Building Energy Research Articles

Enhancing building energy efficiency: Numerical analysis of a hybrid thermoelectric ventilation system and earth-air heat exchange with photovoltaic-thermoelectric power integration for heating and cooling loads

The lack of studies on hybrid systems combining thermoelectric ventilation (TEV) and earth-air heat exchangers with a photovoltaic-thermoelectric generator (PV-TEG) as the power supply for the hybrid system is addressed in this research. This study aims to investigate the effectiveness and performance of such a combined system in providing the required thermal load of a building. To simulate the performance of TEV and PV-TEG systems, energy conservation equations for various components of the system were formulated for the quasi-state mode. The performance of the earth-air heat exchanger system was evaluated using the effectiveness-number of transfer units method. The resulting set of algebraic equations was then solved using a MATLAB code. Results indicated significant preheating (5.41–15.03 °C) and precooling (0.7–10.22 °C) effects during the daily hours of 15-February and 15-July, respectively. Additionally, the TEG component effectively reduced PV temperatures by 3.54 °C–16.99 °C in 15-February and 3.99 °C–21.54 °C in 15-July. The system also demonstrated a high monthly useful thermal energy output (1215–1584 kWh) in the cold months (December to March), contrasting with higher monthly useful thermal exergy (712–763 kWh) in the summer months. Furthermore, increasing the number of TEGs from 10 to 50 resulted in significant improvements in the yearly average energy and exergy proficiency indexes (PIya,en and PIya,ex) by 164.97 % and 459.82 %, respectively. The supply air mass flow rate and the length and depth of the earth-air duct were identified as the second and third most influential parameters affecting these indexes. Moreover, the system at CPV concentration ratio of 3 provided the highest PIya,en and PIya,ex of 1.512 and 14.65, respectively.

Validation of a Model Predictive Control Strategy on a High Fidelity Building Emulator

In recent years, advanced controllers, including Model Predictive Control (MPC), have emerged as promising solutions to improve the efficiency of building energy systems. This paper explores the capabilities of MPC in handling multiple control objectives and constraints. A first MPC controller focuses on the task of ensuring thermal comfort in a residential house served by a heat pump while minimizing the operating costs when subject to different pricing schedules. A second MPC controller working on the same system tests the ability of MPC to deal with demand response events by enforcing a time-varying maximum power usage limitation signal from the electric grid. Furthermore, multiple combinations of the control parameters are tested in order to assess their influence on the controller performance. The controllers are tested on the BOPTEST framework, which offers standardized test cases in high-fidelity emulation models, and pre-defined baseline control strategies to allow fair comparisons also across different studies. Results show that MPC is able to handle multi-objective optimal control problems, reducing thermal comfort violations by between 66.9% and 82% and operational costs between 15.8% up to 20.1%, depending on the specific scenario analyzed. Moreover, MPC proves its capability to exploit the building thermal mass to shift heating power consumption, allowing the latter to adapt its time profile to time-varying constraints. The proposed methodology is based on technologically feasible steps that are intended to be easily transferred to large scale, in-field applications.

An exploratory factor analysis on technological-related barriers to the construction of zero-energy buildings in Nigeria

PurposeZero-energy building (ZEB) has been considered as an innovative approach to reducing building carbon emissions (CEs) and improving building energy performance. Despite huge benefits of ZEBs, there are still challenges limiting the construction of ZEBs in the construction sector. This study seeks to assess and determine the principal component of technological-related barriers to the construction of ZEBs in Nigeria.Design/methodology/approachThe study designed a questionnaire to examine the technological-related barriers to the construction of ZEBs in Nigeria. Questionnaires were administered, and 272 valid responses were elicited. Thereafter, data were analysed using descriptive and inferential statistics.FindingsThe results from the exploratory factors analysis show that the principal components of technological-related barriers to the construction of ZEBs in Nigeria are categorised into five principal components: access and awareness for technological integration and innovation; knowledge on renewable technology integration; space and complexity of sustainable energy technologies; cost and readiness for ZEB technologies; and research outcomes with practical implementation in sustainable building technologies.Originality/valueThis study contributed to more effective ZEB studies by highlighting technological-related barriers to the construction of ZEBs in construction industry. An understanding of these barriers can aid construction stakeholders, organisations, policy-makers and governments in devising strategies targeted at reducing these technological-related barriers and fostering the construction of ZEBs in construction sector. Recommendations for further study on ZEBs were also made.

