Building Systems Research Articles

Integrated Supervisory Control and Data Acquisition System for Optimized Energy Management: Leveraging Photovoltaic and Phase Change Material Thermal Storage

ABSTRACTReliable energy sources are crucial for both economic growth and quality of life. In developing countries, where expensive fuels are often the primary energy source, governments are exploring innovative solutions like small‐scale, IoT‐based projects to achieve energy independence in buildings. This research investigates the integration of renewable energy technologies, statistical modeling, cloud computing, and IoT to develop a self‐managing energy system for buildings. The system prioritizes renewable sources, specifically monocrystalline solar cells with 20% efficiency for photovoltaic (PV) energy and flat plate collectors with 90% efficiency and minimal energy loss for thermal energy. Thermal energy is stored in paraffin wax, chosen for its high storage efficiency and thermal properties. The system also utilizes an absorption chiller with a high coefficient of performance (COP) to provide cooling using solar thermal energy. The building's energy loads are categorized as A, B, C, and D, each utilizing both PV and thermal energy. A SCADA system oversees the operation, monitoring the on–off status of these loads. The system is designed for continuous operation, with simulations conducted using Anaconda Jupyter Notebook and Python. This model aims to offer a sustainable and efficient energy solution for buildings, meeting energy demands while optimizing energy use.

Optimal scheduling of green heating systems in buildings considering economy and exergy efficiency

Green heating in buildings is of great significance to global energy conservation and emission reduction, and the design of optimal scheduling models is the key to achieve cost reduction and efficiency improvement in heating systems. For the building heating system containing multiple heterogeneous energy sources such as solar energy, air energy and electricity, a multi-objective optimal scheduling model is established with the goal of maximizing the exergy efficiency and minimizing the economic cost of the system, taking into account the quality difference of energy in the conversion process. Regarding the nonconvex and nonlinear constraints in the scheduling model, the incremental linearization method and Big-M method are used to linearize the transformation, and obtain the multi-objective mixed-integer linear programming model. Further, the ε-constraint method is used to solve the Pareto frontier of multi-objective optimization and the technique for order preference by similarity to ideal solution (TOPSIS) method is used to make decision. Finally, the simulation study of the model is carried out, and the results show that the proposed multi-objective optimal scheduling can balance the system operation economy and exergy efficiency compared with the belief rule base and evidential reasoning (BRB-ER) method, and the system’s daily economic cost are reduced by 9.66 %, 9.94 % and 17.24 %, respectively, and the system’s average daily coefficient of performance (COP) are improved by 4.66 %, 14.05 % and 13.22 %, respectively, on three typical days, it is verified that the proposed optimal scheduling model has better economy and exergy efficiency. The proposed multi-objective optimal scheduling model introduces exergy efficiency rather than energy efficiency as one of the optimization objectives of the heating system and reasonably solves the optimization model, which can provide a new idea and reference for the multi-objective optimal scheduling of the green heating system in buildings.

Optimizing Building Orientation for Passive Cooling

This research paper explores the application of passive design strategies in three museums in Lagos, Nigeria: The National Museum, Jaekel House, and the Heritage Museum in Badagry. The study focuses on building orientation as a key passive design strategy and evaluates its effectiveness in enhancing energy efficiency and thermal comfort in tropical climates. With the use of data collection from Google Earth Pro, a comparative analysis examines the architectural features and design elements of each museum, highlighting their use of building orientation, shading devices, and landscaping to optimize passive cooling and natural lighting. The innovative layout and strategic orientation of the National Museum demonstrate a successful integration of passive design strategies, while Jaekel House's use of deep overhangs and vegetation showcases a deliberate commitment to sustainable architecture. The Heritage Museum in Badagry, with its historical significance, presents an intriguing case study for passive design. While limited information is available, satellite imagery suggests an optimal orientation for passive solar design, complemented by deep overhangs and a reflective roof. The findings suggest that the three museums can benefit from further enhancements to their passive design strategies, including the integration of renewable energy sources, smart building systems, green roof systems, and water harvesting and management systems. In addition, educational initiatives can raise awareness and promote sustainable practices among visitors and the local community.

