Building Energy Requirements Research Articles

Thermal performance analysis of PCM-integrated structures using the resistance-capacitance model: Experiments and numerics

While holding significant potential to reduce cooling energy requirements in buildings, the incorporation of phase-change materials in building envelopes requires information regarding their diurnal and seasonal behaviour through high-fidelity simulations. However, utilizing short-term simulations and experimentation using state-of-the-art models for such a complex configuration does not accurately represent the true heat transfer dynamics of the building. To address this lacuna, we present a physics-based, low computational cost, experimentally and numerically validated resistance–capacitance (RC) model specifically tailored for PCM-encapsulated structures, designed for long-term simulations. The validation of this model is conducted through in-house experiments. Additionally, we support the credibility of our RC model by subjecting it to validation through 3D numerical simulations, emphasizing its precision and reliability. Then, we use the validated model to optimize the thermal properties of concrete roofs in hot and dry climates, taking a specific instance of a city in Western India for various geometric configurations as an illustrative example. We find that a PCM with a phase change temperature between 37 and 42 °C can reduce the peak ceiling temperature by up to 10 °C and the peak energy ingress by a factor of 2 or more, in a typical roof element subjected to the prevailing climatic conditions during peak summer. This shows the time constant of the modified roof is effective in delaying and damping the imposed solar insolation. We make specific recommendations on the selection and geometry optimization of PCM-incorporated roof elements.

BIM-based energy consumption analysis for residential buildings: effects of orientation, size, and type of windows

Buildings have a significant environmental impact due to their reliance on nonrenewable energy sources. To address this concern, computer software is utilized to estimate building energy requirements. Building Information Modeling (BIM) has emerged as a valuable tool in this process, with BIM 6D offering a particularly beneficial feature. BIM 6D provides a comprehensive energy model of the building, simulating its actual energy performance. By analyzing natural and artificial lighting systems, particularly daylighting, BIM 6D can assist in optimizing energy efficiency. Despite being underutilized, the potential of this feature to enhance building sustainability is noteworthy. BIM 6D facilitates well-informed design and operational decisions for both new constructions and the renovation of existing buildings. The study examines design criteria such as building orientation, window orientation, size, and type to optimize energy consumption in a bungalow in the Sangli region of Maharashtra, underscoring the importance of energy-efficient buildings to local communities. Energy analysis and optimization are conducted using the Autodesk Insight tool. The methodology involves creating 3D models with Autodesk Revit Architecture software and performing various analyses, including Building Energy Modeling (BEM) simulations, Illuminance Analysis, Solar Access, and Daylight Autonomy assessments. The analysis findings indicate that low-e glass windows of small size and oriented between 0° and 90° significantly enhance energy efficiency under certain conditions, while changing the window type does not affect the analysis. The paper concludes with recommendations for window orientation and size to enhance energy performance in residential buildings, contributing to the broader goal of sustainable and energy-efficient construction practices. This study provides valuable insights for designing energy-efficient residential buildings in rural areas, addressing awareness gaps, and promoting sustainable construction practices.

Graphite-enhanced PCMs in Gypsum Boards for Reduced Building Energy Requirements

Graphite-enhanced PCMs in Gypsum Boards for Reduced Building Energy Requirements

Evaluating conventional and renewable energy systems for green buildings: A case study on energy efficiency and cost optimization

This study explores two primary categories of power generation: conventional methods reliant on fossil fuels and renewable energy methods. Fossil fuel-based approaches, including natural gas and heavy fuel oil, contribute to environmental concerns due to their carbon dioxide (CO2) emissions, which are responsible for the greenhouse effect and global warming. Many nations, particularly in Europe, have implemented stringent measures to restrict CO2 emissions, imposing fines on industries that exceed emission limits. In response to these concerns, this study focuses on a green building project that aims to meet its power requirements through renewable energy sources. Various renewable energy resources will be evaluated, considering the merits and drawbacks of each technology, to identify the most suitable option for the building under investigation. Additionally, a comprehensive thermodynamic analysis will be conducted, including calculations of electrical needs such as cooling load, lighting load, and equipment load. A customized power calculator will also be developed for application in similar projects. The ultimate objective of this study is to achieve complete autonomy from the electric grid by utilizing renewable energy resources, transforming the building into a green building. The results indicate that at an outside temperature of 22 °C, the total heat gain decreases from 157.3 to 39 kW, reducing the total cost of electricity from 20,000 to 5100 AED/year. Conversely, at an outside temperature of 50 °C, the total heat gain increases from 157.3 to 196 kW, raising the total cost of electricity from 20,000 to 26,000 AED/year. These findings demonstrate the significant influence of outside temperature on the building's energy requirements and costs, underscoring the importance of considering temperature variations in energy efficiency and cost optimization strategies.

