Building Energy Demand Research Articles

Enhancing building energy efficiency through a climate adaptive design: Considering upcoming extreme climate conditions

Known as one of the significant sources of impacting building energy efficiency, climate change can drastically alter the long-term energy consumption patterns of buildings. It is widely acknowledged in the literature that designs based on conventional pathways relying solely on historical weather data disregard the effects of the changing climate and lose their intended energy performance over time. Several endeavors have been made to generate weather files capable of capturing the essence of the changing climate and carrying its variability. In line with these efforts, the synthesized weather files have emerged as a promising alternative to the commonly used morphing method for incorporating climate change variability into building energy designs. However, a comprehensive framework with a potential to accommodate this variability in this context is yet to be investigated. Addressing this gap, the present study aims to propose a novel framework that can facilitate dynamic design upgrades in response to the changing climate including the extreme climate conditions. This framework has been applied to a case study building in Australia and around 31% annual thermal load reduction, on average, has been achieved. Furthermore, the results showed that conventional design pathways are likely to leave buildings inadequately prepared against the upcoming climate conditions. Therefore, the proposed framework is capable of generating a dynamic design that not only maintains the expected energy performance by incorporating the changing climate into its structure but also draws a more realistic picture of the building energy demand pattern over time.

Building energy characteristic evaluation in terms of energy efficiency and ecology

The assessment of energy efficiency and the environmental impact of building operation, in energy terms, determine to a large extent the energy intensity of the housing sector as a whole and the planning of energy transition measures. In order for these measures to be properly targeted and the building assessment to be reliable, it is necessary to accurately determine the energy demand of the building. The calculation model presented in the article allows for a multivariate assessment of a building's energy demand using static methods. The methodology presented in the article for the determination of energy performance certificates for buildings is complemented by an interpretation regarding the efficiency of the energy systems in the building and the environmental emissions. A model of the existing single-family building was evaluated for this purpose, and subjected to a multivariate analysis depending on the configuration of the heating system. Final and primary energy values, fuel consumption and emissions of pollutants to the environment were determined for each of total twenty-two variants. The analysis carried out and the results obtained show that there is no logical relationship between the primary energy indicator, used for the energy assessment of buildings, and the energy intensity of the building and its environmental impact.

SUSTAINABLE PRODUCTION AND MANAGEMENT OF AN INDUSTRIALIZED HOUSE

The PRO.CRE.AR. Bicentenario (Argentine Bicentennial Single-Family Housing Credit Program) implemented between 2012 and 2015 approximately, included a model of wooden houses, thus enhancing in Argentina the opportunity to have a house built with industrialized wooden elements. The paper compares at a conceptual level the construction of the same house (similar to the prototype named “Maderera”) as a traditional building, i.e., with brick masonry and reinforced concrete, and instead with industrialized wood construction as funded by the PRO.CRE.AR program: the comparison focuses on the amount of CO2 incorporated in the two constructive solutions. Alongside this assessment, conducted on the basis of estimated quantities of materials and data found in the literature, the paper examines the amount of CO2 not produced over the lifetime of the house when photovoltaic elements and solar panels are possibly adopted for local energy production and then discusses the positive impact of any energy storage systems. The results regarding the building energy demand and the CO2 saved support the choice of building industrialization for the wood supply chain, propose additional options for the deployment of wood construction, and evaluate the impact of home PV & solar panels for sustainability and integration into the technological system.

