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Facing the challage of energy conservation and emission reduction in the building sector, most energy saving strategies of central heating only focus on the performance improvement of building envelope, ignoring the impact of energy use behavior. This paper proposed a building energy conservation strategy based on energy use behavior analysis, which provides a perspective for in-depth exploration of building energy conservation. After investigating a typical university in the northern China, the energy use behaviors were characterized and two different DeST energy consumption models before or after considering energy use behavior were established. By comparing the results calculated by these two models, the impact of energy use behavior analysis on building energy consumption was quantitatively analyzed. The results show that the heating energy consumption and carbon emission can be reduced by 23.28% and 23% respectively through the proposed strategy based on energy use behavior. Therefore, the analysis of energy use behavior can provide a theoretical basis for heating operators to make decisions on energy supply regulation. With central cooling and heating further popularizing in the future, the research methodology of this paper has more extensive reference value for energy conservation and emission reduction strategies in the building sector. [ABSTRACT FROM AUTHOR]

Building stock modelling usually deploys representative building archetypes to obtain reliable results of annual energy heating demand and to minimise the associated computational cost. Available methodologies define archetypes considering only the physical characteristics of buildings. Uniform occupancy schedules, which correspond to national averages, are generally used in archetype energy simulations, despite evidence of occupancy schedules which can vary considerably for each building. This paper presents a new methodology to define occupancy-integrated archetypes. The novel feature of these archetype models is the integration of different occupancy schedules within the archetype itself. This allows building stock energy simulations of national population subgroups characterised by specific occupancy profiles to be undertaken. The importance of including occupant-related data in residential archetypes, which is different than the national average, is demonstrated by applying the methodology to the UK national building stock. The resultant occupancy-integrated archetypes are then modelled to obtain the annual final heating energy demand. It is shown that the relative difference between the heating demand of occupancy-integrated archetypes and uniform occupancy archetypes can be up to 30%. [ABSTRACT FROM AUTHOR]

The occupant windows opening behavior has a great impact on indoor air quality and building energy consumption. Therefore, measuring the window opening behavior and factors that affect it are important for the occupant behavior modeling and architectural design. In this study, we proposed non-intrusive measurement methods which can achieve large-scale sampling for the window state. An image recognition code based on MATLAB was used to conduct projective transform of the building elevation maps, identify the window positions and determine their opening proportions. The method can recognize most of window open states with the error of 8%. Based on this method, the window opening states for a hospital building from August to December in 2018 (about 6000 samples) was collected. Then the significance of the influencing factors and window opening distributions under different factors were analyzed. The results showed the outdoor temperature had the most significant effect, and the frequency of the window opening proportion in the range of 0.3–0.5 within 20–30°C is significantly higher than other temperature ranges. This large-scale sampling method proposed in this paper provided a powerful tool for building modeling and energy analysis. [ABSTRACT FROM AUTHOR]

Overall energy consumption has expanded over the previous decades because of rapid population, urbanization and industrial growth rates. The high demand for energy leads to higher cost per unit of energy, which, can impact on the running costs of commercial and residential dwellings. Hence, there is a need for more effective predictive techniques that can be used to measure and optimize energy usage of large arrays of connected Internet of Things (IoT) devices and control points that constitute modern built environments. In this paper, we propose a lightweight IoT framework for predicting energy usage at a localized level for optimal configuration of building-wide energy dissemination policies. Autoregressive Integrated Moving Average (ARIMA) as a statistical liner model could be used for this purpose; however, it is unable to model the dynamic nonlinear relationships in nonstationary fluctuating power consumption data. Therefore, we have developed an improved hybrid model based on the ARIMA, Support Vector Regression (SVRs) and Particle Swarm Optimization (PSO) to predict precision energy usage from supplied data. The proposed model is evaluated using power consumption data acquired from environmental actuator devices controlling a large functional space in a building. Results show that the proposed hybrid model out-performs other alternative techniques in forecasting power consumption. The approach is appropriate in building energy policy implementations due to its precise estimations of energy consumption and lightweight monitoring infrastructure which can lead to reducing the cost on energy consumption. Moreover, it provides an accurate tool to optimize the energy consumption strategies in wider built environments such as smart cities. [ABSTRACT FROM AUTHOR]

• A hybrid appliance classification model is proposed for the feature extraction in the electrical power savings of buildings. • The strategy of time series is presented in the proposed appliance classification. • A secondary ensemble structure is proposed to handle the class imbalance multiclass classification problems. • The random forest algorithm is selected as the binary classifiers in the secondary ensemble structure. • The effectiveness of the proposed model is verified on the extended PLAID dataset. The non-intrusive load monitoring, which gets the energy usage information of each appliance by analyzing the aggregative signal in the centralized point is beneficial to electrical power savings of buildings. As an important step in the non-intrusive load monitoring, the appliance classification has gained great attentions. The feature extraction, the design of classification structure and the selection of classifiers are three important parts in the appliance classification. In this paper, a hybrid appliance classification model is proposed. In the hybrid model, the time series features are utilized to depict the characters of appliances, which is different from the conventional perspective of feature extraction. A secondary ensemble structure is designed to handle the class imbalance multiclass classification problems. The class imbalance is often the case in appliance classification but no effective solutions has been put forward yet. Besides, the random forest is selected as the binary classifiers in the secondary ensemble structure. The effectiveness of the proposed model is verified on the extended PLAID dataset. The experimental results indicate that the time series features provide better overall performance compared to the widely used features, the random forest performance better than the widely used classifiers and the overall performance of the proposed model is better than that of the state of the art model. [ABSTRACT FROM AUTHOR]

