Building Energy Use Research Articles

A Comparative Energy Analysis of Dynamic External Shadings for Office Buildings

Abstract In this paper, the energy performance of dynamic instead of static shading devices is investigated for prototypical US office spaces. Specifically, six dynamic shading systems are considered to assess their ability to reduce heating and cooling thermal loads, total building energy use, and electrical peak demand. The most promising dynamic shading is the rotating overhang and is evaluated further using a wide range of design and operation conditions. It is found that rotating overhangs can provide effective and easily controllable dynamic shading systems for windows to reduce the energy consumption of US office buildings by up to 39% especially for mild US climates. Moreover, the rotating overhangs have the added benefit to support PV panels for on-site electricity generation. A series of sensitivity analysis results indicate that the performance of the dynamic external shading systems depends on its depth and orientation, the location climate, the window size, and the glazing type have significant impacts. For instance, the dynamic shading device can reduce annual heating and cooling energy end-use for an office space located in Boulder, CO, compared to no shading case by 19% for a window-to-wall ratio (WWR) of 15% and by 31% for a WWR of 30%.

Research Paper on High-Performance Energy Efficient Building

Abstract: Energy is an important tool for the overall development and improvement of the quality of life. To make energy an efficient building means using less energy for doing the same amount of work. The meaning of energy efficiency is to reduce energy consumption, reduce greenhouse gases (carbon emission) and global warming and decrease the dependence on fossil fuel by taking the benefit of natural resources and minimizing the waste of energy. Due to the increase of world population, it is nearly about 40% of the annual energy consumed in the world in building today. In India, buildings are responsible for onethird of total energy consumption today. The use of building energy will be continuously growing by rapid population growth. The case study focuses primarily on three types of bricks: red brick, concrete brick, and autoclaved aerated blocks. We find that Concrete bricks have the highest cooling load and AAC Blocks have the lowest. As a result of this, we can con The peak cooling load has been reduced by 60% in comparison, so it is a more preferred choice than the traditional ones determine that AAC Blocks require less cooling to maintain comfortable room conditions, resulting in a reduction in the use of electricity. If we are to meet a sustainable goal then we have to decrease the use of energy for space heating and cooling and in environmental terms, buildings are used to maintain thermal comfort. By making an energy-efficient structure sure Selecting a proper material and by analyzing in AutoCAD and Autodesk Revit software. Changing the material of the structure and analyzing the thermal insulation of the structure. Keywords: Energy Efficient building, Thermal Insulation, Energy consumption.

Archetype Modeling Uncertainty and Bayesian Calibration for Campus-scale Analysis of Seoul National University Buildings Energy Use

Purpose: Considering the gap in research on uncertainties in urban building energy modeling, with specific regard to archetype building development, this study seeks to first throw light on archetype modeling’ sources of uncertainty, and second, solve these issues with the Bayesian calibration method. Method: By using a case area of Seoul National University, this paper models five 3D archetype buildings and estimates their monthly energy use intensities on EnergyPlus. The simulation results are modified with the Bayesian calibration technique, compared against existing, observational data before and after the Bayesian calibration, and further validated by computing the coefficient of the variation of the root mean square error. Result: Findings show that the Bayesian calibration may be considered to address uncertainty as it improves the simulation results; however, it is not a one-size-fits-all solution to uncertainties, caused by the lack of some parameters, not added to the model, which are crucial to the archetype characterization. Overall, this paper contributes to the research on urban building energy modeling-related uncertainties, hoping for urban planners to consider sources of uncertainty during the archetype characterization stage and make the adequate modeling decisions in the name of sustainable urban growth.

