Building Energy Use Research Articles

Tradeoffs in optimization of Active Insulation Systems and HVAC: A case study in residential buildings

Active insulation systems (AIS) have been conceptualized to make buildings more responsive to changing environmental conditions, creating energy efficiency and flexibility gains. Building flexibility and efficiency can be further improved if the building is also equipped with optimized HVAC system controls. Optimal control of AIS and HVAC systems can be used to activate thermal mass, and past work has quantified the potential benefits of jointly optimizing both systems. In this work, we more fully elucidate and evaluate the potential tradeoffs between optimal control of these technologies with respect to building energy use and peak demand–including different configurations of heat pump systems and integrated building thermal energy storage. Tradeoffs are revealed by applying a multi-objective optimal control framework and comparing the Pareto optimal sets across various system configurations. Discussion of the results provides insights into how buildings can be best designed and controlled to achieve both energy efficiency and flexibility.

A novel method for calculating the projected health and climate co-benefits of energy savings through 2050

Building energy use represents a major challenge toward achieving our climate goals. Energy and carbon emissions from buildings are routinely measured, but reducing carbon emissions also comes with significant health and climate co-benefits that are rarely quantified, and, if they are, only retrospectively. We developed the Co-benefits of Built Environment (CoBE) Projection tool to project the health and climate co-benefits of energy savings and emissions reductions in buildings up to 2050. CoBE uses energy and emissions data from the U.S. Energy Information Administration and Environmental Protection Agency as well as social cost of carbon to quantify the climate impacts, and reduced complexity models (InMAP, EASIUR, and AP2) to quantify the health impacts in terms of premature mortality cases attributable to PM2.5 exposure and their associated monetized health costs. Projections for year 2050 show that for every dollar of electricity savings, health and climate co-benefits add $0.02-$0.81 of additional savings, with the highest co-benefits occurring in Wisconsin and Michigan and the lowest occurring in California across projected years in 2018–2050. CoBE Projection can aid stakeholders and decision-makers in (1) assessing current individual building or building portfolio performance by quantifying emissions, health, and climate footprint; and (2) evaluating their sustainability plans by comparing the projected co-benefits under different energy conservation scenarios, to achieve highest health and climate co-benefits.

Modelling Short-Term Appliance Energy Use with Interpretable Machine Learning: A System Identification Approach

The modelling and analysis of appliance energy use (AEU) of residential buildings are important for energy consumption control, energy management and maintenance, building performance evaluation, and so on. Although some traditional machine learning methods have been applied to produce good prediction results, these models are usually not interpretable, in that they fail to explain how appliance factors make contributions to the variation of AEU individually and interactively. Explicitly knowing the role played by each of the appliance factors in explaining AEU, however, is very important for energy saving. Motivated by this observation, this study introduces an interpretable machine learning approach which is built upon the nonlinear autoregressive moving average with eXogenous inputs model. The advantage of the proposed model is that in comparison with other state-of-the-art machine learning methods, for example, feedforward neural network, recurrent neural network (e.g., gated recurrent unit), and long short-term memory network, the established model is not only able to produce more accurate energy use prediction, but more importantly, also fully transparent and physically interpretable, clearly and explicitly indicating which factors significantly affect the variation of AEU. The findings of this study provide meaningful insights for improving the AEU efficiency.

Deep energy retrofits using different retrofit materials under different scenarios: Life cycle cost and primary energy implications

Deep energy retrofits of existing buildings can contribute to achieving a renewable-based society, though they are mostly lacking in the EU. To understand the cost and primary energy savings of deep energy retrofits, all life cycle phases, including construction, operation, maintenance and end-of-life, must be considered. The initial building without retrofit must be analysed over its remaining lifetime covering energy operation, maintenance and end-of-life implications that change with a retrofit. Such a method is applied to a multifamily building retrofitted to two passive house standards limiting annual final heat use to 50 and 30 kWh/m2. Energy improvements to the building envelope are analysed considering different versions of initial cladding materials and retrofit materials, and different economic and electricity supply scenarios. A retrofit to 50 kWh/m2 is profitable for all economic scenarios while the 30 kWh/m2 version is profitable for most of the scenarios giving net primary energy savings of 57–60% and 63–72%, respectively, compared to the non-retrofitted building. The cost of different retrofit materials is similar, while the primary energy use is much lower for wood-based materials. The changed costs and primary energy use for the non-retrofit building, in maintenance and end-of-life phases, varies depending on assumed initial cladding materials.