• Introduction of ranking approach to evaluate measures provided by 5 member states. • History of measures to mitigate energy poverty in 5 member states is analysed. • Regional approach more effective than state-controlled one in energy poverty alleviation. • Countries with wide range of measures perform better than others with narrow range. • Ambiguity in terms relating to energy poverty originates in EU directives. Energy Poverty (EP) is the inability to attain a socially and materially necessitated level of domestic energy services. In the EU this occurs primarily due to low incomes, poor energy performance of buildings and high energy costs. The impacts of EP range from impaired social lives to unhealthy living conditions, with further consequences in the physical and mental health of energy poor individuals. Member states have been assigned by the EU with the responsibility of dealing with EP within their own territories. This is attainable mainly by creating effective policies, while also encouraging synergies among policies of different fields. However, scientific knowledge is gathered and action is taken on a national level only in a limited number of EU countries. For this reason, this paper aims to fill in the gap and capture snapshots from five EU countries (Cyprus, Spain, Portugal, Bulgaria and Lithuania) where EP has not been exhaustively examined. The study provides an overview of selected policies and measures directly or indirectly targeting EP alleviation and analyses their history and evolution at an EU level as well as at national level. It considers the different geographical dimensions, conditions and aspects (e.g. national or regional) where EP is encountered, in an attempt to identify any variances or similarities in the approaches adopted. Through this comparative study, strengths and weaknesses of national strategies are identified and analysed. Conclusively, based on this analysis, recommendations are made on how to utilise policy tools and provide the most efficient support to energy poor households in the corresponding countries. [ABSTRACT FROM AUTHOR]

Among the various factors that affect building performance, occupant behavior (OB) has received more and more attention and has become a research hotspot in building design and operation in recent years. However, due to the remarkable randomness and diversity of OB in buildings, it is difficult to accurately describe its characteristics using a fixed schedule, which is a normal approach of most energy simulation tools. DeST, a whole building energy simulation tool offers the function of stochastic OB simulation so that the simulation results can be more in line with the actual situation. In the newly released DeST 3.0, the OB modules are developed based on the actual observed OB data and are divided into two parts: occupancy and occupant action modules. The occupancy module is based on the Markov chain, which simulates the occupant movements among the spaces inside and outside the buildings. The occupant action modules are proposed with an environment and event-driven stochastic infrastructure based on a three-parameter Weibull distribution, such as occupant shading, window, and air-conditioning (AC)/heating control behavior modules. This paper presents the OB modules development for coupled simulation in DeST 3.0 and discusses the effects of integrated simulation of occupant behavior on building energy consumption. Firstly, an overview of the current modelling efforts of OB is given, from the trend to the existing foundation. Secondly, the development of OB modules for coupled simulation in DeST 3.0 is introduced in-depth, including co-simulation of AC/heating control behavior, window behavior and shading behavior. Lastly, two case studies that realize coupled simulation using the developed modules are shown, one is the comparison between existing occupancy schedules from GB standards and the developed occupancy simulation, and the other is the comparison between the result of simulating with or without using AC/heating control behavior modules in two bedrooms of the residential building. [ABSTRACT FROM AUTHOR]

[Display omitted] • Carbon emissions are explored in China's building operations from provincial perspective. • GDIM is employed to factorize ten affecting factors of CO 2 emissions in building sub-sectors. • CO 2 emissions of building sub-sectors are predicted with dynamic simulation. • The contribution of provinces to the change and peak of national emissions are discussed. Carbon emissions from building operations have a critical impact on achieving carbon peaking goal in China. However, the provincial emission performance in building operations are quite different and it is still unidentified how they affect the national emission change and peaking target, making national emission reduction target hard to implement at the provincial level. This paper explores the driving factors leading to emission changes and examines the prospective emission peaks in China and China's provinces by adopting generalized Divisia index method (GDIM) and the combination of scenario analysis and Monte Carlo simulation. Furthermore, the regional emission heterogeneity and the contribution of provinces to the change and peak of national emissions are discussed to identify regions and provinces that should be focused on emission monitoring. Results show that in China and most Chinese provinces, energy consumption of commercial buildings, energy consumption and floor space of urban residential buildings besides income improvement, and the per capita CO 2 emissions of rural residential buildings contributed greatly to emission growth. Under current policies, commercial and urban residential buildings in most provinces would fail to reach their emission peaks ahead of schedule. The urban residential buildings in the northern region, commercial and rural residential buildings in the eastern region caused higher CO 2 emissions. At the provincial level, Shandong, Shanxi and Hebei made the top contribution to total emissions of commercial, urban residential and rural residential buildings, respectively. Shandong and the provinces in north China of urban residential buildings and four provinces including Shandong, Guangdong, Hebei and Zhejiang of commercial and rural residential buildings had the significant impact on the uncertainty of the peaking time of CO 2 emissions. These findings provide a strong decision-making reference for Chinese government to formulate targeted regional emission reduction policies in building operations. [ABSTRACT FROM AUTHOR]

• Obtaining the optimal interpolation method for adjacent sites. • Proposing the method for revising meteorological data at city scale. • Verifying the accuracy of the method by energy consumption simulation. In the development process of building energy conservation design, the correct provision of local Typical Meteorological Year (TMY) data is a prerequisite and basic work to evaluate building energy consumption, which directly affects the fine use of outdoor data for building energy conservation. Based on the recorded and observed data of meteorological stations in China, the acquisition and correction methods of TMY were studied by mathematical statistics and target searching, and the building energy consumption simulation of urban office buildings was conducted to verify the accuracy of the method, including:(1) Reviewed the generation method for TMY and the acquisition of data.(2) Based on the existing TMY data, the best spatial interpolation method of adjacent stations is studied by using the standard deviation method, and the selection method of the best number of stations on a large regional scale is given.(3) An improved method of urban environment TMY is proposed. The dry bulb temperature and relative humidity of the building site are corrected to generate TMY under Urban Heat Island (UHI) effect. The research in this paper improves the coverage and accuracy of urban TMY data and supplies basic research support for building energy-saving designs. [ABSTRACT FROM AUTHOR]