The impact of AI on boosting renewable energy utilization and visual power plant efficiency in contemporary construction

The integration of Artificial Intelligence (AI) in the renewable energy sector has revolutionized the efficiency and utilization of renewable energy sources in contemporary construction, significantly impacting visual power plant operations. AI technologies, including machine learning and predictive analytics, optimize energy production, enhance system performance, and streamline maintenance processes, thereby driving the adoption and efficiency of renewable energy systems. AI-powered predictive maintenance tools analyze vast amounts of data from renewable energy installations, such as solar panels and wind turbines, to predict and prevent equipment failures. This proactive approach reduces downtime and maintenance costs, ensuring continuous and efficient energy generation. Additionally, AI algorithms optimize energy storage and distribution by accurately forecasting energy production and demand. This optimization helps balance supply and demand, reducing reliance on non-renewable energy sources and enhancing grid stability. In visual power plants, AI enhances operational efficiency through advanced monitoring and control systems. Real-time data from sensors and IoT devices are processed by AI to provide actionable insights, enabling operators to make informed decisions promptly. AI-driven automation in visual power plants ensures optimal performance by adjusting parameters in real-time, based on environmental conditions and energy demand, thus maximizing energy output. Moreover, AI facilitates the design and construction of smart buildings and infrastructure, integrating renewable energy systems seamlessly. AI-driven energy management systems in buildings analyze consumption patterns, optimize energy use, and promote energy-saving practices, contributing to overall energy efficiency. This integration not only reduces the carbon footprint of construction projects but also aligns with global sustainability goals. The impact of AI on boosting renewable energy utilization and visual power plant efficiency is profound. By leveraging AI technologies, contemporary construction can achieve higher energy efficiency, lower operational costs, and increased sustainability. As AI continues to evolve, its role in the renewable energy sector will likely expand, further enhancing the capabilities of renewable energy systems and paving the way for a more sustainable future in construction and beyond. This paper underscores the transformative potential of AI in renewable energy and its critical role in shaping the future of sustainable construction practices.

Assessment of cantilevered curtain wall system supported by tension rods for an enclosed circular building

Curtain wall systems have undergone significant advancements to meet the evolving architectural requirements and achieve aesthetic appeal and functionality in buildings worldwide. These non-structural enclosures, typically constructed with lightweight materials like glass or metal panels, contribute to the visual appeal of high-rise structures while providing numerous benefits, such as ample natural light and unobstructed views. The current paper focuses on analyzing and designing a cantilevered curtain wall system supported by tension rods for the auditorium façade at a simulation centre. The curtain wall surrounds the entire primary structure of the building, which has a circular layout. The study uses numerical modeling with the Robot software to analyze stresses and deflections in the glass, tension rods, mullions, and transoms while considering a wind load of 1.6 kPa. According to the findings, tempered laminated glass that is 19.52 mm thick satisfies the Ultimate Limit States (ULS) and Serviceability Limit States (SLS). Under the limit states, the Technal system (FM169) reinforced with steel tubes can transfer the design loads. The steel framing system that supports the tension rods and transfers the load of the curtain wall to the main structure is likewise considered safe. The structural integrity of the curtain wall is ensured by the prescribed requirements for the connections, which include a 5 mm fillet weld with a throat thickness of 3.5 mm, 20 mm diameter tension rod (Kin long system), and 8 mm thick MS plates with channel brackets. This research expands the practical application of cantilevered curtain wall systems of buildings with enclosed circular layouts.

Consideration of Thermal Comfort, Daylighting Comfort, and Life-Cycle Decarbonization in the Retrofit of Kindergarten Buildings in China: A Case Study

Kindergartens play a crucial role in nurturing the physical, cognitive, and social development of children. Hence, designing kindergarten buildings requires the consideration of the unique requirements and behavior of children. Considering the rapid urbanization of China and its commitment to achieving the 3060 carbon goal, in this study, we examine the retrofitting of kindergarten buildings in China and propose a retrofit optimization method for kindergarten buildings that considers thermal comfort, daylighting, and life-cycle carbon emissions. Through this method, information on the thermal and daylighting comfort of occupants, weather data, occupant scheduling, and envelope and energy system of the kindergarten building to be retrofitted can be obtained through various approaches, such as video playback, field investigation, literature research, and consult drawings. On this basis, optimization variables are selected, and a physical model is established to guide the retrofit process. Afterward, a rapid comprehensive optimization framework based on parallel computing is adopted to obtain the comprehensive optimal design scheme for the building to be retrofitted. The proposed method is applied to a kindergarten building retrofit case in Nanjing, China, and the results show that the optimal comprehensive scheme results in a reduction in carbon emissions of 34,158.3 kg, an increase in the thermal comfort period of 2.7%, and an improvement in daylighting comfort of 79.7% over the benchmark scheme. The significance of this study extends beyond its potential for widespread application in kindergarten building retrofits. It contributes to advancing sustainable building design and environmental stewardship, creating healthier and more comfortable learning environments for children while mitigating the environmental impact of buildings. Moreover, it emphasizes the importance of considering children’s unique needs and behaviors in building design, ultimately leading to better outcomes for their overall development.