Mathematical models proposed for calculating the utilization magnitudes and utilization proportions of renewable energies for satisfying the building energy requirement

Mathematical models proposed for calculating the utilization magnitudes and utilization proportions of renewable energies for satisfying the building energy requirement

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.

Photovoltaics for reaching Net-Zero Energy Standard in Educational buildings

Buildings should become energy-neutral to reduce pressure on the planet's resources and the atmosphere. Then cities must be energized with in-site renewables integrated to reach low-impact sustainable cities. This research project is an initial step to figuring out the feasibility of reaching the Net Zero Energy condition in the Architecture Department buildings of the University of Cuenca, as a case sample of higher-educational facilities located next to the equator and on the highlands of the Andes mountains range. In this research we had just determined the photovoltaic (PV) potential to reach the actual energy requirement as an initial step, to do so, we had virtually constructed the building facilities and obtained the building energy requirements, considering full power requirements measured before the COVID restrictions. As the main results, we determined, that 88,6 % of the 2019 energy requirements could be solved with solar PV considering roof availability and architectural restrictions. The financial analysis also reveals that it is a profitable strategy. Next research will determine that comfort levels are adequate to foresee the total requirements to reach the NZEB standard.

Microclimatic models and their implications on the energy requirements of buildings in warm dry urban areas

Understanding the parameters that amplify or mitigate temperature increases in the built environment is crucial to reducing the energy demand of buildings and achieving urban resilience. This research examines how microclimate scenarios can affect building energy performance forecasts in cities with temperate and dry climates. The aim is to calculate the variation in building energy requirements resulting from using different weather files and urban parameters using the UBEM tool. To achieve this, UWG and Energy Plus software were used to create urban and building models. The study analysed four microclimatic scenarios in the city of Mendoza, Argentina. The scenarios used as a weather file input the annual format of a specific year (AMY files) or long-term (TMY file), whilst also considering the specific urban parameters of the assessed area. The study found that an incorrect weather file can cause a 36% error in energy usage assessments for cooling buildings during summer months. The contribution of this study is the development of a methodology for obtaining a weather file that accurately reflects the microclimate characteristics of the urban area of interest.

Structural analysis for a slab-on-grade cellular concrete foundation to reduce heat losses in temperate climate residential buildings

Reducing the energy requirements of buildings is imperative in the face of global warming and the imminent need to build low or zero-carbon housing in the following decades. This research proposes the design of a structural foundation to reduce heat transfer between the ground and the house to improve thermal comfort, thus reducing the incidence of low interior temperatures and lower energy requirements. The proposed foundation consists of a waffle slab system incorporating on-site cellular concrete fabrication for a residential home. The foundation proposal was evaluated over a year through simulation to quantify the heating demand reduction for four cities with temperate semi-arid and temperate sub-humid climates. The analysis shows that the prevalence of low temperatures is reduced when applying any of the proposed foundations. However, the benefits must be weighed for cities like Durango and Puebla since the reduction of hours with low temperatures is minimal, and they show an increase in energy for cooling. The city with the most significant benefit in reducing the incidence of low temperatures and the heating demand is Toluca, followed by Zacatecas, which are cities showing prevalent heating demands. Based on the results, it is observed that it is viable to apply foundations with a lower overall heat transfer coefficient, with the addition of lightened materials produced on-site. However, it is still necessary to conduct more experimental studies on these prefabricated systems applied to foundation slabs and care for their implementation in mixed climates.