• A heat transfer model was developed under the non-constant wall temperature condition. • Non-constant wall temperature was built based on field test results. • The developed model prediction accuracy is higher than the existing calculation model. • The developed model was extended for the hydropower tunnel ventilation performance. Underground tunnel ventilating has been used in many hydropower stations to reduce the building energy consumption of the underground space by precooling or preheating air. The tunnel wall temperature has an important influence on the cooling or preheating performance of the deeply buried underground tunnels. To solve the problem that the heat balance equation is difficult to solve due to the wall temperature variation along the tunnel, a simple heat transfer model under the non-constant wall temperature condition was developed. The developed model is validated against field tests which showed good agreement between the model results and test data. Field tests of underground traffic tunnels in 5 hydropower stations show that the maximum and minimum attenuations reach 13.0 °C and 2.2 °C, respectively. The comparison results show that the relative deviation of the prediction value of the developed model is 3.72% lower than that of the existing constant wall temperature model. Furthermore, the developed model is used for performance simulation of tunnel ventilation for real great hydropower stations, and the conclusions show that the maximum deviations between the calculated model and tested values range from 0.34 °C to 2.76 °C, and the maximum relative deviations range from 1.89% to 9.69%. In addition, the average relative deviations range from 1.13% to 2.40%. Data comparison verifies that the developed model established in this paper has accurate prediction performance, which is conducive to the design optimization of air conditioning and utilization of natural thermal for underground hydropower stations. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

A microalgae-based biofacade is a double-skin facade with integrated photobioreactors (PBRs) for solar cultivation of microalgae. One purpose of cultivating microalgae on building facades is to create an 'active' facade that can reduce the energy consumption required to maintain thermal comfort inside the building, by creating exchange loops between the building and the microalgae cultivation system. This paper describes the experimental validation of a thermal model of a building equipped with a biofacade PBR, and its use in analyzing the energy consumption of international-standard buildings in two different climate conditions (oceanic climate using the weather data from Nantes, France, and semi-arid climate using weather data from Los Angeles, USA). The results of this analysis show that the microalgae biofacade almost entirely removes the building's cooling demand, and the warmer the location the stronger the effect. In addition, the heating demand of the biofacade PBR is also greatly reduced (between 92% and 100% depending on the location). The microalgae biofacade is therefore a more sustainable solution to produce valuable biomass and a promising technology for the development of zero-emission buildings; to this end, suggestions for optimizing the process are put forward. [ABSTRACT FROM AUTHOR]

In recent decades there has been a growing awareness regarding energy consumption in buildings. Unfortunately, at a time when all building actors should get involved in the challenge to reduce energy consumption, designers cannot rely on effective tools to help them in their decision making process concerning airtightness. This literature review allows the identification of two important issues: new airtightness predictive models are complex to develop and existing airtightness predictive models do not meet the needs of designers and contractors. This paper is divided into three main parts in addition to the introduction and the conclusion. The first part deals with the key concepts of infiltration and airtightness, the second part with influencing factors and the third part with airtightness predictive models. These different chapters highlight a need for standardization regarding the metrics used for data presentation, parameters definition and statistical quantification. The lack of standardization hinders the development of a new airtightness predictive tool for designers and contractors. Along with the problem of standardization, supervision and workmanship are parameters that are difficult to model. Their significant impact can explain why designers and contractors find some existing models unreliable. This paper concludes that none of the existing models can be used in their present form as design tools. Further work should focus on the standardization of data presentation and on the development of a new airtightness predictive model. The first step in the development of such a model is to draw an appropriate classification of “air paths.” [ABSTRACT FROM AUTHOR]

Clustering algorithms have been successfully applied in analyzing building energy consumption data. It has proven to be an effective technique to identify representative energy consumption patterns as well as being a pre-processing step for other techniques. In this paper, we propose a clustering method based on k-shape algorithm, which is a relatively novel method to identify shape patterns in time-series data. In the experiment, clustering is performed for each individual building according to its hourly consumption. The novelty of this paper is that a new k-shape algorithm is applied to detect building-energy usage patterns at different levels, and the clustering result is further utilized to improve the accuracy of forecasting models. Ten institutional buildings covering three different typologies are used as case studies and a set of hourly and weekly energy consumption data is further analyzed in this paper. The experimental results reveal that this proposed method can detect building energy usage patterns in different time granularity effectively and also proves that the forecasting accuracy of SVR model is significantly improved by utilizing the results of the proposed clustering method. [ABSTRACT FROM AUTHOR]

Concerning the building environment HVAC facilities, even if a great effort has been made in developing components and systems with high nominal efficiencies, less attention has been paid to the problem of system maintenance. The main objective of the thermoeconomic diagnosis is to detect possible anomalies and their location inside a component of the energy system. The second objective, and indeed the one to be achieved in this paper, is indicated as inverse problem. It is associated with the quantification of the effects of anomalies in terms of thermoeconomic quantities. Its rigorous application in building thermal installations has some difficulties relating to the strong interrelation between the different components and the fact that energy supply facilities are continuously changing with time. The way to deal with dynamic circumstances is thoroughly explored in this article. Likewise, this paper’s main goal is to demonstrate an application of two thermoeconomic diagnosis methodologies in the building sector, one based on the malfunction and dysfunction analysis and the other one based on the characteristic curves of the components. The results obtained allow us to point out the advantages and limitations of both methodologies as well as to combine them and then develop a more reliable diagnosis. [ABSTRACT FROM AUTHOR]

Extensive research works have been carried out over the past few decades in the development of simulation tools to predict the thermal performance of buildings. These validated tools have been used in the design of the building and its components. However, limited simulation tools have been developed for modeling of district energy systems, which can potentially be a very laborious and time-consuming process. Besides many associated limitations, providing a realistic demand profile of the district energy systems is not a straightforward task due to high number of parameters involved in predicting a detail demand profile. This paper reports the development of a simplified model for predicting the thermal demand profile of a district heating system. The paper describes the method used to develop two types of simplified models to predict the thermal load of a variety of buildings (residential, office, attached, detached, etc.). The predictions were also compared with those made by the detailed simulation models. The simplified model was then utilized to predict the energy demand of a variety of districts types (residential, commercial or mix), and its prediction accuracy was compared with those made by detailed model: good agreement was observed between the results. [ABSTRACT FROM AUTHOR]