Daylighting and energy performance optimization of anidolic ceiling systems for tropical office buildings

A simple window is often unable to ensure standard illumination to the deep parts of large office spaces, and advanced daylighting systems are necessary to ensure the level of interior illuminance. Anidolic ceiling systems are one of the less popular daylighting strategies designed to redirect and redistribute daylight deeper, which can reduce the demand for artificial lighting and energy use for lighting purposes. This study shows how a parametric approach can be applied to optimize the daylighting and energy performance of an anidolic ceiling system in a tropical climate. Multi-objective optimization (MOO) techniques were applied to combine different design variables to establish multiple design alternatives and find the optimal design by comparing their performances. A simulation study of an anidolic ceiling system was conducted with test models primarily developed by Rhinoceros and ClimateStudio, and optimization of performance metrics was conducted using Grasshopper and Octopus. Data exportation was done using the TT toolbox, while data analysis was done through mathematical and statistical analyses and cross-checked by Design Explorer. By incorporating an optimum anidolic ceiling system into the case office space, spatial daylight autonomy (sDA) increased 20.51 %∼68.18 %, annual sunlight exposure (ASE) decreased 15.83 %∼8.52 %, mean illuminance increased 221 lux–3027 lux and energy use intensity (EUI) reduced 189.3 kWh/(m2y)∼174 kWh/(m2y) for different configurations of test models. The results demonstrated a MOO methodology that successfully finds the optimum solutions for the anidolic ceiling systems in a deep plan office space to enhance daylighting and energy performance.

Ecologically Regenerative Building Systems through Exergy Efficiency: Designing for Structural Order and Ecosystem Services

Regenerative design is being increasingly explored in urban environments to counteract and adapt to the changing climate and degradation of ecosystems. A critical success factor for the implementation of regenerative design is the evaluation of urban and building systems in relation to ecological performance and benefits. In biological ecosystems, the availability of high-quality energy, called exergy, and structural order can be used as indicators of the efficiency of on-going ecological processes. Structural order refers to the organization and systematic arrangements of biotic and abiotic elements within an ecosystem based on the available space and interactions with the goal to form a functional system. Ecological processes use the available exergy and generate ecosystem services (ESs) upon which human survival and that of other living organisms depend. In this article, structural order and ESs generation are proposed as indicators for exergy efficiency and accumulation in building systems, respectively, which can evaluate to what extent they are ecologically functional and regenerative. Based on this insight, design strategies are derived from the functioning of ecosystems that describe how buildings could become habitats that host living, non-living, and hybrid systems with optimized thermodynamic efficiency and that can generate ESs. This research suggests that when buildings improve structural order (an ecological concept) and implement ESs generating processes similar to biological ecosystems, they can facilitate regenerative processes more effectively that consume and generate resources and, with this, destroy but also accumulate exergy.

A two-year dataset of energy, environment, and system operations for an ultra-low energy office building

This paper describes a two-year high-fidelity dataset for an ultra-low energy office building and living laboratory called HouseZero®. The building integrates multiple low-energy technologies, such as natural ventilation with automatic windows, ground source heat pump, and thermally activated building systems. The building’s performance is continuously monitored with an extensive sensor network. The dataset consists of breakdown energy end uses, photovoltaic (PV) production, zone-level indoor environment including indoor air temperature, CO2 concentration, and relative humidity, micro-climatical conditions, building façade temperature, and detailed system operations including zone-level BTU meter, valve status, slab temperature, window/skylight opening status, heat pump, and geothermal well operations. The data can be used to support data analytics of ultra-low-energy building operations, and data-driven modeling of low-energy building systems.