Nonlinear model predictive control for the space heating system of a university building in Norway

Space heating accounts for a significant share of a building's energy use; moreover, occupants' desires for better thermal comfort may further increase the energy use and peak load of the space heating system, as well as its total energy cost. The goal of this study was to use an optimal control technique: model-based predictive controller, to improve the energy and economic performance of space heating systems with satisfying indoor temperature. The proposed optimal controller incorporated the dynamic energy prices, the disturbances from weather and occupancy, as well as a nonlinear system dynamic model that considered the building thermal mass as thermal energy storage. The model-based predictive controller was tested by simulation on a university building in central Norway, and its control performance was compared to a conventional rule-based control approach, which is currently employed by the case system. The model-based predictive controller's effectiveness was demonstrated by a 2.8% reduction in heat use, 8.8% shaving in peak load, 3.8% saving in heating cost, and a 59% drop in indoor temperature violation. Furthermore, a sensitivity study revealed that the model-based predictive controller still maintained high energy and economic performance even under lightweight building constructions with decreased building thermal mass.

MARTINI: Smart meter driven estimation of HVAC schedules and energy savings based on Wi-Fi sensing and clustering

HVAC systems account for a significant portion of building energy use. Nighttime setback scheduling is an Energy Conservation Measure (ECM) where cooling and heating setpoints are increased and decreased respectively during unoccupied periods with the goal of obtaining energy savings. However, knowledge of a building’s real occupancy is required to maximize the success of this measure. In addition, there is the need for a scalable way to estimate energy savings potential from ECMs that is not limited by building specific parameters and experimental or simulation modeling investments. Here, we propose MARTINI, a sMARt meTer drIveN estImation of occupant-derived Heating, Ventilation and Air Conditioning (HVAC) schedules and energy savings that leverages the ubiquity of energy smart meters and Wi-Fi infrastructure in commercial buildings. We estimate the schedules by clustering Wi-Fi derived occupancy profiles and, estimate energy savings by shifting ramp-up and setback times observed in typical operational/static load profiles that are obtained by clustering smart meter energy profiles. Our case-study results with five buildings over seven months show an average of 8.1%–10.8% (summer) and 0.2%–5.9% (fall) chilled water energy savings when HVAC system operation is aligned with occupancy. We validate our method with results from Building Energy Performance Simulation (BEPS) and find that estimated average savings of MARTINI are within 0.9%–2.4% of the BEPS predictions. In the absence of occupancy information, we can still estimate potential savings from increasing ramp-up time and decreasing setback start time. In 51 academic buildings, we find savings potentials between 1%–5%.

Editorial: Modeling Occupant Behavior and Quantifying Its Impact on Building Energy Use

Editorial: Modeling Occupant Behavior and Quantifying Its Impact on Building Energy Use

Comprehensive Energy Consumption of Elevator Systems Based on Hybrid Approach of Measurement and Calculation in Low- and High-Rise Buildings of Tropical Climate towards Energy Efficiency

Rapid population growth and urbanization contribute to an ever-increasing global energy demand, of which the building sector accounts for one-third. The increasing average height and density of buildings escalate the need for vertical transportation, expanding elevator usage and energy needs. This phenomenon accounts for a significant amount of the total building energy use, necessitating a study of elevator system energy consumption. This study aimed to analyze the energy consumption and carbon emissions of elevator systems in low- and high-rise buildings towards energy-efficient estimations. A comprehensive analysis was performed based on a hybrid approach of measurement and calculation using a formula and reference values derived from previous studies. Four buildings were selected and thoroughly studied, representing the low- and high-rise categories. Data were collected based on on-site sampling and observation, as well as information from the building management offices. The mechanical parameters of the elevator system in each building and operational factors in terms of speed, number of trips, load, travel distance, and time were studied. In this analysis, the energy consumption calculation was performed according to International Standard ISO 25745. Annual carbon emissions were calculated in accordance with the USA EPA and IPCC guidelines. The elevator energy efficiency class was determined based on daily energy consumption. It was found from this study that the annual energy consumption of an elevator system is positively correlated to an elevator’s daily energy consumption. The annual carbon emissions of the elevator systems are dependent on increasing annual energy consumption, which is also connected to building height indirectly. The low-rise buildings showed better energy efficiency compared to the high-rise buildings due to lower travel distance, less trips, and fewer floors. The annual number of trips, travel distances, and energy consumption had an effect on the energy efficiency of the elevator systems in this study.

