Building Energy Usage Research Articles

Predictive-based Models for Efficient Energy Management in Smart Buildings

The integration of sensing technologies with residential buildings raises the concept of a smart home, which has facilitated the life of the habitant nowadays. This technology helps us to track and understand the behavior of the client in the house to give him maximum comfort. A neighborhood area is an interconnected set of houses that exist in the same geographical region and share the same energy resources. The most important component in the process of decision-making is the energy usage in the smart building. The energy optimization problem in the smart building created a challenge for enterprises and the government for a long time. A lot of research were made to solve this energy optimization problem. One of these problems is the organization of energy usage within a neighborhood area network. The main challenges are to maintain the user comfort in each house and to not exceed the total energy offered to the network. For this, we proposed a technique that predicts, based on historical data of each house, its future behavior and created for each one a weekly schedule with hourly annotated field with: high, normal, or low, where each one represents the amount of energy user is able to use at this time. At the end, an incentive-based program is created to give the client an incentive on his bill if he used the daily high energy consumption in the annotated high in his schedule. To create the schedules, we extracted some features from the data, then we used the genetic algorithm to create schedules, then we did an improvement to the technique using dynamic programming that stores the features of a house with created schedule, later when we meet a similar house we can directly give a schedule that fits the need.

Automatic ventilation and cooling of the building using the combination of wind deflector and solar chimney (state 3D with differential perspective)

Using new sources of energy and inventing new methods for energy consumption has always been the focus of researchers. The building sector is known to contribute largely in total energy consumption and CO2. Expanding the availability of energy storage technology and materials is considered as crucial as discovering new energy sources. The International Energy Outlook by the EIA (Energy Information Administration of the outlook) examines and predicts future trends in building energy usage. It anticipates a 34% growth in energy consumption within the built environment over the next two decades, with an average annual increase of 1.5%. By 2030, it is projected that approximately 67% of this consumption will be attributed to dwellings, while the non-domestic sectors will account for about 33%. This study endeavors to combine one of these ventilation techniques from ancient Persian architecture, known as the windcatcher in the form of an innovative roof radiative cooling (Windcatcher) with a solar chimney. In order to save energy consumption and reduce environmental problems by presenting the idea of using solar energy to exhaust the air inside the building through the chimney and using the latent heat of water evaporation to create cooling that is carried out in the direction of the wind by the windcatcher. Hence, fuel consumption can be created in hot and dry areas, in a comfortable environment with suitable humidity conditions. The purpose of this research is to introduce computational areas (3D) and determine the governing values and methods calculated by Ansys Fluent software, which has enabled the use of suction and the creation of moisture balance and heat reduction without fuel consumption, which plays an important role in It implements energy efficiency in the building. This system is also able to save 60% cooling energy and 80% of the ventilation energy during peak hours in a warm and arid climate. Furthermore, the effects of a wall opening on ventilation and thermal efficiency were examined.

Enhancing thermal performance of phase change materials in building envelopes

AbstractPhase change material in conjunction with a passive latent heat thermal energy storage approach is a potentially effective way to solve the growing concerns about building energy usage. This research examines the thermal performance of building envelopes in Miskolc, Hungary. The factors impacting the thermal performance of phase change material integrated into the building envelope were assessed. The findings indicate that the enthalpy and melting temperature of the phase change material significantly impact the effectiveness of phase change material-based walls. It was determined the optimal location of the phase change material layer is possible. This determination is intricately related to the thermal properties of the phase change material and the prevailing environmental conditions.