Thermal Performance Improvement of Composite Phase-Change Storage Material of Octanoic Acid–Tetradecanol by Modified Expanded Graphite

Phase-change cold storage technology is recommended as a solution for energy conservation and carbon neutrality in air conditioning systems of buildings. This study focuses on the development of binary composite phase-change materials comprising octanoic acid–tetradecanol (OA-TD). To enhance its thermal conductivity, expanded graphite (EG) was employed as an additive carrier, and the surface modification of EG particles using hexadecyltrimethoxysilane (HDTMOS) was attempted to make up for the instability and further to improve the performance of OA-TD/EG CPCMs. The OA-TD/EG-HDTMOS CPCMs were synthesized by EG mixed with EG-HDTMOS at a 1:1 mass ratio. The thermal performance and stability of the OA-TD/EG-HDTMOS CPCMs were thoroughly evaluated by multi-cycle melting–solidification and thermal conductivity measurements. The results revealed that the OA-TD mixture, when at a mass ratio of 77:23, exhibited a phase-transition temperature of 11.4 °C and a latent heat ranging from 150 to 155 J/g. Then, the OA-TD/EG-HDTMOS composite material, at a 12:1 mass ratio of OA-TD to EG-HDTMOS, solidified and melted at temperatures of 9.2 °C and 11.2 °C, with a latent heat ranging from 138 to 143 J/g, and significantly improved the thermal conductivity to 0.7 W/(m·K), representing a remarkable 133% increase compared to that of OA-TD alone. Even after undergoing 100 melting–solidification cycles, the OA-TD/EG-HDTMOS maintained superior phase-change thermal performance and stability, making it suitable for cold storage and energy conservation in air conditioning.

Practice-oriented numerical model for seismic response of cold-formed steel-framed mid-rise buildings

Cold-formed steel (CFS) framed buildings are becoming increasingly common, notably in the high seismic regions of the United States (US). However, research to understand the seismic performance of CFS-framed buildings specifically using large-scale (building-level) specimens has begun only recently. Therefore, efforts to validate and improve numerical modeling of whole building systems have been limited due to this lack of experimental data. A recently completed experimental program, conducted at the Large High-Performance Outdoor Shake Table at the University of California, San Diego, involved earthquake testing of a full-scale 6-story CFS-framed building. The test building adopted an assembly of continuous tie-rod and built-up compression stud pack at the ends of individual shear walls to resist seismic overturning moment and uplift forces within the building. The objective of this paper is to present a practice-oriented phenomenological finite element modeling approach intended for design engineers and compare the predictions from this model against the measured seismic response of the 6-story building. The model aims to provide a robust, yet computationally efficient approach, with minimal degrees-of-freedom, while also capturing the salient features of seismic response of mid-rise CFS-framed buildings suitable for nonlinear time history analyses-based design. The building model, developed within the OpenSeesPy finite element platform, includes the shear walls and gravity walls as well as the lateral strength and stiffness contribution from exterior and interior gypsum sheathing. The continuous tie-rod system is also modeled to capture the cumulative floor displacements caused by the axial elongation in the steel rods. The effect of built-up compression stud packs on the lateral behavior of a shear wall was estimated using available experimental data and a detailed numerical model of an isolated shear wall, then subsequently incorporated in the nonlinear hysteretic material model. The numerical model captured the natural frequencies of the translation modes of the building in its undamaged state with <3% error. It also predicted the peak roof drift ratio within an error of 0.03% drift ratio under the design earthquake event. These results provide confidence in the proposed numerical modeling approach as a tool for seismic response prediction of mid-rise CFS-framed buildings with similar structural detailing features.

Decentralized Control Design for Heating System in Multi-Zone Buildings Based on Whale Optimization Algorithm

Decentralized Control Design for Heating System in Multi-Zone Buildings Based on Whale Optimization Algorithm

Quantum Cryptography in Modern Cybersecurity

This article tends to deal with the fast-evolving world of quantum cryptography, specifically the concept of quantum key distribution (QKD), which has significant importance in modern-day cybersecurity scenarios. The most notable feature of quantum cryptography is its ability to ensure safe communication through principles borrowed from quantum mechanics such as entanglement and superposition. The research comprehensively analyses discrete-variable protocols (DVQKD) and continuous-variable ones (CVQKD), closely investigating their potential roles as re-inventors of secure communications owing to increasing cyber threats. In principle, quantum cryptography is invulnerable to all forms of attacks; however, there are numerous challenges to its implementation including high deployment costs, technology limitations and complex integration with existing systems for protecting data. This paper presents an elaborate state of today's quantum cryptography capturing its advancements, active research efforts as well as barriers towards increased use in build systems based on resistance against attacks utilizing properties specific to quantum mechanics. Key Words: Quantum Key Distribution (QKD), Post – Quantum Cryptography (PQC), Discrete-Variable (DVQKD) and Continuous-Variable (CVQKD), Quantum Superposition, Crypto-Attacks.