Future climate scenarios and their impact on heating, ventilation and air-conditioning system design and performance for commercial buildings for 2050

Designers of commercial building's heating, ventilation and air-conditioning (HVAC) systems use typical weather data adjusted with global climate models (known as “morphing”) to obtain data reflecting climate change. Previous studies have found that climate change will increase annual cooling energy by 27–47% and peak cooling demand between 28 and 59% by 2070, but have not explored 2050 timeframes, extreme weather scenarios, nor examined thermal comfort beyond degree-day assessments. This study investigates the use of extreme weather datasets in HVAC building simulation to assess traditional HVAC system sizing methods under extreme conditions, as climate change will lead to more severe and frequent extreme weather events. This study also aims to quantify the impact of climate change on energy use, peak demand, and thermal comfort for a typical commercial building in the four most-populated Köppen-Geiger climates. A typical commercial office building was modelled in OpenStudio using Energy-Plus Weather (EPW) datasets using eight extreme weather scenarios constructed from historic weather data for each climate. Two downscaling morphing methodologies were used to prepare future weather datasets for 2050. The results show datasets with higher variability increase peak cooling demand up to 35% and unmet cooling hours up to 189%, a significant increase over a shorter timeframe than previously reported. A methodology to include extreme hot and cold weather conditions and datasets, in addition to typical conditions, is proposed to future-proof HVAC system design against the impacts of climate change across the most populated global climates.

Impact of the COVID-19 pandemic on the energy performance of residential neighborhoods and their occupancy behavior.

Several contrasting effects are reported in the existing literature concerning the impact assessment of the COVID-19 outbreak on the use of energy in buildings. Following an in-depth literature review, we here propose a GIS-based approach, based on pre-pandemic, partial, and full lockdown scenarios, using a bottom-up engineering model to quantify these impacts. The model has been verified against measured energy data from a total number of 451 buildings in three urban neighborhoods in the Canton of Geneva, Switzerland. The accuracy of the engineering model in predicting the energy demand has been improved by 10%, in terms of the mean absolute percentage error, as a result of adopting a data-driven correction with a random forest algorithm. The obtained results show that the energy demand for space heating and cooling tended to increase by 8% and 17%, respectively, during the partial lockdown, while these numbers rose to 13% and 28% in the case of the full lockdown. The study also reveals that the introduced detailed occupancy scenarios are the key to improving the accuracy of urban building energy models (UBEMs). Finally, it is shown that the proposed GIS-based approach can be used to mitigate the expected impacts of any possible future pandemic in urban neighborhoods.

Design of the passive solar house in Qinba mountain area based on sustainable building technology in winter

This paper aims to make better use of solar energy through the application of passive design concept. The test results show that the average temperature of the case building is about 7.2 °C, indicating that the indoor thermal comfort of rural houses in Qinba mountain area is poor in winter For the purpose of improving the indoor thermal comfort of rural buildings in this area, the simulation is carried out by using ANSYS. CFX software, and the effects of direct benefit solar house and additional solar house are compared. The results reveal that adding additional solar space can achieve better results. The average temperature of the first floor is increased by 1.1 °C ∼1.4 °C and that of the second rises by 0.7 °C ∼1.1 °C. Compared with the original building, the energy saving rate of the building with additional solar space in the heating period is more than 50%. Therefore, the additional solar house can be promoted as a low-energy heating measure for rural buildings in this area. The promotion of additional solar house will certainly play an important role in improving the indoor thermal environment of residential buildings reducing energy use in building and promoting green buildings.

