Method (BREEAM). The paper addresses the necessity of integrated solutions to balance environmental, social, and economic dimensions for sustainability. It also examines the role of regulations, the promotion of sustainable culture through public awareness and education, and innovative concepts such as net-zero energy buildings and biophilic design. By leveraging these advancements and concepts, we can pave the way for a more environmentally sensitive and sustainable future.

Investigation of Building Automation Systems in Terms of Lighting Efficiency

Considering that energy consumption is constantly increasing along with a gradual decrease in energy resource reserves, energy is the most current and pending issue in today's world. Several academic studies have been conducted on the efficient use of energy resources. For this purpose, the idea of "smart building" has been proposed to use energy more efficiently in buildings. With building automation systems, which are indispensable elements of these smart buildings, the energy consumption of buildings can be greatly reduced. In this study, the lighting efficiency of a building automation system built at a training point in the Marmara University Mehmet Genç campus, Vocational School of Technical Sciences, was examined. The Sauter Vision Center software program was used at the observation center of the system. The results were analyzed in terms of efficiency by comparing the energy values in certain time periods obtained from the program with the energy values calculated in full use. The analysis yielded a lighting efficiency value of 54%, which was found to exceed the values reported in the literature.

A novel model to detect abnormal thermal energy consumption in existing buildings with limited metering infrastructure

In EU, excessive energy waste occurs in existing buildings due to multiple reasons, such as inadequate building design, frequent operation of the technical building systems far from the design and reference conditions, lack of proper maintenance, lack of building automation and control systems, unexpected or exceptional weather conditions and inappropriate end-user behaviours. In this context, the detection of “abnormal” energy consumption is crucial both to increase end-users’ awareness and to help energy managers and technicians in the energy diagnosis. In this paper, the authors present a novel model to detect abnormal thermal energy consumption in existing buildings equipped with limited metering infrastructure. Four heating seasons of daily thermal energy consumption and indoor environmental data of a case study building located in Central Italy were analysed to develop, train and test the model for data processing, benchmarking, abnormal energy consumption identification and diagnosis. The results of the test phase show the ability of the model to detect behavioural faults, such as the low consumption and indoor air temperature in two dwellings and the high ones in one other. Also, the poor thermal insulation properties of the building were clearly highlighted by the proposed model.

Next-generation data center energy management: a data-driven decision-making framework

In the era of society’s ongoing digitization and the exponential growth in data volume, alongside a growing energy demand, energy management plays an integral role in data centers (DCs) and is a key factor in the quest for decarbonization. In light of the complex nature of DCs, traditional energy management strategies are inadequate. This research introduces a data-driven decision-making framework for DCs, grounded in the OODA (Observation, Orientation, Decision, and Action) loop and based on insights from an Ericsson-operated DC in Linköping, Sweden. The developed framework enables DCs to enhance energy efficiency effectively. Rooted in the OODA loop and leveraging extensive datasets from DCs’ building management systems, this framework aids in decreasing cooling energy usage through strategic, data-driven decision-making. By adopting AI methods, specifically K-means clustering in this research, for continuous monitoring and fine-tuning (Proportional, Integral, Derivative) PID parameters, the framework aids in improving operational efficiency.

Thermal comfort benefits, energy efficiency, and occupant regulation behaviour in four types of personal heating within the built environment

The conventional approach to creating a uniform and stable thermal environment cannot guarantee the required thermal satisfaction rate and consumes significant energy. Personal comfort systems (PCS) offer a promising solution for office buildings. However, practical guidelines for selecting and operating PCS based on current studies remain elusive. In this study, 28 male participants were exposed to three cold conditions (16, 18, and 20 °C) and separately used four personal heating devices (PHDs): a warm air blower (WB), heated table mat (HM), heated cushion (HC), and leg warmer (LW). Variations in the subjective perception, skin temperature, heating setting adjustments, and operating parameters were monitored. The best improvements in overall thermal sensation under 16, 18, and 20 °C were achieved by HC (ΔOTSV = 0.97), WB (ΔOTSV = 0.85), and HM (ΔOTSV = 0.57), respectively. The energy consumption required to achieve the same thermal comfort benefit differed among the four PHDs. The corrective energy and power of WB, LW, HM, and HC were 64.0 W/K, 42.0 W/K, 14.0 W/K, and 7.9 W/K at 16 °C, respectively. The relationships between the overall thermal benefits of four devices and operating parameters were established to obtain the lower thresholds and suggested values of the heating parameters under different cold conditions. Advice on the automatic control for PHDs was proposed, including the limited air temperature for activation authority, activation priority order in the multi-device scenario, and initial heating settings. Integrating automated PCS with existing building management systems can achieve microenvironmental management, enhancing comfort and energy conservation.

