Non-residential Buildings Research Articles

Predicting non-residential building fire risk using geospatial information and convolutional neural networks

Building fire risk prediction is crucial for allocation of building inspection resources and prevention of fire incidents. Existing research of building fire prediction makes use of data relating to local demography, crime, building use and physical building characteristics, yet few studies have analysed the relative importance of predictive features. Furthermore, image features relating to buildings, such as aerial imagery and digital surface models (DSM), have not been explored. This research presents a multi-modal hybrid neural network for the prediction of fire risk at the building level using the London Fire Brigade dataset. The inclusion of traditional and novel image features is assessed using Shapley values and an ablation study. The ablation study found that while building use is the most effective contributor of classification performance, demographic features, apart from social class, are detrimental. Moreover, while the DSM did not lead to any notable improvement in classification performance, the inclusion of the aerial imagery feature lead to a 4% increase in median validation ROC AUC. The final model presented achieved an ROC AUC of 0.8195 on the test set.

Assessing the Economic Challenges Toward the Implementation of Performance-Based Energy Code for Non-Residential Buildings in Developing Countries

Assessing the Economic Challenges Toward the Implementation of Performance-Based Energy Code for Non-Residential Buildings in Developing Countries

Factors affecting wavelength-resolved ultraviolet irradiance indoors and their impacts on indoor photochemistry.

We measured wavelength-resolved ultraviolet (UV) irradiance in multiple indoor environments and quantified the effects of variables such as light source, solar angles, cloud cover, window type, and electric light color temperature on indoor photon fluxes. The majority of the 77 windows and window samples investigated completely attenuated sunlight at wavelengths shorter than 320nm; despite variations among individual windows leading to differences in indoor HONO photolysis rate constants (JHONO ) and local hydroxyl radical (OH) concentrations of up to a factor of 50, wavelength-resolved transmittance was similar between windows in residential and non-residential buildings. We report mathematical relationships that predict indoor solar UV irradiance as a function of solar zenith angle, incident angle of sunlight on windows, and distance from windows and surfaces for direct and diffuse sunlight. Using these relationships, we predict elevated indoor steady-state OH concentrations (0.80-7.4×106 molec cm-3 ) under illumination by direct and diffuse sunlight and fluorescent tubes near windows or light sources. However, elevated OH concentrations at 1m from the source are only predicted under direct sunlight. We predict that reflections from indoor surfaces will have minor contributions to room-averaged indoor UV irradiance. These results may improve parameterization of indoor chemistry models.

Material efficiency and its contribution to climate change mitigation in Germany: A deep decarbonization scenario analysis until 2060

Germany's greenhouse gas emissions have declined by 35% since 1990, and the national policy ambition is to become largely carbon-neutral by 2050. A change of the industrial landscape and a partial transformation of energy supply have contributed to reductions so far, but for deep reductions, a deep transformation of the country's industrial metabolism is needed. While energy efficiency is well established, the same cannot be said for material efficiency, which includes product light-weighting, lifetime extension, more intense use, and value retention strategies like higher recycling rates, remanufacturing, and reuse. Sector-specific research showed substantial energy and emissions savings potentials of material efficiency, but the overall material efficiency potential for most world economies, including Germany, is unknown. We applied an open-source and modular dynamic material flow analysis model of the transformation of passenger vehicles, residential buildings, and commercial and service buildings in Germany (together ca. 50% of national greenhouse gases) to a material-efficient system, covering the time span 2016–2060. The potential impact of the above-mentioned material efficiency strategies was studied for the climate-relevant materials concrete, steel, timber, aluminum, and plastics. Once the potentials of energy-efficient products, electrification of end-use sectors, and energy system transformation are seized, supply and demand side material efficiency and sufficiency can reduce remaining 2050 emissions by an additional 19–34 % (passenger vehicles), 27–31% (residential buildings), and 14–19% (non-residential buildings). The 2016–2050 cumulative savings can be up to 750 Mt (million metric tons) CO2-eq. Material efficiency can be a key contributor to deep emissions cuts like a 95% target. This article met the requirements for a gold-gold JIE data openness badge described at http://jie.click/badges.