Advocating for view and daylight in buildings: Next steps

With the exponential growth in population and commensurate increased density in urban cities, access to daylight and views to nature is being severely curtailed in buildings. In parallel, increasingly urgent demands to sharply reduce building energy use and associated greenhouse gas emissions are being made to mitigate climate change. There are many challenges and performance tradeoffs associated with the building facade (i.e., daylight and view versus solar and glare control); increased prioritization of health and well-being as a fundamental human requirement could adversely affect building energy-efficiency. Given the current state of knowledge on the effects of daylight and view on health and well-being in buildings, we identify critical needs in research, tools and technologies that if satisfied may enable more effective use of daylight and view in buildings within the constraints of climate change. Lack of knowledge regarding the complex causal mechanisms of window views on human factors is a severely limiting factor in forward progress. Current models and methods to derive bidirectional scattering distribution functions (BSDFs) will need to be modified. Developers of energy-efficient window technologies will need more guidance to shape product development. Advanced window technologies and integrated design can enable attainment of both health and well-being and net zero energy goals, but considerable work will be needed to make such options turnkey and broadly available.

Performance Comparison and Analysis of the Curtain-Wall-Type Liquid-Type Photovoltaic Thermal Unit According to the Pipe Connection Method

Recently, there has been increasing attention on the use of renewable energy in buildings, particularly, the photovoltaic thermal (PVT) system that uses both solar power and thermal energy. However, there is a limit to adopting the PVT system in real buildings because many architects value the aesthetics of buildings or spaces. This study developed a curtain-wall-type liquid-type PVT (CW-PVT) that can be installed on a wall as it integrates with the building. To analyze the system performance, a real-scale experimental plant was established in an outdoor environment. The performance of the CW-PVT unit was verified for two different module pipe connection types: parallel and serial. Meteorological variable data, the inlet and outlet fluid temperatures, surface temperature, and electrical energy generation of the modules were measured and collected using the measurement equipment according to the module pipe connection type. Consequently, the parallel-type method was approximately 10% more efficient than the serial type in energy production, whereas the serial-type method produced water with a temperature approximately 47% higher than that of the parallel type. Notably, it was advantageous to apply the parallel-type connection to maximize the energy generation efficiency in buildings where the system efficiency is vital and the serial-type connection in buildings where the high temperature of hot water is required.

Spatio-temporal heterogeneity analysis of energy use in residential buildings

Conventional building energy dynamic analysis relies on climatic and geographical statistics of buildings. A large number of studies focus on investigating the major influencing factors and the energy use with engineering methods and statistical methods to predict buildings' energy use. However, due to the lack of temporal statistics when analyzing buildings' energy use intensity (EUI), very few studies concerned the energy consumption from spatial and temporal heterogeneity simultaneously. This study extends the traditional regression model by introducing the temporal variations into the geographically weighted regression (GWR) model and developed a geographically and temporally weighted regression (GTWR) model. A case study, which is based on four years’ urban building energy consumption (from 2015 to 2018) in the city of Seattle, is used to validate the performance of the proposed regression model. Meanwhile, the ordinary least square (OLS), GWR, and temporally weighted regression (TWR) models are developed for making cross-comparison of the goodness-of-fit. The results show the GTWR and TWR models reduce the mean absolute error by 13% and 7%, respectively, relative to the GWR model. Moreover, the GTWR model has a substantial improvement of goodness-of-fit, which can be referred from the value of R2 increasing by 49% in OLS, 16% in GWR, and 6% in TWR. Two driving forces, the number of floors (NF) and energy stars (ES), are identified to have the most significant coefficients in evaluating the site EUI. The novelty of this research lies in achieving better reliability and accuracy in building energy modeling by GTWR, which provides an assisting tool for government and local authorities to make the master plan and sustainability strategies for the new buildings in the city.