Exploring the effects of a warmer climate on power and energy demand in multi-family buildings in a Nordic climate

The need to understand how a warmer climate affects the power and energy demand in cold countries is important for urban planners and policymakers. By using data from utility bills that are commonly available today, together with outdoor temperatures, it is possible to analyze historical and future power and energy demand. The scientific value of this research includes the development of a methodology to explore effects on future heat demand in the Nordic region based on a combination of historical data, building properties, and predictions of future climate. This is achieved by using an energy signature model and regression analysis. Seventy multi-family buildings in Linköping, Sweden, are investigated from 1980 to 2050. The results show that the effects from historical variations in internal heat gains (average annual increase of 1 %) on the specific energy use for space heating (SPH) is minor for the district, i.e., less than 2 % when comparing 2020 and 1980. The opposite is found for variations in outdoor temperatures, where the average specific energy use is predicted to decrease by about 25 % in 2050 compared to 1980, with the used forecast of future climate. This corresponds to a decrease from 127 kWh/(m2·year) to 93–96 kWh/(m2·year). Additionally, the maximum heating power demand of the district is predicted to decrease by about 30 %, from 4,855 kW in 1980 to 3,468 kW in 2050. In conclusion, our results demonstrate a strong effect of decreased SPH and heating power demand in residential districts due to a warmer climate.

Transformer based day-ahead cooling load forecasting of hub airport air-conditioning systems with thermal energy storage

The air conditioning system constitutes more than half of the total energy demand in hub airport buildings. To enhance the energy efficiency and to enable intelligent energy management, it is vital to build an accurate cooling load prediction model. However, the current models face challenges in dealing with dispersed load patterns and lack interpretability when black box approaches are adopted. To tackle these challenges, we propose a novel k-means-Temporal Fusion Transformer (TFT) based hybrid load prediction model. Specifically, the daily load patterns are grouped using an improved k-means clustering method that considers both input feature weights and dynamic time warping (DTW) distances. Additionally, the statistical features of the clustering output are inputted into the TFT. By further incorporating context information, the integration of data between different schema categories is achieved, thus reducing errors that may occur during the transition process. As a result, the prediction performance and interpretability are significantly improved. The Chongqing Jiangbei Airport T3A terminal is used as a case study, and experiments are conducted using cooling data from the No.1 energy station, as well as the airport traffic data and the meteorological station data. Results are compared with other mainstream models, confirming that the proposed day-ahead load forecasting model achieves improvements in several performance indicators, including MAE, MAPE, CV-RMSE, and R2, which are 384 kW, 3%, 5%, and 0.058 respectively.

Combining use of natural ventilation, external shading, cool roof and thermal mass to improve indoor thermal environment: Field measurements and simulation study

Thermal mass, natural ventilation, external shading and cool roof are commonly regarded as passive cooling measures. Combined use of these passive cooling measures could achieve a more comfortable indoor environment, and further reduce the building energy demand. The aim of this paper was to assess the short and long-term performances of individual and combination passive measures in a cooling-dominated region of China experimentally and numerically. The field measurement revealed that natural ventilation, external shading and cool roof can reduce the internal wall and indoor air temperatures. The mean air temperatures in the test chamber were 1.3, 2.0, and 1.1 °C lower than that in reference chamber. The combined use of natural ventilation, external shading and cool roof can maintain the daily indoor air temperature within the range of 26.2 and 28.1 °C. The mean air temperature in the test chamber was reduced by 2.8 °C. Long-term simulation results confirmed that applying combination mode of natural ventilation, cool roof and external shading, the indoor temperature, varying from 21.9 to 28.1 °C, is greatly improved compared with the case operated natural ventilation only, particularly during peak temperature hours. The effect of specific heat capacity on indoor air temperature is complex. Increasing the specific heat is not always beneficial to indoor thermal environment. Further, the greater the heat transfer coefficient leads to the higher indoor air temperature through entire summer. The maximum indoor air temperature is reduced by 2.6 °C when the heat transfer coefficient changes from 1.0 to 0.1 W/m2·K.