Combining geographic information and climate data to develop urban building energy prediction models in Taichung, Taiwan

Climate change in Taiwan has extended and intensified the summer season, leading to a notable surge in energy demand for cooling systems, especially in densely populated regions. Building energy usage is directly correlated with cooling degree hours (CDHs), representing the hourly temperature differential between indoors and outdoors. This study employed high-resolution Taiwan ReAnalysis Downscaling (TReAD) data to develop an urban energy prediction model focusing on localized cooling demand in central Taiwan's urban areas. Validated against actual electricity consumption data, the model achieved an R2 value of 0.76. The study reveals that urban areas exhibit a high cooling demand during the hot season, exceeding 25,000 °C-h and with an annual energy consumption of 44–64 kWh/m2. Conversely, rural areas have a lower cooling demand – that is, below 8,000 °C-h, with an annual energy consumption of <10 kWh/m2.Considering the IPCC's RCP8.5 warming scenario, October shows a 20–40 % increase in cooling demand compared to July and May. This underscores the need to address rising energy consumption especially during the early and late stages of the hot season in response to climate change.

Fuzzy logic-supported building design for low-energy consumption in urban environments

Climate, building materials, occupancy patterns, and HVAC (heating, ventilation, and air conditioning) systems all interact in complex ways, making it difficult to design low-energy buildings. Thus, innovative architectural and engineering design strategies are required to meet the worldwide need to decrease building energy usage. To improve the calculation of energy consumption of buildings, this work introduces the FCR-BCS (fuzzy clustering rule-based building control systems), which integrates fuzzy logic concepts into computational simulations. FCR-BCS can contemplate real-world uncertainties and fluctuations using linguistic factors and approximate reasoning for more precise and trustworthy results in energy-efficient building design. This method's significance rests in its potential to significantly reduce energy use, advance sustainability, and improve urban residents' quality of life; architects and engineers can thus employ FCR-BCS to enhance the efficiency of HVAC systems and insulation. The outcomes of FCR-BCS simulation assessments show that it is capable of making buildings more energy efficient. The experimental outcomes demonstrate that the suggested model increases the sensitivity analysis by 99.4 %, energy efficiency analysis by 99.8 %, occupancy patterns analysis by 97.5 %, temperature profile analysis by 98.8 %, and energy consumption analysis by 99.6 % compared to other existing models.

Exploring the Spatiotemporal Heterogeneities in Urban Vitality Through Scalable Proxies from Mobile Data

Jane Jacob’s concepts of urban vitality and diversity have become prevailing urban planning philosophies in most countries for making cities more livable. Recent changes in demographics and the impacts of COVID-19 have exacerbated the economic and social challenges that cities commonly face, particularly spatiotemporal heterogeneities. Being able to understand these heterogeneities in scalable approaches is fundamental to tackling these challenges in cities. Therefore, this article aims to provide a new form of scalable estimation of urban vitality by using the de facto population. Instead of merely adopting static statistical information such as morphological characteristics in areas, we leverage dynamic factors such as internal mobility flows and energy use intensity as proxies for the spatiotemporal dynamics of indoor and outdoor behaviors of crowds. In this way, we combine dynamic attributes and static features to describe the patterns of urban vitality, which are directly related to spatiotemporal dynamics in urban places. We utilize GNSS-based mobile data and building energy usage intensity as dynamic proxies along with static data such as land use mix and age distribution. To better capture spatial heterogeneity, we use a Multiscale Geographically Weighted Regression (MGWR) model to identify the relationships between the de facto population and the dynamic and static factors. Drawing from the factors determining urban vitality, this article provides policy implications for alleviating spatiotemporal urban imbalances. These data-driven implications can fill the technical knowledge gaps in establishing planning strategies for achieving urban sustainability while enhancing overall subjective livability.

Development and tests of the novel configuration of the solar chimney with sensible heat storage