Energy production features of rooftop hybrid photovoltaic–wind system and matching analysis with building energy use

Rooftop photovoltaic (PV)–wind hybrid systems serve as a promising energy supply source to mitigate environmental concerns and satisfy high energy demands. Most of energy matching studies focused on the matching capability of photovoltaic generation with building load, and the application of wind power to complement PV was rarely considered. In the context, this study explored the production aspects of PV–wind turbine (WT) hybrid systems and their potential to meet building energy use demands in densely built urban areas. Both solar and wind resources in 18 cities in eastern China were classified into three energy output levels, and Hangzhou was selected as a representative city for analysis of the complementarity of the two resources. Based on the modeling of hybrid PV–wind system generation, a PV/WT production feature curve was generated by k-means clustering. The matching between renewable energy production and building energy use and the influences of different wind resources and building heights were analyzed for rooftop hybrid systems in two typical residential buildings. The results reveal that the wind and solar resources in Hangzhou are partly complementary in terms of time sequence and power output. For the multi-story and high-rise residential building considered, a rooftop hybrid PV–WT system was concluded to be beneficial in improving energy generation, reliability, and load matching performance in a limited space. The technical performance for the high-rise case was inferior to that for the multi-story case considered. In the case of residential buildings, the matching between on-site supply and load is influenced by the local wind resources and building height. The richer the urban wind resource, the greater the load cover ratio and the self-sufficiency of the installed rooftop hybrid system. However, the load matching performance declines with increasing building height. The proposed general method for analyzing rooftop hybrid PV–WT systems can enable widespread and efficient solar and wind energy utilization in densely populated and built urban areas.

Prediction of energy use intensity of urban buildings using the semi-supervised deep learning model

Prediction of building energy performance is a critical strategy for building energy management. Extant studies established city-scale prediction models only based on buildings with energy data. However, building energy data in most cities is limited, which may impair model performance. A large number of unlabeled buildings (without energy data) may reveal important energy use knowledge, but few studies have explored their capability to improve building energy prediction. Therefore, a novel semi-supervised deep learning method, namely dynamically updated multi-fold semi-supervised learning method based on deep neural networks (DUMSL-DNN) is proposed to predict building energy use intensity (EUI) by utilizing unlabeled samples. Manhattan is selected as a case study, which contains 4854 labeled samples and 34,456 unlabeled samples. Compared with the optimal DNN model, DUMSL-DNN can improve building EUI prediction with root-mean-square error (RMSE) reduced by 9.36% and mean absolute error (MAE) reduced by 9.43%. The DUMSL method is superior to typical semi-supervised learning methods with the lowest RMSE of 0.5207 and the lowest MAE of 0.3325. By the implementation of DUMSL-DNN, more areas with high EUI are identified in Manhattan. Specifically, commercial buildings and residential buildings built before 1965 have higher EUI. Measures are accordingly proposed to improve building energy efficiency.

Leadership and Deliberate Action

The bipartisan Infrastructure Investment and Jobs Act (IIJA), signed into law in 2021, includes as much as $65 billion in funding to support the private sector, municipalities, and states in modernizing energy infrastructure, including deploying clean energy at scale, reducing greenhouse gas (GHG) emissions, and creating clean energy jobs. The IIJA aligns well with states' policies and plans as they seek to generate 100 percent of electricity from clean energy resources within the next one to two decades. Plans vary by state, but the goals to decarbonize electric generation, and energy use in buildings and transportation are common among the most aggressive states.

Effect of Architectural Building Design Parameters on Thermal Comfort and Energy Consumption in Higher Education Buildings

It has been challenging for designers to identify the appropriate design parameters that would reduce building energy consumption while achieving thermal comfort for building occupants. This study aims to determine the most important architectural building design parameters (ABDPs) that can increase thermal comfort and reduce energy use in educational buildings. The effect of 15 ABDPs in an Australian educational lecture theatre and their variabilities on energy consumption and students’ thermal comfort for each parameter were analysed using Monte Carlo (MC) techniques. Two thousand simulations for every input parameter were performed based on the selected distribution using the Latin hypercube sampling (LHS) technique. Sensitivity analyses (SA) and uncertainty analyses (UA) were used to assess the most important ABDPs in terms of thermal discomfort hours and energy consumption. The study found that the ABDPs, such as cooling set-point temperatures and roof construction, significantly reduce the operative temperature by up to 14.2% and 20.0%, respectively. Consequently, these reductions could significantly shorten the thermal discomfort hours, thereby reducing energy consumption by 43.7% and 41.0%, respectively. The findings of this study enable building designers to identify which ABDPs have a substantial impact on thermal comfort and energy consumption.