Transparent Photochromic Coatings for Smart Windows and Information Storage

AbstractOrganic–inorganic composite photochromic coatings receive significant attention in energy utilization and conservation due to their easy application and rapid photoresponse. However, their high sensitivity to moisture limits practical applications. This study fabricates organic–inorganic composite functional coatings using a simple one‐pot method, producing materials with excellent photoresponsive, water resistance, and thermal insulation. Unmodified phosphotungstic acid serves as the inorganic photochromic filler, while methacrylate derivatives act as the polymer matrix, resulting in coatings with high initial transparency (over 85%). After 5 min of UV exposure, the samples shift to nearly non‐transmittable states in the visible range and maintain color depth through 14 coloring‐erasing cycles. Unlike polyvinylpyrrolidone substrates, which absorb moisture and soften quickly, this coating has a static water contact angle of up to 91.7°, showing excellent water resistance. Additionally, the composite material provides effective heat insulation in simulated chamber experiments, lowering the temperature by 15 °C compared to untreated chambers. In summary, this composite coating, with its excellent thermal insulation, rapid light responsiveness, stable cycling performance, and outstanding waterproof capabilities, proves highly suitable for applications such as smart windows, information storage, and building energy efficiency.

Highly efficient flame retardancy and fire spread behavior of rigid polyurethane foams with extremely low content of flame retardant

In recent years, flame-retardant rigid polyurethane foam (RPUF) has once again captured the scholars’ attention due to the growing the growing demands in the field of building energy efficiency. However, the inherent flammability of RPUF is high and the complexities of its flame-retardant mechanisms remain inadequately understood. In this study, a modified biomass material, amino starch phosphate ester was successfully synthesized which was then combined with expandable graphite (EG) and incorporated into RPUF. The research delved into the optimal ratio and minimal flame-retardant additive required to achieve the stringent UL-94 V-0 rating, while also thoroughly investigating the flame retardancy and thermal properties of the composite system. The findings revealed that with just 6 wt.% of flame retardants, the RPUF achieved the UL-94 V-0 rating, with a 41.1 % reduction in peak heat release rate (pHRR) and a 23.7 % decrease in total heat release (THR). Additionally, it diminishes the flame's size and width, leading to quicker extinction, while also lowering the internal combustion temperature of the polyurethane, thereby delaying both preheating and combustion times. The synergistic interaction between amino starch phosphate ester and EG during combustion results in the formation of a dense, continuous char layer, effectively inhibiting the pyrolysis and combustion of RPUF. This study provides a theoretical foundation for the development of efficient flame-retardant systems for RPUF.

Research on Predictive Analysis Method of Building Energy Consumption Based on TCN-BiGru-Attention

Research on Predictive Analysis Method of Building Energy Consumption Based on TCN-BiGru-Attention

Reducing uncertainty of building shape information in urban building energy modeling using Bayesian calibration

This study proposes urban building energy modeling that extends beyond single-building-level models to the urban level. However, most urban building energy models use representative buildings that may not accurately reflect the diversity of building shapes, systems, and envelope performance when conducting building energy evaluations at the urban scale. To address this issue, previous studies have utilized representative buildings and Bayesian calibration to estimate uncertain building information parameters without considering building shape information. Therefore, the primary objective of this study is to estimate building shape information using artificial neural networks and Bayesian calibration based on building energy consumption data to identify the shape information uncertainty of representative buildings. The results indicate that some shape information can be estimated by comparing the overall distribution of the building stock using the two-sample Kolmogorov–Smirnov test. Furthermore, we found that the proposed energy modeling methodology yields energy consumption patterns similar to those of the target building stock. This preliminary investigation addresses the uncertainty of representative buildings in urban-scale modeling, elucidates the relationship between building form and energy consumption, and introduces a method for inferring shape information from energy consumption data.

Simultaneous Energy Optimization of Heating Systems by Multi-Zone Predictive Control—Application to a Residential Building