Heating, ventilation, and air conditioning systems account for a significant part of the energy usage in buildings and are primarily responsible for greenhouse gas emissions and operating costs. These can be lowered by the adoption of solar energy. This paper presents the developing and testing of a novel solar chimney with sensible heat storage. The thermal performance and potential contribution of the developed thermal chimney to decreasing residential buildings energy consumption in Polish climate conditions is investigated. The developed solar air chimney is composed of a novel accumulation material that allows for sensible heat storage. Solar radiation can heat the accumulation layer during the day and the stored energy can be used to preheat the ventilation air. The prototype of the solar chimney was tested in laboratory conditions using a dedicated experimental rig. Furthermore, numerical calculations were performed using TRNSYS software, where a validated dynamic numerical model was developed to simulate the performance of the device. The results show that airflow direction, volume, and the number of heated walls significantly impact the power transferred from the solar chimney walls to the air. The maximum power transferred from the solar chimney walls to the air per unit of surface varied from 360.2 W/m2 (when all walls were heated) to 672.4 W/m2 (when only one wall was heated). The validated model underestimates the outlet air temperature of the solar chimney by a maximum of 0.39 °C on average basis and shows that the increase of air temperature between −10 and 20 °C at 1000 W/m2 determines a decrease of 12.5 % of the thermal output. The obtained results allow one to highlight that the proposed solution can be considered as a replacement for typical brick chimneys located on roofs or as a thermal storage wall.

Comparing the Effectiveness of Dynamic Facades for Natural Daylight and Energy Usage in Buildings

Abstract In this research, we explore the integration of dynamic facades to enhance both aesthetics and energy efficiency in buildings. The study introduces two dynamic facade designs, one employing a triangular shape and the other inspired by origami folds, aiming to compare their effectiveness in optimising natural daylight and overall energy usage. The triangular facade features movable wings that dynamically regulate natural light, balancing visual appeal and practical functionality. Similarly, the origami-inspired design adapts to external conditions, creating a symbiotic relationship between form and function. The research employs Python and Rhino 7 with Honeybee and Pollination plug-ins for a comparative analysis. The evaluation includes Spatial Daylight Autonomy (sDA300,50) exceeding 55%, Annual Sunlight Exposure (ASE1000,250) no more than 10%-20%, and optimal facade opening angles based on daylight factor (df) values. The dynamic facades are examined for their impact on energy efficiency, encompassing factors like natural daylight, and operational energy. Through this analysis, the study aims to determine which dynamic facade design, triangular or origami-inspired, proves superior in optimising natural daylighting and overall energy usage. This research contributes valuable insights into the balance of innovative architectural designs and sustainable energy practices, potentially influencing future building designs.

K-PCD: A new clustering algorithm for building energy consumption time series analysis and predicting model accuracy improvement

Clustering algorithms are often applied to building energy consumption data analysis to mine representative patterns of building energy usage. This paper proposes a new clustering algorithm: K-PCD, which is particularly suitable for building energy consumption time series. K-PCD utilizes a Pearson correlation coefficient-based distance measure (PCD), and a novel centroid calculation method that takes into account both the PCD and the traditional Euclidean distance (ED) between time series. This study also proposes a new clustering validity index (CVI) tailored to the predictability of building energy consumption: Energy Prediction Clustering Performance Index (EPCPI). The K-PCD and EPCPI are practically analyzed and validated using one year of data from 29 real buildings. The results indicate that comparing with traditional clustering algorithms, the K-PCD achieves better clustering results. This EPCPI index thoroughly explores building operation patterns, and after adding clustering labels, it can maximize the prediction accuracy of the Back Propagation Neural Network (BPNN) model for building energy consumption.

Integrated optimization of the building envelope and the HVAC system in office building retrofitting

Increasing the energy efficiency of existing buildings is the major strategy to reach carbon neutral targets, given that the building sector is a major emitter. In particular, one major part of building energy usage is the building heating, ventilation and air-cooling system, which are highly depending on the thermal performance of building envelope and increase the challenge of selecting optimal packages of energy efficiency measures. This paper thereby presents a method for optimizing the building envelope and heating, ventilation and air-cooling system. The simulation-based NSGA-III approach developed in this paper is based on coupling the dynamic simulation models created in EnergyPlus software with the non-dominated sorting genetic algorithm, as the engine that performs an optimization process. A large domain of energy efficiency packages combined with heating, ventilation and air-cooling systems and building envelope are investigated to minimize energy consumption, retrofit cost and thermal discomfort in indoor environments, which including ideal loads air system, four pipe fan coil system and variable refrigerant flow system, insulation materials and insulation thickness for external walls and roof, different types of external windows, and window-wall ratio. An office building in Chongqing was chosen as a case study to demonstrate the practical implementation of the proposed method. The results show that the four pipe fan coil system was superior in the diversity and performance of the Pareto front. Then, the optimal packages were generated by a synthetic method, which have 58.57 kWh/m2 of energy consumption, 30.76 CNY/m2 of retrofit cost and 40.67 % of the percentage of thermal discomfort hours. The results also indicate that the yearly energy savings rates are 17.30 %, 23.05 % and 21.10 % separately for ideal loads air system, four pipe fan coil system and variable refrigerant flow system after performing optimization. These results highlight the importance and necessity of performing an optimization procedure for existing buildings to select the optimal retrofit measures.