Ranking building design and operation parameters for residential heating demand forecasting with machine learning

The European Union's Energy Performance in Buildings Directive has made significant strides in enhancing building energy efficiency since its inception in 2002. However, approximately 75% of EU buildings still fall short of energy-efficient standards. Furthermore, there is a growing momentum to extend the concept of nearly zero-energy buildings to entire districts, thereby fostering Net-Zero Energy Districts. This underscores the necessity for large-scale urban building energy modelling to identify and improve underperforming buildings and energy transition planning. Given the increasing interest in black box models for building energy performance, this study aims to identify common input variables in buildings energy demand literature, analyse their influence, and develop a heating demand prediction model using different algorithms: Random Forest, XGBoost, and Extra Trees. Four large datasets generated from white-box simulation in three Spanish cities were used for training and testing the models. Four features consistently stand out as the most important for prediction: shape factor, infiltration rate, south equivalent surface, and internal gains, regardless of the training algorithm or the climatic zone. The multi-location XGBoost algorithm with an optimizer emerged as the best-performing model, with an average Mean Absolute Percentage Error value hovering around 40%. Analysis employing SHapley Additive exPlanation (SHAP) values showcases the model's ability to identify factors that drive higher heating energy demand, alongside its strong predictive performance. This suggests potential integration of the model into programmes to identify key variables to be addressed during building renovation. Additionally, results show the potential of XGBoost-based software's potential for identifying building renovation targets.

The impact of climate change on an office building in Portugal: Measures for a higher energy performance

Even though global warming is expected to raise the energy demand in office buildings, most energy analyses and design studies neither consider its impacts nor adaptive energy solutions. This study evaluates the effects of future climate on the energy demand of an office building in Portugal and proposes mitigation measures to improve its energy performance and environmental sustainability. The study simulates the energy demand of the building in 2055 and 2090 under different future scenarios using data from the latest climate model experiments, considering systems, envelopes, and operations interventions, and assesses the potential of photovoltaic panels to meet the energy demand. The results show that the interventions may reduce electric energy consumption between 16 % and 25 %, and the photovoltaic panels can meet 23 %–59 % of the total demand, depending on the scenario and the year. However, storage technology and equipment efficiency must evolve favorably to reach low energy. The findings reveal if a building designed and optimized for the current climate will remain optimal in the future and demonstrate the benefits of applying cleaner production and sustainable consumption principles to the office building sector. The study provides valuable insights for practitioners and policymakers.

Towards a net-zero-energy building with smart control of Trombe walls, underground air ducts, and optimal microgrid composed of renewable energy systems

In recent years, permanent outages and lack of access to electricity have been significant problems in most cities of Iran. The increasing growth of fossil fuel consumption made it necessary to change the design of buildings. Designing a net-zero energy building (with natural ventilation and renewable energy systems) is a solution for reducing fossil fuels. In this paper, dynamic energy modeling of the building for the smart operation of the trapezoidal Trombe wall and underground air ducts is performed. In the first step, the Trombe Zones are considered for heating. Then the combination of the Trombe zones with underground air ducts for cooling is simulated. Then the system combination with a solar hot water system to supply hot water energy is evaluated. Finally, the economic analysis of the optimal hybrid system (with a photovoltaic system, wind turbine, battery, and inverter) for supplying the electricity of the building equipped with both passive energy strategies was evaluated by Homer software. The results show that by integrating the Trombe wall, the heating load is reduced by 44 %, and by integrating a coupled geothermal cooling system with the air duct and the Trombe wall, the cooling load is reduced by 39%. Also, the net present cost over the 25-year life of the optimized renewable system is reduced by about 12.5% due to both passive strategies. The average daily electricity energy demand of the base building is equal to 86.37 kWh and for the passive building, it is equal to 67.49 kWh.