Green building future: algal application technology

In the context of rising global energy demands driven by population growth and urbanization, the construction industry significantly contributes to greenhouse gas emissions during the construction phase and subsequent energy consumption. Fossil fuel dependency for heating and energy needs exacerbates climate change, necessitating urgent solutions. Algal technology emerges as a promising strategy for green building practices, addressing energy efficiency and emissions reduction. Algae's unique ability to absorb carbon dioxide (CO2) through photosynthesis is harnessed by deploying photobioreactors on building exteriors. Studies indicate that each kilogram of dry algae consumes 1.83 kg of CO2 while offering applications as organic fertilizer, oil, and protein sources. This technology not only diminishes CO2 emissions but also transforms wastewater and generates bioenergy, catering to building energy requirements. Algal technology's economic and environmental significance becomes evident through carbon capture, energy generation, and circular waste management, aligning with sustainability principles. This study highlights the potential of algal technology to shape the future of eco-friendly conscious construction practices, providing avenues for reduced emissions, efficient energy utilization, and sustainable development.

Physico-mechanical properties and thermal monitoring performance of thermal enhanced cement slurry-coated LWAs containing microencapsulated phase change material

Over the past decade, phase change materials (PCMs) have emerged as promising solutions for thermal energy storage (TES) systems, aimed at minimizing heating and cooling energy requirements in buildings. Nevertheless, despite their potential, there are some significant challenges in effectively integrating PCMs into building components. As part of this study, lightweight aggregates (LWA) were coated with a cement slurry containing microencapsulated phase change material (MPCM) to produce lightweight concrete (LWC) with the aim of investigating its mechanical and thermal properties. The LWAs were coated with MPCM at proportions of 2.5%, 5%, and 7.5% of their weight, and an LWC-MPCM was produced using these coated aggregates. The LWC-MPCM exhibited a decrease in dry unit weight up to 1248 kg/m3 and a reduction in thermal conductivity up to 0.60 W/mK with negligible loss of strength. SEM examinations revealed that the cement slurry coating provided strong adhesion to the aggregates, resulting in a robust concrete-aggregate interface. The room with the LWC-MPCM experienced a decrease of approximately 0.23 °C in center temperature compared to the reference room during the daytime. Additionally, after sunset, the LWC-MPCM showed an increase of approximately 1 °C in room center temperature. These advantageous physico-mechanical and thermal properties establish LWC-MPCMs as promising and energy-efficient components for producing thermo-regulative building materials.

Building renovation plan - introducing energy and cost into the managerial perspectives: A case study

Implementing more stringent energy requirements for public buildings has the potential to expedite the renovation rate of building stocks. While many studies have explored optimal renovation solutions to reduce energy consumption and cost, few have established a comprehensive building portfolio strategy from the perspective of portfolio managers. This study addresses this gap by developing a framework that empowers building portfolio managers to make informed decisions regarding building renovations. Through an in-depth case study, the current workflow for determining renovation needs, which involves analyzing maintenance requirements, building usability, and adaptability, is enhanced by incorporating a cost-optimal renovation framework. The study utilizes building simulations and life cycle cost analysis to assess the energy reduction potential of six school buildings requiring renovation. The results demonstrate a potential for energy use reduction, ranging from 11% to 44% when incorporating energy-saving measures into the renovations. Cost-optimal solutions are achieved for two buildings, resulting in energy reduction ranging from 31% to 36% and cost reduction ranging from 7% to 8%. The primary contribution of this research is developing a framework that reduces ambiguities of which buildings a portfolio manager should prioritize for renovation. By identifying current performance and improvement potential, the framework enables benchmarking against energy performance classes, aiding in strategic decision-making. However, the study also underscores the complexities of assessing a diverse building stock from a bottom-up perspective. Barriers such as obtaining high-quality data, scaling assessments, time constraints, and knowledge transfer are problems to overcome. Additionally, evaluating various building typologies with diverse characteristics, including those with legal restrictions due to heritage status, presents unique challenges. Future research directions should focus on expanding the framework to incorporate a life cycle assessment perspective. This approach will ensure a comprehensive building portfolio strategy that mitigates the risk of sub-optimal solutions.