ALDREN: A Methodological Framework to Support Decision-Making and Investments in Deep Energy Renovation of Non-Residential Buildings

Since 2002, the Energy Performance of Buildings Directive (EPBD) has set up the path to improve the efficiency gains in the EU building sector, including measures that should accelerate the rate of building renovation towards more energy efficient systems. Under the 2010 EPBD, all EU countries have established independent energy performance certification systems supported by independent mechanisms of control and verification. The EU directive 2018/844 has introduced different novelties and one of these regards the possibility for the Member States, together with the Long-Term Renovation Strategies (LTRS), to introduce an optional Building Renovation Passport Article 2a.1(c), considered as an empowering document that gives more reliable and independent information on the potential for energy savings that is tied up in their buildings. On 14 October 2020, the European Commission launched its Communication and Strategy on the Renovation Wave initiative, intending to double the current Europe’s renovation rate to make the continent carbon neutral by 2050. However, current practices and tools of energy performance assessment and certification applied across Europe face several challenges. In this context, the ALDREN project is a methodological framework that aims to support decision-making and investment in deep energy renovation of nonresidential buildings, based on a set of procedures (modules) that consist in the step-by-step implementation of protocols to assess the energy performance, indoor environmental quality, and financial value of buildings, before and after the energy renovation. The paper presents the ALDREN overall procedure with a focus on the development of the Building Renovation Passport and its application to an Italian office building.

Building characterization through smart meter data analytics: Determination of the most influential temporal and importance-in-prediction based features

The present paper aims at determining the most influential features to be extracted from smart meter data to facilitate machine learning-based classification of non-residential buildings. Smart meter-driven remote estimation of the chosen characteristics (the buildings’ performance class, use type, and operation group) is significantly helpful in buildings’ commissioning, benchmarking, and diagnostics applications. As the first step, state-of-the-art feature selection methods and a proposed customized approach are utilized for determining the most influential parameters in the pool of temporal features, proposed in a previous study. Next, importance-in-prediction based features, generated from an hour-ahead load prediction pipeline, that can improve the classification accuracy are proposed and added as additional input parameters. Finally, interpretations about some of the most influential features for different classification targets are provided. The obtained results demonstrate that, while aiming at estimating the buildings’ use type, through performing feature selection and adding importance-in-prediction based features, the number of utilized features is reduced from 290 (initial pool of features proposed in a previous study) to 29, while also increasing the accuracy from 71% to 74%. Similarly, number of employed features for estimating the performance class is decreased from 224 to 17 and the achieved accuracy is improved from 56% to 62%. Finally, using only 6 selected features, compared to 287 features in the initial set, the obtained accuracy for the classification of operation group is increased from 98% to 100%. It is thus demonstrated that the proposed methodology, through selecting and utilizing notably fewer features, results in a notable simplification of the feature extraction procedures, improves the achieved accuracy, and facilitates providing interpretations about the reason behind the influence of some of the most important features.

Comparative Analysis on Thermal Properties of Traditional and Present-Age Rural Dwellings' External Walls Located in Submontane Regions of Mazandaran Province

Abstract Reducing energy consumption in residential and nonresidential buildings is one of the main challenges faced in moving toward a more sustainable future. This paper attempts to analyze tradi...

Classification of daily electric load profiles of non-residential buildings

We investigated clustering techniques on time series of daily electric load profiles of fourteen higher education buildings on the same campus. A k-means algorithm is implemented, and three different methods are compared: time-series features extraction with Manhattan distance and raw time series with Euclidian distance and Dynamic Time Warping. The impact of data characteristics with data collection time-steps and timeframes is studied using a database of more than 6,500 daily electric load profiles. We show that Euclidian distance applied to electric demand time series with three-month timeframes and ten-minute time-step provides the most consistent clustering results. In addition, useful insights are highlighted for non-residential buildings electric demand modeling and forecasting. Two groups of buildings can be distinguished regarding electric load profile patterns. On one hand, teaching, research, libraries, and gymnasium buildings show similar patterns distributed in two clusters corresponding to business days and closing days load profiles. On the other hand, campus office buildings present a larger number of clusters inconsistent with day-type dependent load profiles. A seasonal effect is also observed using six-month and one-year timeframes. Finally, a two-cluster distribution is obtained when aggregating all buildings load profiles.

Experimental Data and Simulations of Performance and Thermal Comfort in a Patient Room Equipped with Radiant Ceiling Panels

Hospitals require the highest energy demands in non-residential buildings. They provide healthcare 24/7/365 and, at the same time, they ensure indoor air quality, thermal comfort and sterility. However, several studies reveal that high indoor temperatures and low relative humidity (RH) are often perceived in patient rooms during the heating season, suggesting an important energy saving potential. Against this background, radiant ceiling panel (RCP) systems result to be one of the most appropriate solutions as they allow to achieve significant energy savings while providing the highest level of thermal and acoustic comfort, as well as of infection control. In the present study the microclimatic survey of a patient room at Maggiore Hospital in Bologna, Italy, equipped with an air conditioning system integrated with RCP, has reported occupant thermal discomfort. Experimental data were used to calibrate a building model and dynamic building energy simulations were carried out to analyse indoor air temperature, relative humidity, predicted mean vote (PMV) and predicted percentage of dissatisfied (PPD) indexes under different inlet air temperatures, to identify the best design conditions for energy efficiency and thermal comfort improvement. It was found that the highest advantages can be obtained when neutral air is supplied.