Delivering holistic energy-saving operation for heterogeneous all-parallel chiller plant systems through two-stage optimization assisted by input convex neural networks

With the rapid growth of building cooling demand and energy consumption, the potential for energy-saving operations gave rise to the popularity of implementing heterogeneous all-parallel chiller plant systems in large-scale public buildings. Meanwhile, heterogeneity, multi-dimensionality, and tight coupling interactions considerably complicate the holistically optimal operation of the system. Although artificial neural networks (ANN) have achieved great success in complex system identification, the endogenous non-convexity of classical feedforward neural networks (FNN), which are widely applied in model-based optimization for chiller plant operation, makes conventional optimizers prone to falling into local optima, forcing previous methodologies to sacrifice model precision for computational tractability. In this study, a special type of ANN named input convex neural network (ICNN) was leveraged to identify five individual convex models for energy-consuming units in the chiller plant system, providing convex input-output mappings while preserving high-resolution representations. Then, a two-stage optimizer with a sequential integration of a global searcher and a local optimizer was developed to effectively solve the holistic energy-saving problem by optimizing ten decision variables including chilled water supply temperature, partial load ratio of chillers, speed ratios of pumps and cooling tower fans, cooling water flow rate of cooling towers, and on/off states of devices. Finally, the methodology was deployed to a case study using on-site operation data and was compared with conventional candidates. Average holistic energy-saving ratios of 13.37% and 9.14% were achieved with average runtimes of 1.12 s and 1.07 s by the proposed optimizer in the high-load and light-load scenarios, respectively, showcasing more advantageous in resolving the operation optimization of complicated chiller plant systems.

Ultralong Bistable, Electrolytic MnO2-Based, Electrochromic Battery Enabled by Porous, Low-Barrier, Hydroxylated TiO2 Interface.

Electrochromic (EC)battery technology shows great potential in future "zero-energy building" by controlling outdoor solar transmission to tune heat gain as well as storing the consumed energy to reuse across other building systems. However, challenges still exist in exploring an electrochemical system to satisfy requirements on both ultra-long optical memory (also called bistability) without continuous power supply and high energy density. Herein, an ECbattery is proposed to demonstrate ultra-long bistability (>760h) based on the reversible deposition and dissolution of manganese oxide (MnO2) without the addition of any mediators. A porous low-barrier hydroxylated titanium dioxide (TiO2) interface is incorporated to synergistically enrich Mn2+-affinity active sites for deposition and effectively reduce the electron transport barrier of MnO2 for dissolution, thereby significantly improving the reversibility, high optical modulation (60.2% at 400nm), and energy density (352mAhm-2). The modification strategy is also verified on the cathode-less button cells with a much higher average coulombic efficiency (99.9%) compared to the batteries without the porous hydroxylated TiO2 interface (74.6%). These achievements lay a foundation for advancements in both electrochromism and Zn-Mn aqueous batteries.

Advanced smart HVAC system utilizing borehole thermal energy storage: Detailed analysis of a Uppsala case study focused on the deep green cooling innovation

This article presents and thoroughly examines an innovative, practical, cost-effective, and energy-efficient smart heating, ventilation, and air conditioning (HVAC) system. The fundamental component of this concept is a state-of-the-art method called Deep Green Cooling technology, which uses deep drilling to utilize the ground's heating and cooling potential directly without the need for machinery or heat pumps. This method satisfies demands with the least energy use, environmental impact, and operational costs. In order to effectively oversee and regulate energy production, storage, and utilization, the system consists of an intelligent control unit with many smart controllers and valves. Renewable energy deployment is made easier, and the intelligent automation unit is more compatible with the help of a high-temperature cooling resource with a high supply temperature of 16 °C. The technical, environmental, and financial aspects of the suggested smart office building system in the southern region of Uppsala, Sweden, are evaluated using TRNSYS software. According to the results, boreholes provide more than 28.5 % of the building's energy requirements by utilizing the ground's ability to generate affordable, dependable seasonal thermal energy. The district heating network satisfies the remaining demand, amounting to 787.2 MWh, highlighting the benefits of combining conventional and renewable energy sources for increased supply security and dependability. The borehole thermal energy storage system meets the building's entire cooling need, underscoring the importance of high-temperature cooling systems. The most expensive part of the system is the borehole thermal energy storage, which accounts for over half of the total investment. The system has an appropriate payback period of ten years, proving its long-term profitability and cost-effectiveness, thanks to removing the machinery and heat pump. With 3138 MWh of ground-source heating and cooling, the system saves 17,962 USD by reducing CO2 emissions by about 143.7 t, sufficient to grow 16.3 ha of trees throughout the payback period.