Weather conditions account for a major source of deviation between simulation results and actual energy performance of buildings. Typical meteorological year (TMY) weather data are commonly used to evaluate building energy demand at design stage. However, such data might overestimate or underestimate the energy demand of buildings considerably depending on the building designs. Also, TMY does not capture the yearly weather variations which is important for evaluating the potential energy savings and penalties for specific energy efficient measures in the long run. This paper conducts a large-scale building performance simulation (3906 simulations) to explore the uncertainty in energy performance incurred in deploying the TMY file and to quantify the discrepancy in energy performance with those evaluated under 30 years of actual weather data for a variety of cool roof designs in Montreal, Canada (a cold climate). A parametric study of energy performance of the buildings with 14 roof insulation values and 9 solar reflectance values (covering both cool roofs and dark roofs) under Canadian Weather Year for Energy Calculation (CWEC) weather data and 30 actual weather data from 1960 to 1989 is conducted. The simulated results based on the 30 years actual weather data are compared to those based on the CWEC data to quantify the differences by root-mean-square deviation (RMSD) evaluation. The findings suggest that: (1) there is a deviation from 3% to 29% between the predicted energy performance under CWEC and that under actual weather data; (2) highest roof insulation level has the least overestimation in space conditioning energy demand; (3) cool roof with the least roof insulation level has the largest overestimation in space conditioning energy demand; and (4) the majority of cool roof designs estimated by CWEC tends to be overestimated which leads to more conservation designs. This paper demonstrates the importance to evaluate the long term performance of the cool roof by taking into account actual weather data. And it also quantifies the potential overestimation in energy demand and offers designers a reference at early design stage of cool roof in the cold climate [ABSTRACT FROM AUTHOR]

HVAC systems in buildings are composed of many subcomponents which consume energy in huge amounts. In recent years, energy consumption becomes very important aspect due to the lack of energy resources and environmental reasons. In terms of energy consumption, the components of HVAC systems account for large portion of total energy used in buildings. Therefore optimization of energy consumption in HVAC applications is an essential requirement in terms of thermodynamic and economic point of views. Besides, the efforts for decreasing energy consumption turn into economic recovery. It also provides benefits for human health and cleaner environment. In this paper, as originality, thermodynamic and economic analyses of an existing social and cultural building which has a heating and cooling area of 8852 m 2 are presented by comparing of a conventional HVAC and a VRF system. A novel contribution is given to the open literature by comparing two systems with actual data measured from the existing system. The other originality is to apply and test a VRF system to a complex-structured building. This study is a rare one which contains insulation, heating and cooling accounts of a building together. In this paper, VRF system is modelled to the existing building for the comparison of the conventional HVAC system. Both systems have been compared in terms of heating and cooling capacity, initial, operation and maintenance costs. It is found that the VRF system is more economic and efficient such that the VRF system is found to have 44% cost profit when compared with the conventional HVAC system. [ABSTRACT FROM AUTHOR]

Many retrofit projects are being carried out in existing buildings to reduce energy consumption. However, the energy consumptions before and after retrofit need to be known in order to evaluate such retrofit projects. Even though the energy consumption after retrofit can be determined through measurement, the energy consumption before retrofit cannot be known. This study is to more easily estimate energy usage prior to the retrofit. Generally, dynamic simulation or regression model should be used to estimate the energy consumption of buildings before retrofit. However, existing regression models have no way to calibrate the model if it is inaccurate. In this paper, we use a clustering technique to improve the accuracy of the regression model. The estimation of energy consumption before retrofit is referred to as “baseline model” and the inverse model is used to create this baseline model. The inverse model is created through monthly data, daily data, and other similar data. In this study, the inverse model was created through daily data and the baseline model was derived from it. The conventional change-point Model and the cluster inverse model presented in this paper were compared and evaluated with the criteria presented through M&V (Measurement and Verification). The results suggest that the cluster inverse model which reflects the characteristics of data is more appropriate when the baseline model is derived from daily data. [ABSTRACT FROM AUTHOR]

Lowering the embodied carbon dioxide equivalent (embodied CO 2 e) of buildings is an essential response to national and global targets for carbon reduction. Globally, construction industry is developing tools, databases and practices for measuring embodied CO 2 e in buildings and recommending routes to reduction. While the TC350 developed standardized methods for the assessment of sustainability aspects in construction works and Environmental Product Declarations, there is no consensus on how this should be carried out in practice. This paper evaluates the current construction industry practice through a review of both academic and professional literature, and through focus groups and interviews with industry experts in the field. Incentives in the available building codes, standards, and benchmarks are also analysed, as are the existing methodologies, tools and datasets. The multiple data sources are used to identify the barriers to the effective measurement and reduction of embodied CO 2 e in practice. This paper recommends that Governments mandate for improved data quality and support the development of a transparent and simplified methodology. [ABSTRACT FROM AUTHOR]

Application of building integrated photovoltaic (BIPV) technology in the building envelope gives an aesthetical and modern appearance. BIPV is a practical, innovative and promising technology for net zero emission buildings. This paper introduces the best in class of the BIPV products and their properties along with international guidelines and testing standards. BIPV products for rooftops, façades and windows have been highlighted in this paper. The properties of BIPV products incorporate solar PV efficiency, V oc , I sc , P max and fill factor (FF). The life cycle sustainable assessment of BIPV module has been reviewed by examining energy payback time and GHG emission. [ABSTRACT FROM AUTHOR]