Novel approach to energy consumption estimation in smart homes: application of data mining and optimization techniques

Buildings account for a significant portion of total energy consumption, and the introduction of intelligent buildings represents a significant step forward in efficiently managing energy utilization. The proposed solutions represent a significant step forward in the development of intelligent residential environments. Beginning the process of achieving improved building intelligence necessitates a thorough evaluation and prediction of the necessary heating and cooling energy requirements, taking into account all relevant influencing factors. This study describes methodologies for using data mining models to predict the heating and cooling energy requirements of intelligent buildings during the construction phase. Data mining techniques, specifically Support Vector Machines (SVM) and Random Forest, are used, demonstrating their superior efficiency over alternative methods. Metaheuristic algorithms, particularly the Owl Search Algorithm (OSA), are described as effective tools for optimizing results across a wide range of problem resolutions. OSA is described and proposed alongside novel data mining methods, demonstrating that this combination of algorithms improves the performance of Random Forest and SVM-based models by 11% and 24%, respectively. The proposed models can generate predictions with a small number of parameters, eliminating the need for complex software and tools. This user-friendly approach makes the prediction process more accessible to a wider audience. While specialized equipment and professional-grade tools will be used, the proposed models are accessible to a wide range of individuals interested in participating in the prediction process.

A data-driven framework for fast building energy demand estimation across future climate conditions

Abstract The rapid and precise forecasting of building energy requirements plays a crucial role in decision-making processes for architects during the early design phase. This study introduces a data-driven framework capable of projecting energy demands in the context of evolving climate conditions. We evaluated four widely-used machine learning algorithms. Our results indicated that a hybrid approach, integrating Catboost and Bayesian optimization, excelled in both accuracy and efficiency for predicting building energy demand under climate change conditions. The framework proposed herein has potential applications in fostering sustainability in early-stage architectural design.

Validation of methods for developing typical meteorological years based on future climate models

You Buildings designed for coming decades are more susceptible to global warming, rising average temperatures, and heat waves, which are of a greater risk than in the 1990s. Simulation-based design decisions may be inaccurate if just historical data is used to generate climate files. The building's energy performance, cooling and heating system’s design, and room unit sizing require reliable weather data. So far mainly historic meteorological data has been used for simulations. Annual simulations and cooling/heating system design often use a typical meteorological year (TMY) based on historical data using ISO 15927-4 and ISO 15927-2 thorough instructions for TMY for annual simulations and design day data. The above climate models lack the data needed to construct TMYs for building performance simulation (BPS) tools. Climate model forecast accuracy must also be examined from the BPS perspective. This work aims to illustrate and test methods for producing TMYs using three climate models with different resolution and modality in Tartu, Estonia, from 1985 to 2015. The ISO 15927-4 approach selected months from historical meteorological data appropriate for each climate model. Three BPS TMYs were created, and a reference building's energy needs simulated. Long-term historical data was used to simulate the reference building's energy requirement and develop TMYs. The results showed that all the TMYs developed based on NorESM, MPIESM and ECEarth climate models can represent well the energy needs obtained with the TMY based on historical weather data depending on the TMY generation method while MPIESM shows deficiency in cooling season. TMY based on NorESM have the most consistent energy need simulation results while TMY based on ECEarth also performs well both for predicting cooling and heating needs. The future work includes finding other suitable methods for TMY generation that results in even less errors for all models and further validation with other reference buildings for final climate model selection to develop future climate scenarios.

Analysis of the relevant technical characteristics in indoor comfort terminal units for the purpose of satisfying the demanding energy, functional, hygienic and sustainability requirements of NZEB and ZEB buildings

With a growing number of countries, cities and organizations committing to a carbon neutral society by the middle of this century, zero and nearly zero energy buildings (NZEB & ZEB) and nearly zero carbon emissions buildings (NZCB) are becoming the new standard. These buildings with high energy efficiency and low carbon footprint, together with numerous advantages, lead to the materialization of new needs that have a significant impact on the comfort terminals units located in the various indoor environments. In fact, we are witnessing a growing concern for the phenomena of overheating and in general we are already starting to notice that air conditioning and dehumidification systems, in most cases, must be started already in spring and used until late autumn. Heating, on the other hand, becomes less relevant in quantitative terms, thanks to the greater degree of insulation of the building envelope, but thermal inertia can become a problem. In these new conditions, dictated by constructions with a high energy efficiency criterion, it is becoming strategic to have a new generation of indoor comfort terminals units available that take into account the stringent energy, functional, hygienic and sustainability requirements of decarbonized buildings. In this context, present paper focuses on the analysis of what the relevant technical characteristics could be in indoor comfort terminal units to meet the requirements of NZEB and ZEB in the coming decades.