Advanced co-simulation framework for assessing the interplay between occupant behaviors and demand flexibility in commercial buildings

With buildings contributing significantly to electricity usage, enabling demand flexibility becomes a challenge, especially when accounting for occupant comfort. This study introduces an innovative co-simulation framework integrating multiple models: heating, ventilation, and air conditioning (HVAC) system, building zone load, indoor airflow, supervisory control, and occupant comfort and behavior. Uniquely, this framework allows for a comprehensive and dynamic analysis of building systems and occupant interactions in demand response events. Using this framework, we conducted a case study using a typical small office building model. Specifically, we focused on three areas: (1) the impact of indoor airflow modeling on energy use, occupant comfort, and behaviors forecasting, (2) the impact of occupant behaviors on demand flexibility, and (3) occupant comfort and behaviors under demand response events. Key performance indicators such as energy use, flexibility factor, durations of occupant discomfort and occupant behaviors were analyzed. Our findings indicated variations in energy usage and occupant comfort within demand flexibility events, marked by uncertainty boundaries, with variability in demand shedding up to 57.9%. We concluded that this framework is suitable for analyzing typical commercial buildings and their HVAC systems in terms of demand flexibility potential under the impact of occupant behaviors.

Mitigating operational greenhouse gas emissions in ageing residential buildings using an Urban Digital Twin dashboard

With the increasing stock of ageing infrastructure and resource constraints in Singapore, related risks and carbon emissions can be mitigated through long-term resilience planning, automated building inspection, and effective maintenance. Sustainable actions are needed to maintain Singapore's ageing infrastructure. Hence, a state-of-the-art control and management system is required in the form of smart city digital tools. We introduce an Urban Digital Twin (UDT)—GHG App for decision-makers in Singapore's operational building greenhouse gas (GHG) emission mitigation and decarbonisation initiatives. Based on multiple-criteria decision analysis (MCDA), a Potential for Intervention (PFI) map was created to rejuvenate the building system. Decision-makers can use this map to prioritise the rejuvenation of low-carbon building systems in the built environment. A heat map of the PFI results highlights which buildings need urgent rejuvenation based on critical parameters. The GHG App utilises this method to generate maps and enables users to modify parameter weights based on their priorities, automatically updating the map. Users can plan an intervention for buildings with higher PFI values once the map is generated. The GHG App provides interactive data visualisation of 119,872 features representing Singapore's built environment, including the context size of 6,785 existing residential buildings modelled and used to demonstrate the analysis results. Our research findings can contribute to the development of standards for accounting for operational GHG emissions, setting emission limits, and planning decarbonisation in the built environment sector.

Electrical Fire Safety Considerations in the Fire Protection System Design of High-rise Buildings

High-rise buildings, as significant symbols of urban development, demand critical attention to the electrical fire safety design of their fire protection systems. This paper aims to explore the electrical fire safety design in the fire protection systems of high-rise buildings to enhance fire safety. By comprehensively analyzing the principles and methods of electrical fire safety design, the feasibility and effectiveness of such systems in high-rise buildings are examined. This study also considers modern technologies and innovations to improve the performance of electrical fire safety systems. Finally, several recommendations are proposed to strengthen the practical application of electrical fire safety design in high-rise buildings.

Performance simulation and analysis of a solar-assisted multifunctional heat pump system for residential buildings

ABSTRACT As building electrification is recognised as an important opportunity to reduce greenhouse gas emissions, the integration of solar energy and heat pump represents a promising solution towards net-zero carbon buildings. This paper presents a hybrid multifunctional solar-assisted heat pump (SAHP) system that can provide space heating, space cooling, domestic hot water, and onsite electricity generation. Photovoltaic-thermal collectors are used for electricity generation, heat collection, and radiative cooling. The system design and controls support fourteen operational modes involving different components. TRNSYS software is used to model and simulate the multifunctional SAHP system. With a 2-m3 storage tank and 30-m2 PVT collectors, the multifunctional SAHP system has a seasonal performance factor of 2.7 in Baltimore and 3.7 in Las Vegas. The onsite electricity generation can cover 53% of the building’s electricity needs in Baltimore and 83% in Las Vegas.