Research on low‐carbon evaluation of clean energy use in rural residential buildings based on Analytic Hierarchy Process method

AbstractAs an important part of the construction industry, rural residential buildings are characterized by low energy utilization, unreasonable structure, and low consumption level, so it is particularly important to study the low carbon transformation and evaluation system. In this study, the coefficient of variation method was used to identify the important factors affecting the low‐carbon transformation of rural residential buildings, and the evaluation system of low‐carbonization of rural residential buildings was determined by Analytic Hierarchy Process (AHP), and the system was used in a rural residential building low‐carbonization evaluation study. The results show that “Energy utilization,” “Envelope structure,” and “Economic factors” have obvious influence on the low carbonization of buildings, with the weights of 36.4%, 24.5%, and 19.5%, respectively. Among the secondary indicators, the top four weightings are “clean energy utilization rate” (0.152), “external wall insulation system” (0.090), “window performance” (0.088), and “electricity consumption” (0.084), which are the most critical factors influencing the low carbonization level of clean energy utilization in rural residential buildings. Finally, based on the constructed low‐carbonization evaluation system, we propose targeted solution strategies to provide a theoretical basis for establishing an effective assessment system for the low‐carbonization of clean energy in rural residential buildings.

Implementing a Web-based Optimized Artificial Intelligence System with Metaheuristic Optimization for Improving Building Energy Performance

ABSTRACT Improving energy efficiency in buildings is a challenge during operation and maintenance. The work proposes a cloud artificial intelligence-based building energy management (cloud AI-BEM) system for predicting building energy consumption. The proposed system includes the data layer, the AI-analytics layer, and the decision support information layer. The data layer collects and stores data in the cloud database management system. The analytics layer performs applied a hybrid AI model which was developed and deployed in this layer that enables predict future energy consumption in buildings. The hybrid AI model, namely the SAMFOR model was developed based on the integration of the seasonal autoregressive integrated moving average (SARIMA) model and the firefly algorithm (FA) and least-squares support vector regression (LSSVR). The web-based layer visualizes insights for users. As insights, the cloud AI-BEM system enables to monitor and to compare the energy consumption among buildings; to predict one-day-ahead energy use in buildings, to produce key performance indicators of energy use; to visualize energy data and outdoor temperature data; to easily interact with data from the system interface. Average accuracy in terms of RMSE values ranged from 1.36 kW per 30 mins. The R values were higher than 0.957 and very close to 1.

Influence of Ventilation Openings on the Energy Efficiency of Metal Frame Modular Constructions in Brazil Using BIM

Construction projects demand a higher amount of energy predominantly for heating, ventilation, and illumination purposes. Modular construction has come into the limelight in recent years as a construction method that uses sustainable building materials and optimizes energy efficiency. Ventilation openings in buildings are designed to facilitate air circulation by naturally driven ventilation and could aid in reducing energy consumption in construction projects. However, a knowledge gap makes it difficult to propose the best dimensions of ventilation openings in buildings. Hence, the aim of this work is to empower the decision-making process in terms of proposing the best ventilation opening dimensions toward sustainable energy use and management in buildings. A novel framework is presented herein to evaluate the impact and propose the best dimensions of ventilation openings for metal frame modular construction in Brazil, using building information modeling. The ventilation openings were constructed and their dimensions evaluated in eight Brazilian cities, based on the bioclimatic zone (BioZ) classification indicated in ABNT NBR 15220: Curitiba (1st BioZ); Rio Negro (2nd BioZ); São Paulo (3rd BioZ); Brasília (4th BioZ); Campos (5th BioZ); Paranaíbe (6th BioZ); Goiás (7th BioZ); and Rio de Janeiro (8th BioZ). The study results show that the energy consumption of the same building model would vary based on the dimensions of ventilation openings for each BioZ in Brazil. For instance, modeling the same modular construction unit in the city of Rio Negro could consume around 50% of the energy compared to the same unit constructed in the city of Rio de Janeiro, using the small opening sizes based on the smallest dimensions of the ventilation openings. Similarly, modeling the construction unit in Curitiba, São Paulo, Brasília, Campos, Paranaíba, and Goiás could reduce energy consumption by around 40%, 34%, 36%, 18%, 20%, and 16%, respectively, compared to constructing the same building in the city of Rio de Janeiro, using the small opening sizes based on the smallest dimensions of the ventilation openings. This work could help practitioners and professionals in modular construction projects to design the best dimensions of the ventilation openings based on each BioZ towards increasing energy efficiency and sustainability.