Green Architecture Concepts Applied to Wanakota Apartments

The shrinking land in urban areas and the worsening world climate crisis have created a new residential development problem. One way to maximize land designated for residential use is to build vertical housing, such as constructing flats and apartments that can accommodate more residents in a limited area. However, it also creates new problems because the use of energy required for a building will be extensive. The application of Green Architecture is an effort to reduce energy use for buildings and reduce the negative impact of buildings on the environment. one helpful reference is applying green architectural concepts. The apartment Wanakota is designed using the green building concept with the Indonesian Green Building Council (GBCI). Wanaka means City Forest, and trees inspire this name as trees. This tree is not only an aesthetic enhancer but as a CO2 absorber and oxygen supplier in the environment.

Characterizing the theory of energy transition in Luxembourg—Part three—In the residential sector

AbstractIncreasing consciousness to address future concerns is a challenging subject, noticed considerably by policymakers and investors. Investigation of energy use in residential buildings in terms of quantity and sources of energy was the main focus of this study. Indeed, the paper was aimed to study the role of contextual factors on two points: the energy demand and the spreading of renewable technologies in the residential sector. It was theory‐driven research to highlight some future concerns. The first theory was to reveal how the growth of single‐family houses versus multi‐family houses could affect the growth of energy consumption and the application of renewable technologies. The second theory was built to highlight that spreading energy measures to increase efficiency or introducing a new energy carrier should be embedded in the context of new construction activities or in a template of renovation projects. The third theory was to emphasize the priority of thinking about managing the changes in residential buildings because of the lasting impact of the decision about the type of building. Finally, the purpose of the ongoing transition was argued to highlight that the growth of renewable technologies has not been necessarily aligned with getting away from fossil fuels.

Advanced Carbon Reinforced Concrete Technologies for Façade Elements of Nearly Zero-Energy Buildings.

The building sector accounts for approx. 40% of total energy consumption and approx. 36% of all greenhouse gas emissions in Europe. As the EU climate targets for 2030 call for a reduction of greenhouse gas emissions by more than half compared to the emissions of 1990 and also aim for climate neutrality by 2050, there is an urgent need to achieve a significant decrease in the energy use in buildings towards Nearly Zero-Energy Buildings (nZEBs). As the energy footprint of buildings includes the energy and greenhouse gas consumption both in the construction phase and during service life, nZEB solutions have to provide energy-efficient and less carbon-intensive building materials, specific thermal insulation solutions, and a corresponding design of the nZEB. Carbon reinforced concrete (CRC) materials have proven to be excellent candidate materials for concrete-based nZEBs since they are characterized by a significantly lower CO2 consumption during component production and much a longer lifecycle. The corresponding CRC technology has been successively implemented in the last two decades and first pure CRC-based buildings are currently being built. This article presents a novel material system that combines CRC technology and suitable multifunctional insulation materials as a sandwich system in order to meet future nZEB requirements. Because of its importance for the life cycle stage of production, cost-efficient carbon fibers (CF) from renewable resources like lignin are used as reinforcing material, and reinforcement systems based on such CF are developed. Cutting edge approaches to produce ultra-thin lightweight CF reinforced concrete panels are discussed with regard to their nZEB relevance. For the life cycle stage of the utilization phase, the thermal insulation properties of core materials are optimized. In this context, novel sandwich composites with thin CRC layers and a cellular lightweight concrete core are proposed as a promising solution for façade elements as the sandwich core can additionally be combined with an aerogel-based insulation. The concepts to realize such sandwich façade elements will be described here along with a fully automated manufacturing process to produce such structures. The findings of this study provide clear evidence on the promising capabilities of the CRC technology for nZEBs on the one hand and on the necessity for further research on optimizing the energy footprint of CRC-based structural elements on the other hand.