Simulation and optimization design of air distribution in a cigarette production factory

Air-conditioning energy consumption accounts for a significant proportion of the total energy usage in large commercial buildings. This study utilizes computational fluid dynamics (CFD) to examine the airflow distribution in a cigarette factory with a high ceiling. Numerical simulations were performed to assess the effectiveness of various air supply methods. Using FLUENT, the researchers analyzed the uniformity of airflow, temperature and humidity fields under summer conditions. The findings indicate that employing an upper supply and lower return air method can decrease air-conditioning energy consumption by 7.25%. Field measurements were carried out to validate the numerical simulations, with the measured values used as initial parameters. The average deviation between the measured and simulated temperature and humidity in critical work areas was within 2%. The comparative analysis of the simulation and measurement results confirms the reliability and effectiveness of airflow organization simulations in large commercial buildings. These results offer a scientific foundation and computational guidance for designing and implementing energy-efficient upgrades to air-conditioning systems in similar industrial facilities.

Effect of thermal resistance and infrared transparency on radiative cooling efficiency

Radiative cooling materials that reflect sunlight and emit infrared light to the cold sky can lower the surface temperatures of building envelopes, reducing cooling energy usage by buildings. Over the last decade, various structures, such as thin films/coatings and thick infrared-transparent/opaque foams, have been developed for these applications. Understanding the influence of thermal resistance and infrared transparency of radiative cooling materials, along with the thermal resistance of the substrate to which they are applied, is vital for optimizing their energy savings potential. Herein, we employed a heat transfer model that integrates conduction, convection, and radiation to investigate the energy savings of radiative cooling materials with varying roof and material thermal resistances. Our results suggest that radiative cooling materials are most effective on roofs with low thermal resistance, like metal roofs, and increasing roof thermal resistance reduces their energy-saving effect. Thermally insulating infrared-transparent radiative cooling foams can significantly boost energy savings by minimizing convection and radiation losses. Conversely, compared to thin film, thermally insulating infrared-opaque foams can enhance or reduce energy savings depending on whether their surface temperatures are above or below room temperature. Throughout summer, infrared-transparent foam consistently achieves the highest energy savings, whereas infrared-opaque foam shows the least. This work provides guidance for the practical application and structural design of radiative cooling materials.

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.

Performance investigation of solution-processed semi-transparent perovskite solar cells in building sectors

Semi-transparent perovskite solar cell (PSC) windows have received much attention from scholars due to their remarkable power generation capacity and thermal insulation performance. However, considering the complexity of their fabrication process, and the significant decrease in power generation efficiency when scaling up to large-sized solar modules. To solve the above problems, this study developed a scalable production technological process to prepare translucent PSC modules, in which large-area chalcogenide thin films were prepared by a scratch-coating method. The prepared PSC module has an average visible light transmittance of 20.1 % and shows a maximum power conversion efficiency of 3.92 %. Experimental tests were conducted to investigate its photo-thermal regulation performance concerning the existing common window. The results show that the newly developed photovoltaic window reduces the indoor air temperature by 1.4 °C while the illuminance is reduced by about 59.2 % compared to the common window. A further simulation is performed by Energyplus, with a conclusion that PSC windows can decrease glare probability by 24.7 %–45.7 %, diminish building energy usage by 26.6 %–59.6 %, and achieve up to 469.9 kWh power generation in Harbin, China. The investigation of this paper paves the technical and foundational way for the large-scale application of PSC in building sectors.