Hourly based methods to assess carbon footprint flexibility and primary energy use in decarbonized buildings

Constant emission factors to assess the carbon footprint of buildings energy use, as usually included in national Building Technical Codes, show their limitations since the electrical grid mix changes constantly. For this reason, hourly-based methods using time-varying penalty signals to calculate carbon emissions and primary energy use in buildings constitute more effective assessment methods, especially with the aim to activate energy flexibility in buildings based on those inputs. Such signals have been developed and tested in the present work. The robustness and effectiveness of the methods is tested throughout two study cases. The first case compares the impact of using hourly signals over constant factors from the standards. For that purpose, a measured aggregated consumption profile corresponding to 226 real households is analyzed. In the second study case, demand response is implemented through control strategies reacting to the hourly penalty signals, aiming to decrease the emissions, primary energy use and cost. Results for the first case reveal that hourly rates better capture the variability of the electric grid compared to constant yearly factors from national standards, with a 50% difference in carbon emissions and a 20% overestimation with primary energy. Results from the second study case show how the implemented modulation strategies offer benefits in the flexible scenarios compared to the base scenarios, in terms of accumulated emissions or primary energy. Improvements are especially perceived when splitting data seasonally and considering periods with higher demand. Furthermore, this study provides insights for developing energy flexibility inputs when assessing the building performance during critical events such as the COVID19 pandemic or extreme weather conditions, where hourly and seasonal variation might have greater impact. Demand response mechanisms as energy flexibility strategies studied through this work might help in the reduction of total emissions and primary energy. Depending if the goal is to shift the demand due to environmental or economical reasons, different modulation strategies can be implemented to reach greater benefits.

The Impact of HVAC Operational Strategies on Energy Use in an Office Building in a Hot Climate

Office buildings, characterized by high internal heat gains, represent a major share of energy use, mainly due to HVAC systems, especially in commercial facilities. Such energy use becomes more significant in locations with harsh climatic conditions. The careful design, selection, and operation of HVAC and energy systems, however, can contribute to major reductions in the energy use in buildings. In this paper, we present the outcomes of a study conducted to assess the impact of HVAC operational strategies on energy use in office buildings in the hot and humid climate of Saudi Arabia. The study utilized detailed building-energy simulation coupled with utility-use data. The results revealed annual electrical energy savings ranging from an average of 1.9% for each 1°C increase in the thermostat setpoint temperature up to 42.4%, when limiting HVAC system operation to occupied hours only. On an average, there was about a 1.4% penalty in the annual energy for each hour of increase in the HVAC system start-up time. However, this penalty should not be avoided at the expense of compromising thermal comfort. In summary, a combination of appropriate HVAC operational strategies can have a significant impact on a building’s annual energy savings, as demonstrated in the presented case study.

A New Principle for Building Simulation of Radiative Heat Transfer in the Presence of Spherical Surfaces

Radiant heat interchanges are pivotal to assessing the energy use of buildings and facilities that channel some sort of solar radiation. Form factor integrals are needed for an accurate simulation of the main features of the envelope of such buildings. However, the expressions required when the space under analysis is curved, for instance, in domes and vaults, are not feasible. The calculation process of algorithms is usually addressed by cumbersome analytical deductions or else by rough statistical approximations included in the simulations, such as ray-tracing methods. Neither of which works properly under curved geometries. The following article deals with an innovative methodology for employing an exact property that solves any spherical configuration of the radiant surfaces. The newly found relationship is validated by comparison with other solutions previously deducted by the author and by numerical simulations when available. Since there is no other exact method of calculating radiation exchanges within spherical fragments, we consider that this finding represents an advance which contributes to overcoming a variety of unexplained and practical problems of radiative heat transfer applicable to architectural developments, lighting elements and aircraft components.