Climate change has made energy management a global priority. In France, the Grenelle Environment has set very ambitious progress targets for positive-energy buildings, particularly in terms of reducing and managing energy consumption. However, effective energy management in multi-zone buildings presents significant challenges, particularly when considering the inter-zone dynamics and heat transfer. This study examines multi-zone heating control, using a data-driven model for predictive indoor temperature modeling in intelligent buildings taking into account the influence of interconnected adjacent zones. The research methodology uses dynamic thermal simulation, parallel predictive models based on multiple linear regressions, and a multi-objective non-dominated sorting genetic algorithm II (NSGA-II) for the optimization process, which evaluates various generated heating strategies. This research introduces an approach to improve building energy efficiency by considering inter-zone dynamics and reducing heating-related energy consumption compared to a conventional heating strategy. By applying this model predictive control on a simulated case, a reduction in energy consumption due to heating is observed while respecting thermal comfort. This work contributes by implementing a method that independently controls temperatures in different building zones simultaneously while applying distinct constraints to each zone. This approach empowers occupants to manage heating consumption based on their preferences, ensuring personalized comfort. In addition, a comparison was made using a model that did not account for inter-zone interactions. This comparison demonstrates that incorporating these interactions into the predictive model enhances the effectiveness of the model predictive control approach. The multi-zone approach was also validated experimentally by using real experimental data, demonstrating significant reductions in energy consumption.

Optimizing Indoor Comfort and Energy Efficiency using Right-Angled Triangular Responsive Facades in Cairo, Egypt

Building energy consumption has been rapidly increasing in recent years due to several factors such as climate change and global population growth. Besides, the majority of buildings are not designed with the consideration of the alteration of the severe conditions of the external surrounding environment, which affects the indoor environment negatively. As a result, excessive HVAC systems are utilized in order to maintain the indoor environment and achieve the indoor human comfort. Thus, large amounts of energy are being consumed and the rates of the energy consumption are increasing rapidly. Responsive architecture is considered as one of the solutions that architects, and façade designers use in order to block the excessive solar radiation and direct natural light and thus enhance the indoor comfort zone. However, the majority of the façade’s pattern designs are not following specific guidelines. This study contributes to the field by identifying an optimal right-angled triangular façade design that effectively enhances indoor thermal comfort, reduces solar radiation, and minimizes energy consumption, thereby providing a practical solution for improving building performance in response to climate change and urban growth challenges. This article will study four different façade pattern cases, which are common in the rotational movement, façade orientation and pattern dimensions; however, they differ in the orientation of the axes of movement. The four-façade pattern proposals will be investigated through simulating the solar radiation, consumed cooling energy and the indoor operative temperature during the maximum solar exposure day. A comparative analysis will be conducted between the results in order to highlight the most efficient right-angled triangular pattern that can be used on the south façade in Cairo, Egypt in order enhance the indoor thermal comfort, enhance the energy consumption rates, reduce the solar radiation and improve the building performance.

An automatic and efficient fault diagnosis strategy for air conditioning units by combining attention mechanisms

The air conditioning system is a very important energy-consuming device in the field of construction. Timely troubleshooting of air conditioning chillers is an important aspect of reducing building energy consumption. Aiming at the problems of many model parameters, large amount of calculation and strong dependence of diagnostic performance on input features in data-driven diagnostic methods, this paper proposes an efficient fault diagnosis strategy combining RepVGG network and SENet attention mechanism. The multi-parameter feature signal is converted into a two-dimensional matrix to obtain multiple feature images. Based on the SENet attention mechanism, the multi-channel weights of the images are extracted, and the weighted feature image is input into the RepVGG network for feature depth mining and classification. Therefore, the method proposed in this paper greatly reduces the calculation amount of parameters, improves the representativeness of signal features to faults and the efficiency of model training. The effectiveness of the method is validated using the ASHRAE RP-1043 chiller unit dataset. The results show that the accuracy of this method reaches 96.55%, which is significantly better than the comparison methods PSO-ELM(96.4%), DT(95.1%), RepVGG(91.6%), ELM(87.6%) and BP(80.8%).

Systematic review of solar techniques in zero energy buildings

Systematic review of solar techniques in zero energy buildings

UBEM calibration method by energy consumption disaggregation using a change-point model

Urban Building Energy Modeling (UBEM) is valuable for analyzing urban energy demand, but uncertainties can create gaps between predictions and actual usage. Calibration often uses utility bill data due to its urban-scale accessibility. However, comparing this data with simulation results for specific energy uses like cooling and heating is challenging. This study introduces a UBEM calibration method using a change-point model to disaggregate energy into heating, cooling, and base load. Applied to 222 office buildings in Seoul, this method provided more appropriate calibration results by considering individual energy consumption characteristics rather than just total consumption. The CVRMSE for base load, heating, and cooling energy decreased compared to uncalibrated simulations. This research highlights the importance of energy disaggregation when calibrating UBEM with utility bill data, offering a more nuanced approach to urban energy modeling.