Concentration performance evaluation of sunlight concentrators: Fresnel lens, plano-convex lens, and parabolic mirrors for daylighting application

ABSTRACT This study investigates the concentration performance of three concentrator types – Fresnel lens, plano-convex lens, and parabolic mirrors in focusing sunlight onto a 1.5 mm diameter plastic optical fiber. The evaluation uses TracePro ray tracing software for simulation and analysis, considering visible light and infrared bands. Results show that parabolic mirrors achieved the highest concentration ratio at 7720 suns, followed by the Fresnel lens at 6400 suns, and the plano-convex lens at 2750 suns for a wavelength of 535 nm, surpassing geometric concentration values of 1202 suns. Efficiency measurements revealed that the Fresnel lens achieved 86% efficiency, the parabolic mirrors reached 97% efficiency, and the plano-convex lens achieved 93% efficiency. Additionally, the parabolic mirror demonstrated potential for creating the smallest concentration spot size due to its ability to focus light on a single point. This study highlights potential applications of sunlight concentrators in enhancing natural lighting in buildings and promoting energy efficiency. The comparative analysis of these concentrators offers insights into their performance characteristics, aiding in the selection and optimization of concentrators for daylighting applications. Moreover, this research supports the concept of green and sustainable energy by demonstrating the potential of solar energy to meet building energy demand and promote daylighting as a means to reduce reliance on conventional energy sources.

Interpretable domain-informed and domain-agnostic features for supervised and unsupervised learning on building energy demand data

Energy demand from the built environment is among the most important contributors to greenhouse gas emissions. One promising way to curtail these emissions is through innovative energy management systems (EMS’s). These systems often rely on access to real-world demand data, which remains elusive in practice. Even when available, energy demand data typically suffers from missing data as well as many irregularities and anomalies. This precludes the application of many off-the-shelf machine learning algorithms for time series analysis and modelling, necessary for downstream energy management. Transforming energy demand time series to low dimensional feature matrices has been shown to work well in determining similar buildings and predicting meta-data, both of which can be used to create better forecast algorithms used as input in EMS’s. These studies are, however, often marred by the limited size of datasets, as well as the non-interpretable nature of extracted features. This paper addresses these concerns and makes several important contributions: (1) it collates several open-source datasets to create a large meta-analysis dataset containing energy demand data for over 13,000 buildings; (2) it investigates the use of different interpretable feature extraction methods on this collated dataset; and (3) it shows that this feature matrix can be used more generally to determine similar buildings and predict building properties such as missing meta-data. The large feature matrix resulting from the work is open-sourced as part of a web-based dashboard to enable the community to reproduce and further develop our results.

Historic Building Renovation with Solar System towards Zero-Energy Consumption: Feasibility Analysis and Case Optimization Practice in China

This paper aims to study the required solar panel tilt angle, area, and investment payback period for achieving zero-energy heating in historically significant courtyard-style residential buildings. The retrofitting approach involves positioning solar panels on the main building of the structure using four supports, each located at the corners, elevated from the ground and not in direct contact with the building. This approach does not alter the external envelope structure of the building, thereby preserving the authenticity of the cultural heritage. Using BESI software, we simulated the heating energy demand of the sample building. We integrated a solar heating system within the building and analyzed the optimal solar panel layout area, installation angle, and payback period for achieving zero-energy heating. This allowed the building to meet the zero-energy heating requirements. Taking the Hu Family Courtyard heritage conservation building as an example, we proposed the optimal layout plan for solar energy retrofitting.

Multi-Stage Sensitivity Analysis of the Energy Demand for the Cooling of Grain Warehouses in Cold Regions of China

The early design parameters exert a considerable influence on the cooling energy demand of a granary building in operation. In order to investigate the impact of various parameters on energy use, a grain warehouse energy model was constructed using the Ladybug + Honeybee tools on the Grasshopper platform. Three global energy sensitivity methods were used to analyze the model, and the sizes of the influential parameters were determined and ranked. The study uncovered that the cooling energy demand of the grain warehouse was primarily influenced by factors such as the cooling set-point temperature, roof solar absorptance, roof and exterior wall insulation thickness, window type, and orientation. On this basis, a local sensitivity analysis was conducted for the highly sensitive parameters to identify their influence trend and optimal design range. The results showed that the cooling energy demand of the grain warehouse increases faster as the cooling set-point temperature decreases, with the highest growth rate occurring at a temperature below 18 °C. Lower solar absorptance of the roof is conducive to reducing the cooling energy demand of the grain warehouse. When the thickness of the roof thermal insulation is less than 120 mm and the thickness of the external wall thermal insulation is less than 60 mm, energy use decreases more quickly with greater insulation thickness. It is advisable to use traditional or new windows with thermal insulation and shuttered windows. Furthermore, the optimal position of the long side of the granary was between 10° west and 10° east of north. This research could provide guidance for the energy-saving design and renovation of granary buildings in cold regions of China.