Building simple mathematical models to calculate the energy requirements of buildings

This work looks to build a predictive mathematical model that can provide a first approach to a building’s energy requirement (ER) value in a temperate continental climate. The aim is to contribute to the theoretical knowledge of energy assessment tools. To do this, parametric simulations were run and processed using the EnergyPlus 9.5 and JePlus programs. The results were then used as a dataset to build different mathematical models, using the SageMath program to run equations that predicted the ER of each scenario. Work was done with the models, scaling their complexity with the methods and the number of parameters used. Finally, a model with a low error (0.08) and 15 parameters was chosen. It was noted that, although increasing the number of parameters brought the models closer to a 0.02 error, there was a risk of overfitting. The chosen model seeks to incorporate dynamic simulations' accuracy and validity into a simple prediction tool that construction professionals can apply.

Development of a building information modelling based decision-making framework for green retrofitting of existing buildings

In recent years, sustainable development has received more attention in the construction industry along with lowering carbon emissions. As a result, green retrofitting of existing buildings has gained significant attention, which is effective in improving energy optimization and reducing environmental impact. Though there are many advantages and opportunities for implementing green retrofitting in existing buildings, the adoption of these practices remains relatively limited. Building Information Modelling (BIM) has become a game-changing technology in the construction industry and has been widely used for sustainability analysis of buildings and also used for retrofitting of existing buildings. However, the application of BIM for green retrofitting of existing buildings is unexplored. Hence, this paper aims to develop a framework for integrating BIM with green retrofitting to obtain suitable green retrofitting measures for existing buildings. The developed framework integrates sustainable goals with retrofitting strategies and facilitates decision-making on various green retrofitting measures. The developed framework is validated through a case study of an existing building and uses decision-making tools such as a pairwise-comparison matrix and priority matrix for the identification and selection of the best green retrofitting measures. On the application of the developed framework in a case study, 13 green retrofit measures were identified and three suitable retrofit measures were selected. This enabled the reduction of building energy requirements by 37 % and improved water saving by 7.2 %. This framework helps stakeholders, to visualize, analyze, and optimize various retrofit scenarios and make well-informed decisions on suitable retrofitting approaches. The framework can be implemented in any type of existing building and enables the identification of best retrofitting measures.

Comparative analysis of metaheuristic optimization approaches for multisource heat pump operation

Through the use of effectively unlimited energy from the environment, contemporary heat pump installations have proven to be a highly efficient means for satisfying both heating and cooling needs. As such, they are often regarded as a key driver in the electrification process of the thermal domain. Keeping in mind the significant share of the thermal demand in building energy requirements, this aspect in particular is projected to have a great impact on the overall energy efficiency improvement of buildings and increase of demand-side flexibility. The latter point is considered crucial for improving the precarious balance against the contemporary intermittent power supply obtained from a variety of renewable sources. However, in the process of increasing their efficiency, heat pump systems are becoming significantly more complex with combinations of various different sources of energy from the ground, air and the Sun. By extension, the complexity of managing their operation has also increased and proves to be a challenge even for experienced plant operators. In order to aid in this regard, relevant literature suggests that optimization algorithms can be considered as a viable tool. This paper explores the limited extent of existing state-of-the-art approaches for hybrid heat pump control via metaheuristic optimization and broadens the subject matter with a comparative study that analyzes and benchmarks several metaheuristic approaches found in the literature on similar problems. A real-world multisource heat pump installation in Ferrara is utilized as the basis for the study.

Predictive modelling of heating and cooling degree hour indexes for residential buildings based on outdoor air temperature variability

Heating and cooling degree hours (HDH and CDH) are weather-based technical indexes designed to describe the need for energy requirements of buildings. Their calculation is the simplest method to estimate energy demand, providing the pattern of internal temperature variations in a building in response to weather conditions. The aim of the study is HDH and CDH prediction for Wrocław, Poland, based on outdoor air temperature using machine learning methods: artificial neural networks and support vector regression (ANN and SVR). The key issues raise in the study are: a detailed analysis of the most significant temperature lags (from 1 to 24 past hours) serving as predictors for modelling and an assessment of the impact of the database clustering on its accuracy. The best results are obtained with the clustering approach. The best predictor is the outdoor temperature observed 1 and 24 h before forecast demand (R2 = 0.981 and 0.904 for heating degree and cooling degree hours indices, respectively). Models with the highest quality are created using ANN, and the lowest with SVR. Prediction of heating/cooling degree hour indices provides building demand in advance, does not require knowledge about its characteristics, and expresses the possible impact of regional climate modifications.