Current environmental problems arising from the building sector require tools to help reduce resource consumption and environmental impact. Life Cycle Assessment (LCA) is a widely used tool to quantify the environmental impacts of the building sector. The literature recognizes the need to simplify the method application, especially to reduce and optimize data acquisition. Building Information Modeling (BIM) is defined as a virtual 3D building model which integrates with a database of their building elements. Several studies recognize that the integration of BIM and LCA can simplify data acquisition of the building as well as provide both tools with feedback. This paper reviews recent studies centered on BIM-based LCA, and also carries out a methodological analysis of their integration, focusing on the way that BIM can contribute to simplifying data input, and optimize output data and results during the LCA application in buildings. The results show the viability to develop methods based on BIM models for organizing building information used to estimate environmental and energy consumption impacts based on LCA, including: templates and plug-ins for BIM software, and the integration of automated processes combining different data and software. Reviewed papers are simplified LCA applications, mostly focused on CO 2 emission calculation during the early stages of design. Finally, methodological challenges and recommendations for BIM and LCA tools are propose. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid development of cities across the globe and global warming, Urban Heat Island (UHI) and building cooling energy consumption have reached a point of perilous concern. The use of shading systems on building fenestrations has long been known as an effective strategy to minimise façade heat gain as well as glare, resulting in lower cooling energy consumption and indoor comfort. Expending the potentials of shading systems beyond conventional use, the present paper focuses on the effects of shading systems on opaque facades that is currently unaccounted for in simplified Overall Thermal Transfer Value (OTTV) calculations in standards adopted by many countries. A field study was first conducted to compare the effects of various shading configurations on an opaque façade. Subsequently, a simulation study was then conducted to quantify the effects of shading systems on opaque facades with varying thermal transmittance value (U-Value). The results of this study revealed numerous benefits for the deployment of shading systems on opaque building facades, which includes, reduction to building conductive heat gain, building cooling loads, microclimate temperature near the façade, as well as potential reduction to carbon emissions over the building lifecycle. Moreover, the paper also provides considerations for the selection shading configurations, as well as the considerations that would be required for the formulation of a more accurate simplified OTTV equation that would take into account the effects of shading systems on opaque facades. [ABSTRACT FROM AUTHOR]

This paper investigates a novel terminal unit that integrates a radiant cooling floor (RCF) with an underfloor air distribution system (UFAD). The unit consists of rectangular modules that are installed above the standard structural floor of the building, creating a raised floor. The upper face of these modules incorporates a grid for easy installation of the pipes of the RCF, while the space between the raised floor and the structural slab is used as the air supply plenum of the UFAD system. The proposed system presents some advantages over the standard air systems: (1) it allows increasing production and zone set point temperatures, which improve the efficiency of the chiller plant, (2) improved comfort, and (3) improved air quality due to reduced mixing within the occupied zone. The proposed system can also solve or mitigate some of the drawbacks of cooling floors: (1) prevent water condensation over the surface of the floor, (2) increase the cooling capacity of the floor, and (3) reduce the response time to changes in the space thermal loads. The interactions between the RCF and the UFAD are not evident. In this paper, the thermal behavior of the combined unit is investigated by means of a numerical model implemented in COMSOL Multiphysics. The accuracy of this model is checked against three experiments undertaken in laboratory using a scale model of the system. In the first experiment, the air above the floor is kept quiescent and the cooling capacity is measured. The maximum relative error between the predicted and measured capacities is 19.5%. The numerical model is then used to study how the air movement within the plenum modifies the transient thermal response of the unit. The second and third experiments are designed to quantify the temperature change of the air circulating within the plenum (thermal decay or TD). In nominal operating conditions, TD is about 2.7 °C. In short, the new terminal unit is like a radiant cooling floor and a UFAD system working separately, but integrated in the same terminal unit. [ABSTRACT FROM AUTHOR]

The German Federal Government put its first net zero office building in operation in 2013 anticipating EU requirements for all public administrations to solely built “nearly zero energy buildings” from 2019 onwards. The two-storey building is situated in Berlin and has been designed for having an overall annual energy demand lower than the in-situ conversion of renewable energies. The paper shows the results of the first year of the operation. The overall annual electric usage is near to the expected value. Nevertheless, intensive monitoring showed differences between the expected and the measured electric usage in the areas of heating and domestic hot water (+172%), ventilation (−36%), lighting (−33%), equipment and auxiliaries (−14% and −13%, respectively), yield of the PV-system (+32%). A numerical model was created to investigate these deviations as well as to evaluate the impact of possible actions aimed at further improving the energy demand of the building. The model was calibrated with measured data, data from surveys, occupancy schedules, and validated by means of different indexes, among which the mean bias error (MBE). The MBE between monitored and simulated energy performance is below ±2% for all stated areas of energy usage. Analogously, the energy needs for cooling, monitored and simulated, as well as the trends of the indoor air temperature in both heating and cooling seasons, are in a very good agreement. In detail, the monthly MBE for the indoor temperature, in specific summer and winter periods and in the considered rooms, is below the threshold of ±5%, with values slightly higher (anyway lower than ±6%) only at the ground floor. Finally, the paper shows that the reasons for the detected deviations in electric energy usage can mainly be related to the usage pattern of the building and to the occupants’ behavior. In order to ensure energy efficiency as high as planned, a suitable monitoring of the energy performance is fundamental. It enables the calibration and validation of tailored energy models to detect possible malfunctions as well as propose actions aimed at further reducing the energy usage of the building. [ABSTRACT FROM AUTHOR]

Uncertainty Quantification (UQ) employing a Monte Carlo Sampling (MCS) method in a building simulation domain has been widely used to account for risks of predicted outputs for robust decision making. However, the stochastic approach for UQ problems requires significant computational burdens compared to the deterministic approach. This paper addresses two surrogate models (Gaussian Process Emulator (GPE) and Polynomial Chaos Expansion (PCE)) which together can be regarded as a meta-model of a Building Performance Simulation (BPS) tool with a high-fidelity model. In the paper, the developed GPE and PCE with different model structures were compared in terms of a prediction capability under different amount of training data and number of inputs. The aim of the comparative study is to identify the relative prediction abilities and model flexibility of GPE and PCE. It was found that the GPE and PCE produce high performance qualities having fast computation speed compared to the developed basis model if new inputs having identical inputs and probability ranges were used. In terms of two-sample Kolmogorov-Smirnov (K-S) hypothesis test, mean values of the minimum p-values of the GPE and PCE were 0.999 and 0.569, respectively, if the number of samplings are over 30 cases. Otherwise, the PCE shows significantly reduced performance quality than the GPE. [ABSTRACT FROM AUTHOR]