Assessing effects of future climate change on outdoor thermal stress and building energy performance in severe cold region of China

Future climate change is poised to affect the urban thermal environment and building energy performance in the severe cold region, yet its full impact remains unclear, particularly concerning extreme weather events. This study proposes a multi-scale climate-energy framework to assess the future climate's influence on outdoor thermal stress and building energy efficiency under Shared Socioeconomic Pathways for 2050 and 2080 in this region, using Harbin serving as a case study. The framework also evaluates the adaptation potential of trees and natural ventilation. The results showed that climate change may narrow the physiologically equivalent temperature (PET) range in summer daytime, and increase unacceptable heat exposure duration from 2 h to 7–8 h by 2050 and further to 5–10 h by 2080. While the overall annual building energy demands are expected to increase minimally (<12 %), cooling loads could rise substantially. Trees show diminishing returns in reducing peak PET as climate severity increases, yet can moderately reduce total energy demand (4–6 %) and peak demand (4–5 %) during extreme warm days. The energy-saving potential of natural ventilation is projected to decline, with a potential decrease of up to 40 % by 2080 in the most severe scenario. This study provides new insights into how future climate change could impact outdoor comfort and building energy in the severe cold region, and offers a versatile framework for urban sustainability planning that can be applied to other cities and climates.

Controlling the energy supply and demand of grid-connected building integrated photovoltaics considering real-time electricity prices to develop more sustainable and smarter cities

Limited resources and energy supply constraints in today's society necessitate innovative solutions based on existing potentials and the possibility of development in the upcoming years. From this perspective, to efficiently manage both the supply and demand sides of energy in buildings, this study proposes a novel approach for grid-connected building integrated photovoltaics (BIPVs) considering the price of electricity. Regarding real-time electricity prices, the presented method allows for selecting grid electricity or photovoltaics per peak, off-peak, and between these two. In addition to reducing costs, this strategy lessens photovoltaic installation capacity and increases the justification of BIPVs. Considering demand-side management, proper control in terms of economic aspects is conducted based on the system's defined model and the price of electricity. The suggested technique outperforms other applied methods when both energy supply and demand are considered, as well as real-time electricity prices. The validity of this technique is confirmed by simulation results of system performance in a setting and for lighting loads. This approach advocates for establishing smart buildings and cities in tandem with the growth of smart grids and liberalized energy markets.

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.

Improving the prediction of energy performance of building using electromagnetic field optimization algorithm

Abstract Considering the significance of proper energy performance analysis of buildings, many recent studies have presented potential applications of machine learning models for predicting buildings’ thermal loads. Some of these models have been built upon optimization algorithms in order to enhance their prediction accuracy. However, due to the importance of time in engineering calculations, the long optimization time of the hybrid models has remained a problem. In this study, a quick optimization algorithm called electromagnetic field optimization (EFO) is presented to deal with this issue. The EFO is combined with a feed-forward artificial neural network (FFANN) to predict the annual thermal energy demand (EDAT) of a residential building based on the building’s characteristics and architecture. A well-known dataset consisting of 11 inputs is used to train and test the proposed model. Additionally, nine conventional FFANNs and several hybrid machine learning are considered benchmark models to evaluate the performance of the EFO-FFANN. According to the results, the calculated mean absolute percentage errors of the EFO-FFANN in the training and testing phases were 2.06% and 1.81%, respectively. The EFO algorithm could improve the prediction accuracy of the conventional FFANNs by around 38%. Hence, the proposed model and its simplified formula can of interest to both civil and energy engineers to do informed decision-making and optimize building energy performance in real-world projects.