Lessening the energy usage of buildings: an exploration into green walls’ practicalities

The article provides insights into green walls’ potential and energy-saving mechanisms in Melbourne, Australia. The use of vegetation on a building’s façade has been shown to positively affect the building’s demand for heating and cooling and a range of additional benefits. A mixed research study has been developed to explore and quantify using vegetated façades as a feasible option for reducing energy usage in commercial buildings. These results show that a green wall of building construction holds a similar level of thermal resistance as a 2.5 cm layer of expanded polystyrene building insulation, at an R-value of 0.38 m2K/W. Combining this calculation with speculations as to the benefits of shade, evapotranspiration, and wind production has produced data showing a typical green wall can reduce the energy usage of a commercial building by up to 12%. This figure supports the use of vegetated façade systems to reduce energy usage in the built environment. This study can be instrumental in raising awareness for building and property professionals, as well as occupants, of the positive effects of green (vegetated) walls on the façades of buildings as a viable option for reducing heating and cooling loads and, consequently, reducing spending on energy usage.

Preparation and characterization of fatty acid ternary eutectic mixture/Nano-SiO2 composite phase change material for building applications

In order to prepare a composite phase change material (PCM) with suitable phase transition temperature, strong temperature control performance, good latent heat properties, and low cost for incorporation into concrete, the goal is to control temperature cracks, and endow the building structure with temperature regulation capabilities through phase change. In this study, capric acid (CA), myristic acid (MA) and stearic acid (SA) were used as PCMs. Firstly, based on theoretical calculations, a ternary eutectic mixture of CA-MA-SA with a mass ratio of 67.16:22.98:9.86 was successfully prepared. Subsequently, a new composite PCM was created using a melt blending technique, incorporating nano-SiO2 as a supporting material. The comprehensive characterization results from FT-IR, SEM, XRD, DSC and TG indicate that nano-SiO2 interacts with CA-MA-SA through its porous structure and capillary action, as well as surface tension, rather than through any chemical interaction, when 65% of CA-MA-SA is adsorbed in nano-silica, the melting temperature and latent heat of the composite PCM are 19.23 °C and 97.82 J/g, respectively. Without any leakage of molten CA-MA-SA. This material exhibits a favorable temperature for phase transition and possesses a high amount of latent heat, making it a promising candidate for use in construction projects. Additionally, the composite PCM shows remarkable thermal stability after 200 phase change cycles, which not only lowers building energy usage but also conserves energy, making it ideal for broad use in civil engineering.

Dynamic Radiant Barrier for Modulating Heat Transfer and Reducing Building Energy Usage

Buildings consume significant energy, much of which is used for heating and cooling. Insulation reduces undesired heat transfer to save on heating and cooling energy usage. Radiant barriers are a type of insulation technology that reduces radiant heat absorbed by a structure. Applying radiant barriers to buildings reduces costs and improves both energy efficiency and occupant comfort. However, homes often have favorable thermal gradients that could also be used to reduce energy usage if the insulation properties were switched dynamically. This article introduces two dynamic radiant barriers intended for residential attics, which can switch between reflecting and transmitting states as needed. These radiant barriers are manufactured as a single deformable assembly using sheet materials and are compatible with various actuation mechanisms. The efficacy of these radiant barriers is reported based on a hotbox experiment and numerical calculations. The experimental results demonstrate that both proposed dynamic radiant barrier designs increase effective thermal resistance by factors of approximately 2 when comparing insulating to conducting states, and by approximately 4 when comparing the insulating state to the case without a radiant barrier. Additionally, the dynamic radiant barriers achieve heat flux reductions up to 41.9% in the insulating state compared to tests without a dynamic radiant barrier.