Countries in Eastern-Europe have similar characteristics due to their common historical and economic backgrounds. A large part of the housing stock has been built during the Soviet era, applying uniform solutions and similar standards, but similarities extend to other periods as well. On the other hand, the differences should also be noted – although the climate is mainly continental, there are significant variations between South and North and between mountainous and flat areas. In this paper, a detailed comparative analysis is presented for Bulgaria, Serbia, Hungary and the Czech Republic. The results are based on the residential building typologies developed within the TABULA/EPISCOPE project co-funded by the Intelligent Energy Europe Programme. Typical building types will be presented, covering building structures and systems. Important energy performance indicators are identified and compared, supported by available statistical data about the housing stock. The added value of the paper consists of the analysis of heterogeneous data sources and collecting and comparing the information of the housing stock under a common comparison framework of building typology data between countries, and the contribution in the harmonization of the building typology approach. [ABSTRACT FROM AUTHOR]

This paper presents a model based on dynamic material flow analysis, general in its principles and applied to the dwelling stock of Norway for exemplification. The algorithm at the core of the model is presented in the form of a pseudo-code and is described in detail. The driving force in the model is a population’s need for housing and the necessary input are retrievable from national statistics on population, often dating back to around 1800, and its prognoses up to 2050 or beyond. Technical parameters such as the dwellings’ lifetime and the renovation cycles are expressed by probability functions. Outputs of the model are the flows of construction, demolition and renovation; analysis of the renovation activity is given particular attention. The model shows how the renovation rates are a result of the need for maintenance of an ageing stock, and provides quantitative estimates of the present and future natural renovation rates, i.e. without specific incentives. The paper shows how to validate the model against statistics and other data sources, and how to use the model’s future projections on construction, demolition and renovation activities in scenario based analyses of dwelling stocks’ energy demand and greenhouse gases emissions. [ABSTRACT FROM AUTHOR]

Research has shown the large effect that occupants have on buildings' performance. Uncertainties related to the actual energy consumption of buildings increase the risks for the investments in low carbon technologies. Monitoring building occupancy can potentially decrease these uncertainties by providing more information about the occupants and their behaviour. The objective of the investigation is to establish an approach to inform the design process (e.g. building simulation) by addressing the complexity of occupants behaviour. The approach integrates information on occupants' behaviour and attitudes regarding energy use and indoor conditions to determine the requirements for building simulation and energy calculations. This paper presents the results of two monitoring campaigns in which the approach was employed. The monitoring campaigns focused on two owner-inhabited apartments in Spain and three social rental dwellings in The Netherlands. The results have given first insights of the power of the methodology to obtain detailed and understandable data on the occupancy patterns. This investigation highlighted the importance socio-economical status and attitudes towards energy conservation on occupants' behaviour in residential buildings. The methods described in this paper can be readily used to develop occupancy and heating profiles for monitored households to be used in building simulation programs. [ABSTRACT FROM AUTHOR]

In recent years, a growing concern has been how to determine the actual thermal behaviour of façades in their operational stage, in order to establish appropriate energy-saving measures. This paper aims at comparing standardized methods for obtaining the actual thermal transmittance of existing buildings’ façades, specifically the average method and the dynamic method defined by ISO 9869-1:2014, to verify which best fits theoretical values. The paper also aims to promote the use of the dynamic method, and facilitate its implementation. Differences between the theoretical U-value and the measured U-value obtained using the average and dynamic methods were calculated in three case studies, and then compared. The results showed that differences between the theoretical and the measured U-value were lower when the dynamic method was used. Particularly, when testing conditions were not optimal, the use of the dynamic method significantly improved the fit with the theoretical value. Moreover, measurements of the U-value using the dynamic method with a sufficiently large dataset showed a better fit to the theoretical U-value than the results of other dynamic methods proposed by authors. Further research should consider the optimum size of the dataset to obtain a measured U-value that is correctly adjusted to the theoretical U-value. [ABSTRACT FROM AUTHOR]

Recent technical guidance has suggested that comfort and energy efficiency should not be seen as mutually exclusive [CIBSE, “TM54: Evaluating operational energy performance of buildings at the design stage”, 2013]. Currently, however, there is a lack of comprehensive understanding of energy use during building operation and how it influences user comfort. Through comparison of the complex relationships between energy, thermal comfort, and environmental strategy in two flexible higher-education buildings in Sheffield, this paper demonstrates how designers can utilise aspects of active and passive design to deliver more comfortable, lower-energy workspaces. Analysis of the authors’ post-occupancy evaluation of each case study examines what lessons might be learnt and applied to other institutional buildings in order to save energy without compromising occupant comfort. The findings illustrate how perceptions of comfort can be improved by increasing the degree of environmental control occupants have without necessarily increasing energy consumption. The paper highlights the significance of occupancy patterns to a complete understanding of energy efficiency and comfort, and speculates that the prediction and assessment of energy per occupant may have an important future role to play in bridging the gap between energy performance and comfort. [ABSTRACT FROM AUTHOR]