Energy Balance, Cost and Architectural Design Features of 24 Building Integrated Photovoltaic Projects Using a Modelling Approach

This paper presents the energy balance, architectural design features and cost aspects of 24 building integrated photovoltaic (BIPV) projects in three different contexts, namely BIPV in residential, office and historical buildings. These BIPV projects have been modelled and evaluated for different geographic locations because the European Energy Performance of Buildings Directive (2018/844/EU) has resulted in country-specific regulations and situations aimed towards the reduction in energy consumption, and hence the CO2 emissions of built environments. Moreover, the geographical variation of irradiation affects the performance of different BIPV projects on different locations. The results of our study show that the return of investment of BIPV projects across 12 countries took (on average) 13.3 years. Furthermore, the residential projects —as compared to non-residential buildings—were mostly energy plus buildings with an average self-sufficiency of 110% due to their low energy consumption. In conclusion, most BIPV projects resulted in realistic energy performances (on average: 761 kWh/kWp.year), low payback times (10 years for residential and office buildings), and modelled unique design features.

Thermomodernization of a Residential Building to NZEB Level

Directive (EU) 2018/844 of the European Parliament and of the Council of 30 May 2018 amending Directive 2010/31/EU on the energy performance of buildings and Directive 2012/27/EU on energy efficiency introduces a zero-energy building standard, for which the energy balance per year is zero, i.e. the amount of energy obtained from renewable sources is equal to the annual demand. Each Member State shall establish a long-term renovation strategy to support the renovation of the national stock of residential and non-residential buildings, both public and private, into a highly energy efficient and decarbonised building stock by 2050, facilitating the cost-effective transformation of existing buildings into nearly zero-energy buildings. To achieve energy consumption at an appropriately low level in an existing building, you must adjust the heat transfer coefficients of the building envelope and the demand for primary energy to the requirements of the Technical Conditions for year 2021. Renewable energy sources, primarily from the sun, will also play a key role. A computational example of a single-family building shows which partitions are best modernized, what thickness of thermal insulation materials should be used to properly reduce heat transfer coefficients, and which installation systems to use to reduce the potential energy index (EP). It is also depicted as to how much the demand for usable energy in the building will decrease. This article aims to show residents of single-family buildings what steps should be taken to reduce energy consumption and thus - operating costs in an existing building to a minimum.

Features and problems of environmental pollution from decentralized heating systems (on the example of Chita)

This article is devoted to the study of the environmental load of decentralized energy facilities using the example of furnace heating in Chita. Existing residential and non-residential buildings heated with household stoves and low-power boilers increase the environmental burden on the environment. There are tendencies to switch to long-burning boilers. An analytical assessment of the change in the amount of emissions of the main pollutants when changing the operating modes of boilers is presented. Mechanisms for solving problems are proposed.

Water efficiency in Malaysian commercial buildings: a green initiative and cost–benefit approach

PurposeThe demand for potable water increases with population growth, given its role in sustaining life. As part of a wider approach to ensuring sustainable provision, this has prompted a number of carefully formulated water efficiency improvement plans for buildings, one of the highest water-consuming sectors. This paper presents a detailed analysis of water efficiency potential for a multi-storey commercial complex and an office building in central Malaysia.Design/methodology/approachIn this study, a detailed analysis of the water use and efficiency of the water fittings was carried out. Important data such as historical water use and details of water fittings were acquired during a field survey. A series of water flow rate tests were conducted at the selected buildings and each of the water systems was analysed separately, using the criteria of water efficiency stipulated by a locally applied green building tool. A comparison of water consumption before and after the improvement plan was then made.FindingsFindings from the field surveys indicate that only some of the water fittings were certified with recognised efficiency ratings, which suggests a good opportunity for improvement. The proposed replacement of more-efficient fittings could potentially improve the obtainable green credit points by at least 10 from a maximum of 15. A cost–benefit analysis for a water fitting replacement scheme shows that the estimated payback period is less lengthy for the larger commercial building due to the higher potential savings.Practical implicationsStrategies for improvements in water efficiency for existing non-residential buildings are provided in this paper. The same water consumption analysis procedure can be used by maintenance engineers and other practitioners in building assessments where water efficiency and sustainability in building operation are of concern.Originality/valueThis case study responds to the need for efficient use of freshwater and provides insight into the water efficiency opportunities for commercial buildings. The potential cost savings for replacements of inefficient water fittings have been analysed using a green building tool as a guide. This has not been extensively analysed in previous studies.