A systematic review of time user surveys-questionnaires and monitoring variables used to track occupant behaviour in residential buildings

In recent years, a growing body of literature has revealed that occupants and their interactions with the building systems and controls (window opening, thermostats, solar shading, etc.) have become a key factor influencing the building’s energy performance and their Indoor Environment Quality. Occupant behaviour can lead to major disparities between the predicted-simulated and the actual energy use in buildings, described by the term Energy Performance Gap, affecting possible strategies to reduce energy consumption. A potential solution to minimize the differences between the simulated and the real performance is by designing realistic models of occupants, which can be incorporated in a simulation code providing useful information for the design and/or assessment of new or existing buildings. Usually, occupant behaviour models are treated as static-deterministic schedules in building simulations, disregarding their stochastic and diverse nature. Accurate configuration of occupant models leads to fine-tuned simulations, enhancing the operation of building control systems. An occupant-centric building design results in building systems capable to meet occupants’ health and comfort requirements, minimizing at the same time the energy use. Although a number of techniques have been used to model occupancy behaviour, such as probabilistic and statistical models, in the present work, we systematically review the most recent techniques based on questionnaires and monitoring variables. These techniques are quite common and their application could help to better understand what drives occupants to interact with a building and its systems, making possible the design of more robust and realistic occupant models.

Analysis of Swedish school buildings’ energy performance certificates with focus on ventilation systems

Energy performance certificates are valuable sources of information about buildings. They are primarily used to assess the buildings’ energy performance, however the data included can also be used for building stock description or analysis from different perspectives. School buildings account for a substantial part of the Swedish public building stock and represent a great opportunity for implementation of energy saving strategies. To improve the energy efficiency, it is first important to analyse and understand the current energy use and identify the key factors responsible for most of the energy use. In Sweden, data used for EPC compilation are in most cases real measured data opposite to other European countries where EPC comprises calculated data practices. Therefore, the energy performance value provides a much more realistic representation of the building energy use. This study analyses certain aspects of school buildings’ energy performance using data available in EPCs, such as year of construction, floor area, heat supply systems and ventilation system. Comparison with data from some other European countries is also presented. The data which could be included in the certificate to extend the potential of EPC use in other areas, such as evaluation of indoor environmental quality, is also discussed.

Analyzing embodied energy and embodied water for university buildings using input-output-based hybrid method

Buildings present a unique opportunity to not just effectively decrease global energy use but also drastically reduce nearly 40% of global carbon emissions to help mitigate the ongoing climate change. Although most of the building energy use is attributed to building operations as operational energy (OE), a portion of it is termed embodied energy (EE) that is consumed in building construction, transportation, and material production activities. EE of a building, therefore, includes energy consumed directly in onsite and offsite construction and transportation and indirectly through material consumption since each construction material consumes energy in its production and transportation. Like EE, buildings also consume significant amounts of fresh water directly and indirectly as embodied water (EW) in their construction, which is becoming a major concern globally. As fresh water is also depleted in producing and refining energy sources used as EE, a portion of this EW is attributed to energy-related water use (EREW). Most research so far has been focusing on the energy and carbon emission dimensions of buildings overlooking the equally important aspect of water use, which is also crucial for delivering a truly environmentally sustainable building. In this study, an input-output-based hybrid (IOH) model is created to compute and compare EE and EW of 10 higher education buildings and examine the correlation of the calculated EE and EW values. The results demonstrate that the total EE and electricity EE of the study buildings share a very strong positive correlation (r2 = 0.93-0.99) with the buildings’ total EW at the building level. This correlation, however, weakens at the material level. The share of EREW in the total EW ranges from 9-13%, which indicates that reducing just EE may not help decrease EW, and additional efforts may be needed to address EW and reduce fresh water use in building construction.