Envelope systems are the main contributors to the energy consumption associated to cooling or heating buildings. There have been significant research efforts to improve the performance of building envelope systems by integrating passive cooling strategies to reduce cooling loads and maintain acceptable indoor thermal comfort. The ventilated wall cavity system is one of these passive-cooling strategies that have received considerable attention recently due to the significant benefits of reducing thermal loads of buildings. In particular, evaporative cooling inside ventilated wall cavities is an attractive passive cooling technique especially in hot and dry climates. This paper presents the initial findings of a current research study focusing on improving the ventilated wall cavity with spray evaporative cooling system that produce fine water droplets. Specifically, this paper describes a numerical modeling approach to evaluate the performance of the proposed passive cooling system to cool buildings in hot and dry climates. The simulation results suggest that a significant reduction in cooling loads can be achieved since the wall cavity can absorb heat from the indoor and outdoor while inducing acceptable supply air temperature capable of maintaining indoor thermal comfort within buildings. [ABSTRACT FROM AUTHOR]

This paper reviews occupancy based model predictive control (MPC) for building indoor climate control. Occupancy behavior in buildings is stochastic and complex in nature. With better understanding of occupancy presence in rooms and spaces, advanced controls, such as MPC, can be designed to achieve a more energy efficient operation, compared to more traditional control methods, while comfort is maintained. This paper starts with an overview of traditional controls implemented in buildings, and importance of occupancy based controls. Various control-oriented building modeling methods including physics-based and data-driven models are reviewed. Later on, a comprehensive review of MPC in terms of control theory, objective functions, constrains, optimization methods, system characteristics and various types of MPC is presented conducted. In principle, MPC finds an optimal sequence of control commands to optimize an objective function, considering system model, disturbances, predictions and actuation constraints. Lastly, occupancy based controls including commonly used rule-based and latest model-based controls are reviewed. In addition, a few experimental studies are presented and discussed. The paper presents a holistic overview of occupancy-based MPC for building heating, ventilation, and air conditioning (HVAC) systems, and discusses current status and future challenges. The purpose of this paper is to provide a guideline forresearchers who would like to conduct similar studies to have a better understanding of established research methods. [ABSTRACT FROM AUTHOR]

Building hygrothermal performance, indoor air quality and energy consumption depend heavily on envelope airtightness. Over the last three decades, single-family dwellings have been amply studied in this respect by researchers in North Europe, the United States and Canada. However, very few studies have been conducted on airtightness in multi-family housing in warm climates such as Mediterranean Europe. This paper aims to enhance the understanding of building airtightness in early twenty-first-century multi-family buildings in southern Spain. Blower Door tests were conducted in 45 units in seven such buildings. The main airtightness parameter values found are reported and compared to the data for other buildings in southern Europe. The paper includes a statistical analysis of the findings, characterises building types and describes the protocol used to identify and quantify air leakage pathways. The conclusion drawn is that although a rough predictive model can be developed, the results are widely scattered due to the impact of the random component of manual construction, even in buildings with identical construction characteristics and types. The values recorded are nonetheless consistent with the findings for other European surveys. [ABSTRACT FROM AUTHOR]

Harvesting daylight for improving building energy performance has become an important issue in recent years. Most energy simulation tools use a Daylight Factor (DF) based algorithm to capture the complexity of daylight behaviors. However, DF has a limitation for fully accounting realistic daylight conditions, and several modifications or integrations with other supporting tools were developed to compensate for this limitation. This paper uses the Kriging method to predict daylight conditions for a whole year, and couples this method with energy simulation tools. Kriging is a group of geo-statistical techniques used to interpolate the value of a random field at an unobserved location from discrete observed values at nearby locations. In previous research, the Kriging method showed promising outcomes; that result was significantly better than current daylight modeling in energy simulation tool, and very close to the results from advanced physics-based daylight simulation tool. However, Kriging also comes with certain limitations that require further investigation. One major hurdle was Kriging’s long computational time compared to current daylight models in energy simulation tools. For this reason, this paper aims to find possibilities to reduce computational time to realistic levels that can be applicable in practice. Furthermore, it proposes a method to integrate Kriging-based daylight model with an energy simulation tool that can plot hourly indoor light distribution. This proposal obtains more realistic results than current energy simulation daylight models, while reducing computational expense. [ABSTRACT FROM AUTHOR]

Abstract To achieve energy efficient Heating, Ventilation and Air Conditioning (HVAC) systems in buildings it is essential to enhance the designs of their various integrated mechanical and electrical components and to control and operate plant optimally. More efficient HVAC systems lead to a significant reduction in power consumption in buildings, which is significant bearing in mind that buildings consume over 40% of the total power consumption in many developed countries. This Special Issue on energy efficient HVAC systems was open to all contributors in the field of heating, ventilation and air conditioning systems in buildings. The invitation was for novel and original papers which extend and advance our scientific and technical understanding of efficient energy HVAC systems including Heat Pumps, water heating and cooling systems in buildings, efficient air conditioning systems, efficient component designs, energy storage (heating and cooling) and regenerative processes. In the event, all these topics were covered in the very wide-ranging submissions accepted but interesting papers on other aspects of HVAC systems and operation were also received. [ABSTRACT FROM AUTHOR]

The energy demand for supplying Domestic Hot Water (DHW) has an important share in the overall thermal energy consumption of commercial buildings. The aim of this paper is to analyze the performance of DHW production in commercial public service, i.e., educational buildings, to subsequently identify frequent inherent shortcomings of centralised DHW installations and to provide measures for optimization or economically more viable solutions. Several buildings of the University of Natural Resources and Life Sciences in Vienna have been investigated in terms of DHW consumption and performance of the installed centralised systems. Referring to the investigated university buildings, it can be stated, that in cases of low to medium consumption figures, typical centralized DHW installations with a hot water circulation pipework achieve only poor efficiencies in the range of 2–12%. As for one particular centralized DHW system in operation, focusing on demand controlled DHW supply through adapted flow control, e.g., reducing the runtime of the circulating pumps at specific time intervals, and, if applicable and feasible, decreasing hot water flow- and storage capacities can reduce annual energy consumption for DHW by 15–25% and improve the overall system efficiency significantly as could be evaluated in a detailed case study. A seemingly economical yet ecologically controversial option for improving centralised DHW systems is the partial or complete conversion to electric point-of-use water heaters. This reduces thermal energy losses almost completely, hereby significantly increasing the efficiency of a DHW supply system. Nevertheless, existing hot water supply systems, energized by district heating, usually consume less primary energy and most likely generate fewer CO 2 emissions compared to electric DHW heaters. [ABSTRACT FROM AUTHOR]