A Review of Energy Consumption Forecasting in Smart Buildings: Methods, Input Variables, Forecasting Horizon and Metrics

Buildings are among the largest energy consumers in the world. As new technologies have been developed, great advances have been made in buildings, turning conventional buildings into smart buildings. These smart buildings have allowed for greater supervision and control of the energy resources within the buildings, taking steps to energy management strategies to achieve significant energy savings. The forecast of energy consumption in buildings has been a very important element in these energy strategies since it allows adjusting the operation of buildings so that energy can be used more efficiently. This paper presents a review of energy consumption forecasting in smart buildings for improving energy efficiency. Different forecasting methods are studied in nonresidential and residential buildings. Following this, the literature is analyzed in terms of forecasting objectives, input variables, forecasting methods and prediction horizon. In conclusion, the paper examines future challenges for building energy consumption forecasting.

Development and Application of a Flexible Modeling Approach to Reference Buildings for Energy Analysis

This paper proposes a flexible modeling approach to develop a theoretical reference building (RB) for energy analysis. We designed an RB for five non-residential buildings, using dynamic simulation from statistically analyzed data of building stock in South Korea. For modeling, four subsets of data—form, envelope, system, and operation—were assessed. This study uses the autosizing function within EnergyPlus, to develop the RB. The proposed approach allows for a flexible design where capacities and flow rates of the heating, ventilation, and air-conditioning (HVAC) system match the newly defined RB model. This approach would be ideal for closing the gap between the architectural data and equipment elements. The RB developed in this study allows for performing energy performance analysis by end-use. The analysis results by the end-use can provide support for country-level greenhouse gas (GHG)-mitigation-strategy development.

Passive control strategies for cooling a non-residential nearly zero energy office: Simulated comfort resilience now and in the future

The average global cooling demand in non-residential buildings is expected to increase by over 275% between now and 2050. Controlled passive cooling is fundamental to successful operational performance of buildings and in mitigating energy that would otherwise be consumed by mechanical systems. The aim of this study was to determine the resilience of different passive cooling control strategies in delivering optimal comfort and energy scenarios in both current and future extreme conditions, for low energy indoor office spaces. Simulations were conducted using a calibrated TRNSYS 17 model of a nearly zero energy building. The performance of ten passive cooling control strategies was simulated for climatic conditions in two representative cities, Dublin and Budapest. Each strategy used different combinations of passive cooling systems such as day-time ventilation, night-time ventilation and dynamic solar shading. The effect of static and adaptive indoor temperature set-points and a limit on external relative humidity was also investigated. The thermal comfort performance of each strategy was assessed by using standardised thermal comfort, overheating and overcooling metrics. Findings from the study show that passive control strategies maintained comfortable internal conditions between 57% and 95% of the occupied hours, without the need for mechanical cooling. The most resilient strategies were those that combined multiple measures. Passive control strategies were found to be resilient in the medium-term in Dublin, however, the same systems were not able to maintain comfortable conditions in Budapest in 2050. The use of an external relative humidity limit resulted in increases overheating incidences and failure of some overheating criteria. Based on the reductions in mechanical cooling requirements, it was concluded that there is regulatory need to consider the use passive control strategies in the design of buildings.

Sensory and physiological assessment of spatial transient thermal environment changes at a tropical university campus