Suitable Density of Vertical Greenery Systems on Office Buildings for Energy Saving

One of the more optimistic strategies to generate energy savings in buildings and help mitigate the effect of the urban heat island is to “green” the building envelopes. In general, a vertical greenery system (VGS) has excellent possibilities for cutting building energy use, particularly during cooling periods. One of the most important factors is the shadow effect factor, which is greatly affected by the plants. Simulating the facade foliar density using Revit energy analysis is one technique to describe the potential shadow effect of flora. This paper will analyse and investigate to answer the following question: Does the variation in density of the VGS affect the energy savings of the building? The VGS has several factors that affect the energy savings of the building, such as the types that thrive in each region, the facade’s direction, the thickness of the foliage, the presence of air layers in the context of green walls, and the makeup and thickness of the substrate layer. This paper will focus on one factor: the density of the green vertical system. The goal of this study is to develop a standard and simple method for calculating the leaf area index (LAI) and connecting it to the energy savings offered by VGS studies on the office building. Additionally, research was done on the energy savings made at the “Karim Centre”, an office building located in Tripoli, Lebanon. Moreover, the facade direction has many impacts on the indoor thermal system that affect energy savings using the VGS, including green walls and double-skin green facades. There is, however, a dearth of information on operating during the heating season as well as throughout the entire year. This paper will discuss the four evaluation variations of the density of VGS on the facade of the office building, resulting in the suitable density of VGS4, which saves 15.7% less energy with 50% green density on the facade.

Urban building energy modeling from geo-referenced energy performance certificate data: Development, calibration, and validation

Urban building energy models (UBEMs) are considered as applicable tools for urban energy planning. Model developers use different strategies to simulate urban building energy use appropriately, and yet they are often doing so in the absence of high-quality data. While data collection is challenging in many cases, in Sweden, the availability of national databases is relatively good and is expected to facilitate the modeling procedure considerably. This study aims to develop, calibrate and validate an UBEM using available national data, including GIS-based property maps and energy performance certificates (EPCs). The developed UBEM offers an automated framework for constructing simple building-level models from open data and conducting energy simulations in EnergyPlus. The developed UBEM was calibrated and validated for two case study cities in Sweden, Borlänge and Uppsala, where the mean absolute percentage error (MAPE) between simulated results and EPC data was 26% and 22%, respectively. Furthermore, a downward trend was observed in the MAPE with increasing spatial aggregation from building to district and city levels (from 26% to 21% and 10%), which highlights the performance of the UBEM in this study to support accurate urban-scale energy analyses for buildings.

A data-driven framework for building energy benchmarking and renovation decision-making support in Sweden

In Europe, the buildings sector is responsible for 40% of energy use and more than 30% of buildings are older than 50 years. Due to ageing, a large number of houses require energy-efficient renovation to meet building energy performance standards and the national energy efficiency target. Although Swedish house owners are willing to improve energy efficiency, there is a need for a dedicated platform providing decision-making knowledge for house owners to benchmark their buildings. This paper proposes a data-driven framework for building energy renovation benchmarking as part of an energy advisory service development for the Västerbotten region, Sweden. This benchmark model facilitates regional homeowners to benchmark their building energy performance relative to the national target and similar neighbourhood buildings. Specifically, based on user input data such as energy use, location, construction year, floor area, etc., this model benchmarks the user’s building performance using two benchmark references i.e., 1) Sweden’s target to reduce buildings by 50% energy use intensity (EUI) by 50% by 2050 compared to the average EUI in 1995, 2) comparing user building with the most relevant peer group of buildings, using energy performance certificates (EPC) big data. Several building groups will be classified based on influential factors that affect building energy use. Hence, this benchmark provides decision-making supportive knowledge to homeowners e.g., whether they need to perform an energy-efficient renovation. In the future, this methodology will be extended and implemented in the digital platform to provide helpful insights to decide on suitable EEMs. This work is an integral part of project AURORAL aims to deliver an interoperable, open, and integrated digital platform, demonstrated by cross-domain applications through large-scale pilots in 8 regions in Europe, including Västerbotten.