Impact of space utilization and work time flexibility on energy performance of office buildings

This study investigates the impact of different space utilization on energy use intensity, heating load, cooling load, and thermal comfort of occupants using a combination of empirical data gathering and simulation-based studies. The increasing worldwide preoccupation with energy consumption and environmental sustainability has led to increased attention on optimizing interior spaces to mitigate total energy demand. The considered case study for conducting this research was the Norwegian living lab namely Zero Emission Building (ZEB) Flexible Lab in Trondheim, Norway. In this study, 10 different scenarios of occupancy schedules based on flexible arrangements from standard workweeks to extensive remote work configurations were designed and analyzed using IDA ICE 5.0. The findings demonstrate significant reductions in energy use across scenarios with increased teleworking and compressed work weeks. The remote scenario achieved the most significant decrease in Energy Use Intensity (EUI), with a reduction of 46 % compared to the base case. Similarly, the implementation of flexible hours and remote working in scenarios resulted in a reduction of electric heating demand by up to 23 %, underscoring the potential of occupancy-based strategies in enhancing building energy efficiency. However, uncomfortable hours increased by 59 % in the 2-day remote working scenario compared to the base case, demonstrating the need to consider climate conditions when implementing remote work. The research offers valuable insights into the complex connections between flexible arrangements and energy efficiency, considering many elements such as occupancy schedule and use dynamics. This article offers a comprehensive analysis that may give architects, building managers, and policymakers valuable insights. This study contributes to the developing sustainable architecture by emphasizing the impact of dynamic occupancy on the energy performance of office buildings.

Consolidating building greening: Integrating mobile modular vertical greening systems into prefabricated building

Vertical greening systems increase green plant coverage within built environments, enhancing urban air quality while reducing building energy consumption. But they encounter challenges related to intensive maintenance and significant costs. As a mainstream future building form, the characteristics of modularization and factory customization inherent in prefabricated buildings can effectively address the demands of vertical greening system. This study employs structural equation model and cloud model to examine the feasibility of integrating mobile modular vertical greening system into prefabricated structures, based on a survey of 403 respondents in Shandong Province, China. The study highlights the supportive roles and mediating effects of two maintenance management measures. It confirms the strong correlations between cost control strategies and the successful integration of mobile modular vertical greening system into prefabricated building. The results indicate that integrating intelligent technology and plant-microalgae combined systems are essential exogenous and endogenous maintenance management strategies. They exert a positive influence on prefabricated modular vertical greening systems. A positive interconnection exists between integrating intelligent technology and the plant-microalgae combined system. Carbon credit trading incentive proves more advantageous in reducing mobile modular vertical greening system costs. Plant-microalgae combined system playing a partial mediating role. Although crowdfunding-community participation does not significantly reduce the costs of mobile modular vertical greening system, integrated intelligent technology mediates this relationship. This highlights the indirect role of crowdfunding-community participation in facilitating cost control for these systems.

Design and performance analysis of a net-zero energy building in Owerri, Nigeria

Building cooling load depends on heat gains from the outside environment. Appropriate orientation and masonry materials play vital roles in the reduction of overall thermal loads buildings. A net-zero energy building performance has been analyzed in order to ascertain the optimum orientation and wall material properties, under the climatic conditions of Owerri, Nigeria. Standard cooling load estimation techniques were employed for the determination of the diurnal interior load variations in a building incorporating renewable energy as the major energy source, and compared with the situation in a conventionally powered building. The results show a 19.28% reduction in the building’s cooling load when brick masonry was used for the wall construction. It was observed that a higher heat gain occurred when the building faced the East-West direction than when it was oriented in the North-South direction. Significant diurnal cooling loads variation as a result of radiation through the windows was also observed, with the east facing windows contributing significantly higher loads during the morning hours while the west facing windows contributed higher amounts in the evening. The economic analysis of the net-zero energy building showed an 11.63% reduction in energy cost compared to the conventional building, with a 7-year payback period for the use of Solar PV systems. Therefore, the concept of net-zero energy building will not only help in energy conservation, but also in cost savings, and the reduction of carbon footprint in the built environment.