U-Values for Building Envelopes of Different Materials: A Review

In recent decades, the issue of building energy usage has become increasingly significant, and U-values for building envelopes have been key parameters in predicting building energy consumption. This study comprehensively reviews the U-values (thermal transmittances) of building envelopes made from conventional and bio-based materials. First, it introduces existing studies related to the theoretical and measured U-values for four types of building envelopes: concrete, brick, timber, and straw bale envelopes. Compared with concrete and brick envelopes, timber and straw bale envelopes have lower U-values. The differences between the measured and theoretical U-values of timber and straw bale envelopes are minor. The theoretical U-values of concrete and brick envelopes ranged from 0.12 to 2.09 W/m2K, and the measured U-values of concrete and brick envelopes ranged from 0.14 to 5.45 W/m2K. The theoretical U-values of timber and straw bale envelopes ranged from 0.092 to 1.10 W/m2K, and the measured U-values of timber and straw bale envelopes ranged from 0.04 to 1.30 W/m2K. Second, this paper analyses the environmental factors influencing U-values, including temperature, relative humidity, and solar radiation. Third, the relationship between U-values and building energy consumption is also analysed. Finally, the theoretical and measured U-values of different envelopes are compared. Three research findings in U-values for building envelopes are summarised: (1) the relationship between environmental factors and U-values needs to be studied in detail; (2) the gaps between theoretical and measured U-values are significant, especially for concrete and brick envelopes; (3) the accuracy of both theoretical and the measured U-values needs to be verified.

A Sustainable Development for Building Energy Consumption Based on Improved Rafflesia Optimization Algorithm with Feature Selection and Ensemble Deep Learning

Buildings emit a great deal of carbon dioxide and use a lot of energy. The study of building energy consumption is useful for the sustainable development of multi-energy planning and energy-saving strategies. Therefore, a sustainable development for building energy consumption based on the improved rafflesia optimization algorithm (ROA) with feature selection and ensemble deep learning is proposed in this paper. This method can explore data on building energy usage, assess prediction accuracy, and address concerns that building energy usage research must address. The proposed model first uses an improved self-organizing map with a new neighborhood function to select important features. After that, it uses ensemble deep learning to accurately anticipate the building’s energy usage. In addition, the improved ROA is used to fine-tune parameters for feature selection and ensemble deep learning. This research uses the dataset of the American Society of Heating, Refrigerating, and Air Conditioning Engineers (ASHRAE) to compare the performance of several modeling approaches. It identifies the top five most important features based on the model’s results. Furthermore, the proposed model can be successfully applied to a real-world application. They both have the lowest root mean squared errors among the approaches examined. The proposed model indeed provides the benefits of feature selection and ensemble deep learning with the improved ROA for the prediction of building energy consumption.

Integrating vertical greenery for complex building patterns towards sustainable urban environment

Rapid urbanization has increased the urban density and functional diversity, exacerbating environmental challenges such as urban heat islands (UHI), increased building energy consumption and carbon emissions, etc., hindering the sustainable urban development. Addressing these challenges requires innovative solutions that could offer the strategic cooling of urban buildings. Vertical greenery, affixed to building façades, offers a promising approach to provide benefits of cooling, energy savings, carbon reduction and urban space saving. However, there is a lack of quantitative design for integrating vertical greenery into complex urban buildings. Therefore, this study conducts a systematic investigation for low-rise, mid-rise, and high-rise buildings incorporating vertical greenery, while performing comprehensive assessments on indoor and outdoor thermal conditions, building energy usage, and carbon emissions. The findings suggest that low-rise buildings benefit from a vertical greenery layout, while mid-rise and high-rise buildings are better suited for a horizontal greenery layout. Compared to scenarios without greenery, buildings with vertical greenery experience a maximum reduction of 0.66 °C in outdoor air temperature and 0.72 °C in indoor air temperature, along with a 5.9 % decrease in energy consumption and carbon emissions. This study addresses urban challenges through a quantified vertical greenery design, offering valuable insights for sustainable urban development.