Automated Room-Level Localisation Using Building Plan Information.

Building Management Systems (BMSs) are transitioning from utilising wired installations to wireless Internet of Things (IoT) sensors and actuators. This shift introduces the requirement of robust localisation methods which can link the installed sensors to the correct Control Units (CTUs) which will facilitate continued communication. In order to lessen the installation burden on the technicians, the installation process should be made more complicated by the localisation method. We propose an automated version of the fingerprinting-based localisation method which estimates the location of sensors with room-level accuracy. This approach can be used for initialisation and maintenance of BMSs without introducing additional manual labour from the technician installing the sensors. The method is extended to two proposed localisation methods which take advantage of knowledge present in the building plan regarding the distribution of sensors in each room to estimate the location of groups of sensors at the same time. Through tests using a simulation environment based on a Bluetooth-based measurement campaign, the proposed methods showed an improved accuracy from the baseline automated fingerprinting method. The results showed an error rate of 1 in 20 sensors (if the number of sensors per room is known) or as few as 1 per 200 sensors (if a group of sensors are deployed and detected together for one room at a time).

Thermoelectric Generator Applications in Buildings: A Review

With growing concerns about building energy consumption, thermoelectric generators (TEGs) have attracted significant attention for their potential to generate clean, green, and sustainable power. This comprehensive review explores the applications of thermoelectric generators (TEGs) in building systems, focusing on recent advancements from 2013 to 2024. The study examines TEG integration in building envelopes, including façades, walls, windows, and roofs, as well as non-integrated applications for waste heat recovery and HVAC systems. Key findings highlight the potential of TEGs in energy harvesting and thermal management, with façade-integrated systems generating up to 100.0 mW/m² and hybrid LCPV/T-TEG systems achieving overall efficiencies of 57.03%. The review also identifies critical parameters affecting TEG performance, such as solar intensity, thermoelectric arm length, and PCM melting temperature. Despite promising results, challenges remain in improving overall system efficiency, cost-effectiveness, and scalability. Future research directions include developing more efficient thermoelectric materials, optimizing system designs for various climatic conditions, and exploring integration with smart building management systems. This review provides valuable insights for researchers and practitioners working towards more energy-efficient and sustainable building designs using TEG technology.

How AI and ML can help reduce energy usage and carbon emissions in buildings

Commercial buildings play a significant role in global energy consumption and carbon emissions, making their sustainability imperative. This paper explores how artificial intelligence (AI) and machine learning (ML) technologies offer innovative solutions to reduce energy usage and carbon footprint in buildings. By leveraging AI and ML algorithms, building automation systems can optimise energy consumption, enhance occupant comfort and predict future energy demands. The integration of AI and ML into existing building controls requires managing and integrating diverse data sources, followed by cloud-based processing for advanced analytics. The benefits include increased energy efficiency, reduced utility costs and improved occupant experience. Readers will gain insights into: 1) understanding AI and ML’s role in optimising energy usage and reducing carbon emissions in buildings; 2) integrating AI and ML into building automation systems for predictive energy management; 3) overcoming challenges in data integration and cybersecurity for effective implementation; 4) leveraging AI-driven analytics for real-time monitoring and decision making; and 5) achieving sustainability goals and enhancing building resilience through AI-powered solutions. By implementing AI and ML technologies, building owners and operators can not only improve energy efficiency but also strengthen their competitive edge, attract environmentally conscious tenants and contribute to global sustainability efforts.