Optimizing the Use of Water, Energy and Waste Resources in Green Buildings: Critical Review, Benefits and Challenges

Optimizing the Use of Water, Energy and Waste Resources in Green Buildings: Critical Review, Benefits and Challenges

Investigation of mechanical and thermal performances and masonry quality for new raw soil-based insulating hollow bricks

ABSTRACT The development of raw soil-based insulating hollow bricks (RSIHB) provides a promising way for improving the efficiency of building energy conversation and resource utilization of construction waste soil. In this paper, the response surface method (RSM) was adopted to study the effect of multifactor on mechanical and thermal performances of modified raw soil-based samples. The results indicate that the thermal conductivity is significantly affected by the interactions between molding pressure and mixture moisture content, as well as between mixture moisture content and cement admixture. Thus, the optimal mix ratio was determined by the RSM-based regression model. The assessment based on numerical simulation demonstrates that the middle wall ribs immensely improve the stress concentration. The porous structure elongates the path of heat flow conduction, effectively enhancing the thermal resistance of the brick. In addition, bonding strength tests manifest that the mortar consistency should be within 60–80 mm for construction and plastering of the new raw soil-based insulating hollow brick. The research results have strong theoretical significance and practical value for recycling of construction waste and provide a reference for actual production and construction.

Dyna-PINN: Physics-informed deep dyna-q reinforcement learning for intelligent control of building heating system in low-diversity training data regimes

This paper introduces Dyna-PINN, a novel physics-informed Deep Dyna-Q (DDQ) reinforcement learning (RL) approach, designed to address the data-intensive training requirements and model-agnostic nature of the conventional model-free RL methods. The DDQ approach blends model-based and model-free elements to enhance both learning and decision-making processes. By utilizing a physics-informed neural network (PINN) based model, our method enriches the learning process with physical information, enhancing the agent's planning capabilities and leading to faster learning compared to conventional model-free RL methods like Deep Q-Network (DQN) in scenarios with low-diversity training data availability. Our results demonstrate that Dyna-PINN has 50% greater sample efficiency than DQN and outperforms rule-based control in terms of thermal discomfort. Due to physics incorporation, the Dyna-PINN implements a more logical and interpretable control policy. It shows consistently good performance compared to all control variants across low-diversity data scenarios, i.e., 6 weeks of building data, and in higher-diversity data regimes, i.e., 6 months of building energy data, demonstrating the value of physics incorporation into the RL training. Additionally, we present two other DDQ-based techniques, RC-DDQ and NN-DDQ, exploring the synergy between neural networks and physical data in intelligent control designs for building energy systems. Rigorous controller testing is performed using the Building Optimization and Testing Framework (BOPTEST), a high-fidelity simulator that closely represents a real building's operation. Through comprehensive comparisons and realistic simulations, our study underscores the effectiveness of incorporating physics-informed approaches into RL-based control strategies, paving the way for more efficient and robust building energy management systems.

The Building Decarbonization in High-Density Cities: Challenges and Solutions

Abstract Decarbonization of buildings is an imperative and challenging task. Beyond the common challenges associated with building decarbonization, those in high-density urban areas also face technical challenges due to geographical conditions and resource endowments. As decarbonization practices deepen, it has been found that reliance on conventional methods is fraught with difficulties, primarily due to the high proportion of incremental costs involved. This review study explores methods not widely incorporated into existing building energy efficiency standards but which hold the potential for aiding decarbonization. It advocates for a synergistic strategy involving surrounding infrastructure such as power and other building energy systems, innovative low-carbon building materials, and greenery to facilitate this transition.

Real-time optimal hierarchy control of HVAC system with maximized ancillary service support in smart buildings: An experimental approach

This article proposes a real-time optimum control algorithm for Heating, Ventilation, and Air Conditioning (HVAC) units of a commercial building aimed to maximize the ancillary power available for frequency regulation services. First, the prediction of energy consumption of air conditioning zones using long short-term memory (LSTM) is achieved, which helps forecast the aggregated regulation capacity bid of the building in the energy markets. Furthermore, to minimize energy costs and thermal discomfort of building dwellers, an optimization algorithm is designed considering the day-ahead electricity tariffs. Moreover, using installed sensors in a lab-scaled hardware prototype, the proposed algorithm is also shown to be capable of interacting with a Building Energy Management System (BEMS) and investigating the electrical and thermal properties of the HVAC unit. The BEMS controller uses an ethernet protocol to interface with the sensors installed at various operational points throughout the HVAC system. The optimal operation of the HVAC system has been executed with the real-time thermal feedback system, which counterbalances the uncertain thermal load or renewable power availability in the building confronted in real-time operation. The simulation results show the performance of the Artificial Neural Network (ANN) based compressor model in deep learning integrated with the other components of the HVAC unit and thermal model of the air-conditioning zone. Finally, the experimental results display the real-time implementation of multiple thermal and electrical conditions within the proposed control scheme of the HVAC unit. This shows the potential of ancillary services capacity through commercial buildings can be maximized with flexible loads and thereby help in power grid support.