Energy recovery in a commercial building using pico-hydropower turbines: An Australian case study

Optimising energy use in systems and buildings is crucial to reduce climate change. This paper aims to address the gap in knowledge for pico-hydropower (<5 kW) that has been identified as an area of untapped potential in the water industries. A literature review and multivariate analysis are used to find a suitable pico-hydro turbine to install into a coral reef aquarium system in a government owned facility. Key findings from the literature review are untapped potential, gaps in knowledge and global quantification of small hydropower for energy recovery, and lack of enabling data contributing to slow uptake of small hydropower. The study showed a propeller pico-hydropower turbine could be used to recover approximately 10% of the energy used for pumping water through a filtration system. At 2.3 m available head, and 90 L/s water flow, power output up to 1.124 kW was achieved. The project was economically viable with financial and non-financial benefits for the life cycle of the product. There remain sparse case studies for energy recovery using small hydropower in the scientific literature. A growing number of authors see the potential of this renewable energy technology to reduce global greenhouse gas emissions and contribute to the UN Sustainable Development Goals to provide affordable clean energy and address climate change. This study helps to shine a light on opportunities to find value from waste using a novel application of hydropower in a water industry.

Design Optimization of the Skylight for Daylighting and Energy Performance Using NSGA-II

In terms of sustainable design, lateral windows and skylights are important. Daylighting has become a vital component in office buildings because it increases occupants' productivity, well-being, and energy savings via windows and skylights. As a result, various factors have an impact on achieving Useful Daylight Illuminance (UDI) and lowering overheating and glare discomfort. First the merits of the skylight and the window are compared in this study. Second, this study constructs an intelligent optimization method NSGA-II based on Pareto front approaches that allows low energy use of the office building while maintaining excellent thermal and lighting comfort for the inhabitants. Finally, all optimum solutions look at Daylight Glare Probability (DGP) for visual comfort. Isfahan, Iran (32° N, 51° E) as a hot and dry climatic condition, is selected for the simulation. A variety of geometries with different parameters, were applied to meet the goal. Among the different cases, the optimization’s findings highlight the pyramidal design with the highest aperture on the floor. It's best for a hot, dry area and a space with a square or rectangular form. The degree of glare in all ideal geometries, on the other hand, was reasonable—furthermore, these findings aid designers in selecting the appropriate window for the building's form and environment.

Blending of energy benchmarks models for residential buildings

Building energy consumption forecasting is crucial for energy managers and policymakers. However, acutely predicting the energy consumption of any building is more important in energy efficiency benchmarking of buildings. Energy benchmarking is a precise technique for measuring, tracking, and reducing end-use energy usage of buildings by employing comparative scenarios. Bottom-Up energy consumption assessment has been used in several studies to evaluate a collection of buildings' energy performance to a standard. Traditionally, researchers have used a single approach to predict the energy use of buildings and to conduct benchmarking. However, it has been found that doing so frequently compromises the accuracy of the predicted energy use of buildings. It also has an adverse effect on the buildings' energy benchmarking framework results. This paper will provide an energy benchmarking framework for residential buildings based on the blending techniques of multiple approaches. Over 3000 datasets were gathered from the Indian city of Jaipur. In order to analyse the trends in energy consumption, the data were divided into categories based on economic levels. The data is then subjected to some fundamental statistical analysis, along with several approaches are blended to forecast the energy consumption of the buildings with the highest accuracy. These benchmark levels set guidelines for assessing and recognising strong performance while also identifying underperforming buildings and giving them more priority for energy efficiency improvement. The technique may be used for different metropolitan levels and climatic conditions.

Design and Implementation of Renewable Hydrogen-Based System for Social Housing Decarbonization

The energy transition towards a decarbonized economy requires focused and ambitious policies that must be taken through the agreement of governments, stakeholders, and private companies. The intermittency of renewable energy sources (RES) makes it necessary to implement energy storage systems (ESS) that allow for an uninterrupted supply of low-carbon power. Energy generation and consumption activities are responsible for 75% of global CO2 emissions. Particularly, energy use in commercial and residential buildings is the third major contributor after industry and agricultural activities. In this context, the SUDOE ENERGY PUSH project proposes an innovative pilot plant combining RES and hydrogen technologies for social housing decarbonization and self-sufficiency. The preliminary simulations results in savings up to 7,000 kWh per year of primary energy from the grid, 1,000 kg per year of CO2 emissions while consuming 100% clean electricity and more than 600 € per year in electricity bills