PurposeRecent studies have found that the high demand for air-conditioning usage in tropical countries has affected the thermal adaptability of building occupants to hot weather, and increased building energy consumption. This pilot study aims to investigate the effects of transient thermal environment changes on participants' sensory and physiological responses.Design/methodology/approachThe change of thermal perceptions, skin temperatures and core temperatures when exposed to transient thermal environments (cool-warm-cool) from 10 college-aged female participants during a simulated daily commute by foot to class in a tropical university campus were investigated. Subjective measurements were collected in real-time every 5 min.FindingsThe main finding suggests that participants were acclimatised to cool air-conditioned indoor environments, despite exhibiting significant mean skin temperature differences (p < 0.05). In addition, exposure to uniform air conditioning from 17 to 18°C for 20 min was thermally unacceptable and reduced concentration during given tasks.Research limitations/implicationsThe study focused on thermal comfort conditions in a uniform air-conditioned lecture hall, and the findings may not be applicable for residential and other private building spaces. The distinct temperature difference between indoor and outdoor in the tropical built environment resulted in high dependence on air-conditioning usage. The building occupants' well-being and energy conservation implications of the findings are discussed.Practical implicationsThis study provides the platform for discussion on the dynamics of occupants' comfort level and adopting a more variable thermal environment in tropical spatial transient thermal environments among architects and building management system managers. The findings from this study may contribute to the Malaysian Standards for Energy Efficiency and Use of Renewable Energy for Non-Residential Buildings (MS1525).Originality/valueA knowledge gap in adaptive thermal comfort due to exposure from transient conditions in tropical university campus for energy efficiency revision has been investigated.

Water-energy benchmarking and predictive modeling in multi-family residential and non-residential buildings

As the threat of climate change grows alongside a continual increase in urban population, the need to ensure access to water and energy resources becomes more crucial. In the context of the water-energy nexus in urban environments, this work addresses current gaps in understanding of coupled water and energy demand patterns and reveals apparent dissimilarities between utilization of water and energy resources for heterogeneous buildings. This study proposes a data-driven approach to identify fundamental water and energy demand profiles, cluster buildings into groups exhibiting similar water and energy use, and predict their demand. The clustering problem was cast as a two-stage cluster ensemble problem, in which several clustering methods with different settings were employed, and then the results obtained from partial view of the data were combined to achieve consensus among the partitionings. The influential drivers for water and energy consumption were identified, parametric and non-parametric prediction models were developed and compared, utilizing high and low temporal data resolution. The clustering analysis performed in this work revealed that water and energy consumption patterns of heterogeneous buildings are not exclusively characterized by general building characteristics. Analysis of the predictive models showed that an overall non-parametric model provides better predictions for water and energy compared with parametric models and that models with high and low data resolution provide comparable demand predictions. The results of this study highlight the value of data-driven modeling for revealing meaningful insights into usage patterns and benchmarking buildings’ performance to provide a meaningful measure of comparison to facilitate multi-utility management. Overall, the methods outlined in this study provide another step towards building greater resiliency within urban areas in preparation for future changes in population and climate.

Top Energy Saver of the Year: Results of an Energy Saving Competition in Public Buildings

Abstract Non-residential buildings in the European Union consume more than one third of the building sector’s total. Many non-residential buildings are owned by municipalities. This paper reports about an energy saving competition that was carried out in 91 municipal buildings in eight EU member states in 2019. For each public building an energy team was formed. The energy teams’ activities encompassed motivating changes in the energy use behaviour of employees and small investments. Two challenges added an element of gamification to the energy saving competition. To assess the success of the energy saving competition, an energy performance baseline was calculated using energy consumption data of each public building from previous years. Energy consumption in the competition year was monitored on a monthly base. After the competition the top energy savers from each country were determined by the percentage-based reduction of energy consumption compared to the baseline. On average, the buildings had an electricity and heat consumption in 2019 that was about 8 % and 7 %, respectively, lower than the baseline. As an additional data source for the evaluation, a survey among energy team members was conducted at the beginning and after the energy competition. Support from superiors, employee interest and motivation and behaviour change as assessed by energy team members show a positive, if weak or moderate, correlation with changes in electricity consumption, but not with changes in heat consumption.

Urban Water Demand Simulation in Residential and Non-Residential Buildings Based on a CityGML Data Model

Humans’ activities in urban areas put a strain on local water resources. This paper introduces a method to accurately simulate the stress urban water demand in Germany puts on local resources on a single-building level, and scalable to regional levels without loss of detail. The method integrates building geometry, building physics, census, socio-economy and meteorological information to provide a general approach to assessing water demands that also overcome obstacles on data aggregation and processing imposed by data privacy guidelines. Three German counties were used as validation cases to prove the feasibility of the presented approach: on average, per capita water demand and aggregated water demand deviates by less than 7% from real demand data. Scenarios applied to a case region Ludwigsburg in Germany, which takes the increment of water price, aging of the population and the climate change into account, show that the residential water demand has the change of −2%, +7% and −0.4% respectively. The industrial water demand increases by 46% due to the development of economy indicated by GDP per capita. The rise of precipitation and temperature raise the water demand in non-residential buildings (excluding industry) of 1%.