Energetic, economic and environmental sustainability of integrated techniques for energy production in buildings using hydrogen as storage system

In present days increasing attention must be paid to the control of the use of fossil fuels, major cause of greenhouse gas emission and climate change; particularly, a significant portion of total energy is consumed in buildings and could be significantly reduced both increasing energy efficiency and using renewable sources. At the moment, the intrinsic discontinuity of renewable energy limits its widespread use in buildings: anyway, integration of plants with energy storage systems could efficiently satisfy building energy demand. Within this frame, hydrogen has shown to be particularly fit in order to be used as an energetic carrier. In this aim in the paper the energetic, economic and environmental sustainability of an auto-sufficient system for energy production in buildings, based on photovoltaic panels, hydrogen storage and fuel cells is presented.

Effects of urban vegetation on microclimate and building energy demand in winter: An evaluation using coupled simulations

As an important landscape, the evergreen vegetation is prevailing, even in cold region cities. However, the impacts of promoting vegetation on microclimate and building heating load (BHL) are still inexplicit. This study validated the feasibility of a new-developed coupled model that linked ENVI-met and EnergyPlus, for predicting the microclimate and BHL in winter. The impacts of vegetation configuration changes on the performances of three typical urban blocks in Xi'an, a high-density city in the cold region of China, were then evaluated. When the leaf area index of green lands increased from 0.79 to 4.77 m2/m2, a decrease of 3.27 °C on mean radiant temperature, a decrease of 0.48 m/s on wind speed, and an increase of 0.47 °C on air temperature at the block scale could be observed. To the three blocks, the BHL of thermal zones in the height range of tree canopy increased by 7.16 %, 1.52 % and 3.57 % during a sunny day, and decreased by 2.80 %, 0.55 %, and 0.06 % during a cloudy day maximally. Overall, the evergreen vegetation produced negative impacts on the building energy efficiency of cold region cities. For urban planners, it's advisable to concentrate evergreen plants in high-rise blocks to mitigate such disadvantages.

CLIMATE CHANGE TRENDS AND EARLY ADAPTATION STRATEGIES: POTENTIAL IMPACTS AND SOLUTIONS FOR BUCHAREST MUNICIPALITY

This paper explores some of the emerging impacts of climate change on urban environments, specifically, in this case, on the city of Bucharest. While using 2008 to 2023 data acquired from meteoblue.com, the analysis identifies specific trends in the local climate through two indicators: (i) the number of (air temperature) degree-hours beyond or below different thresholds and (ii) the Temperature-Humidity Index (ITU). The key findings relate to some observations that can be made after processing the data and looking at the different resulting trends. On the one hand, the number of degree-hours above certain temperature thresholds seems on an ascendant trendline. On the other hand, a decreasing trendline for degree-hours below lower specific thresholds can be noticed. This suggests that an increase in energy demand for cooling and a slight decrease in energy demand for heating buildings might be expected in the following decades, something with various consequences (e.g., socio-economic, environmental, etc.). When considering the other parameter assessed, the Temperature-Humidity Index (ITU), again, an escalating trend may be observed throughout the period for which data was available. Collectively, the findings raise a flag on specific increasing trends in levels of outdoor discomfort. Although the study's findings may be preliminary and exploring additional datasets would be desirable, they highlight a possible local warming trend and the need for early-stage urban adaptation strategies to increase local climate resilience. Given the concerns raised, the concept of Blue-Green Infrastructure (BGI) is identified as an essential solution to some of the different issues brought forth while assessing the data mentioned earlier and even when acknowledging the current problems experienced by Bucharest’s inhabitants during the hot season.