• This study presents a comprehensive review on fan-use rates and effects in practice. • The main trigger of fan-use is a warm environment rather than building types. • Using fans elevates neutral temperature averages by 3 K in real buildings. • More than 15% air-conditioning-use rate is reduced by fan-use in buildings. • 3-K temperature extension is achieved by using fans for productivity not decreasing. This paper is a literature review of field studies on fan-use rates and their effects on thermal comfort, energy conservation, and human productivity. In the assessed literature, fans are more popular in Asia, and more used in mixed-mode (MM) and naturally ventilated (NV) buildings than in air-conditioned (AC) buildings. On the basis of collected fan-use models, probit regression models of fan-use rates and ambient environments were obtained and indicate that the essential trigger of fan-use is a warm environment rather than building types. This result helps us to understand the control behaviors and comfort requirements of occupants. Also, fans could provide benefits in three aspects: widening neutral temperatures, saving energy, and improving occupants' productivity. First, using fans in buildings elevates the neutral temperature and the upper limit of neutral zone (0.5 thermal sensation scale) averages by about 3 K in ranges from 25.7°C to 28.7°C and 27.5°C to 30.7°C, respectively. Second, fan-use reduces AC-use rates in MM buildings in summer. The regression models based on the collected AC-use rate models illustrate that, on average, AC-use is expected to be reduced by about 15% in summer when fans are used. Third, providing occupants access to fans could improve occupants' productivity. Based on the limited data available, a 3-K temperature extension is achieved by fans ensuring productivity not decreasing. This review could shed some light on the extension of the neutral temperature range, predictions of MM buildings' energy consumptions, and methods to enhance productivity. Additionally, this review suggests some valuable directions for future research on fans. [ABSTRACT FROM AUTHOR]

Significant energy savings can be achieved by operating heating, ventilation, and air conditioning control systems with indoor occupancy measurement information. This paper presents a novel plug-and-play occupancy sensing method which will enable temporal minimization of building energy consumption to meet building usage behavior without privacy concerns. The proposed wireless occupancy sensing platform is based on long-wave infra-red (LWIR) focal-plane arrays (FPAs), or thermal imagers, that detect thermal energy rather than visible light. We developed an advanced sensor package consisting of multiple thermal imagers with low-cost optical enhancements to increase field of view and increase sensitivity to occupant detection (filtering building clutter). These imagers can be coupled with radio frequency and ultrasonic-based radar to enhance data collection at key occupant zone boundaries to improve accuracy. Standard filtering and estimation techniques from the image processing and computer vision communities are introduced to overcome the accuracy issues suffered by traditional PIR based sensing, especially when occupants remain relatively still. Accurate low-level counting of individuals can be achieved with minimal impact on privacy. The proposed occupancy detection method can work as a retrofit to enable real-time understanding of the building operational state and usage behavior. Sensor data obtained from real test building zones was used to test the efficacy of the method. [ABSTRACT FROM AUTHOR]

Highlights • The paper studies the impact of urban parks on reducing urban temperature. • Findings reveal that urban park construction reduces building energy consumption. • There is a limit distance to the reduction of energy consumption due to urban parks. • Change of urban characteristics due to park construction affects energy consumption. Abstract A change in the thermal environment of an urban area affects health, living conditions, and energy consumption. In urban planning, urban parks are one of the methods for improving the thermal environment and saving cooling energy. Urban park construction can mitigate temperature, but it also causes urban development by increasing local attractiveness. To achieve efficient energy saving through parks in urban planning, the purpose of this study is to investigate the relationship between building energy consumption and urban characteristics both before and after the construction of an urban park. This study targeted Seoul's Gyeongui line forest, which was recently converted into a linear park on the former railway as an urban regeneration project. We analyzed the relationship between energy consumption and urban characteristics using a regression model, focusing on the changes before and after the construction. In this study, urban characteristics include environment, building physical characteristics, and economic variables. The results show that the construction of the urban park reduced not only temperature but also building energy consumption. The energy reduction effect of the park was limited to a marginal distance. Meanwhile, the urban park construction caused land prices to rise and prompted new development, and this changed the urban characteristics of the area and affected energy consumption. Despite changes in urban characteristics, urban park planning is an effective methods of reducing the energy consumption involved in cooling urban areas. We recommend comprehensive consideration of the urban factors when making park policy to reduce urban temperature and energy consumption. [ABSTRACT FROM AUTHOR]

Abstract The paper presents the results of an experimental activity aimed at producing and characterizing a nano-encapsulated PEG600 (PCMs) into a silica shell. The nano-encapsulation was meant to be useful to improve the material's suitability to integration in building components. The (300±15)nm nanoparticles that were produced underwent a full characterization of their thermal performances. An enthalpy of fusion as high as 66.24 kJ/kg, in a tight melting temperature range (20–21°C) was obtained, making the material suitable for thermal energy storage in buildings. In order to demonstrate the benefits of such as this technology on the reduction of heating and cooling demand of buildings, a concentration of 50% in weight of nanoparticles was, then, embedded into a gypsum plasterboard and used for all indoor plastered surfaces of a reference residential buildings. A saving of respectively up to 4.3% and up to 1.1% of heating and cooling energy demand was predicted in comparison to the ones of a building without PCM. Finally, the material underwent a full toxicological characterization exposing human alveolar basal epithelial cells to nanoparticles. The results showed that there were no toxic effects on cell morphology. [ABSTRACT FROM AUTHOR]

Highlights • A 12 month mixed-method longitudinal study of energy demand in Kuwaiti villas. • Building energy models grounded with physical and social empirical data. • Space cooling accounts for 50% to 75% of total energy use in the case study villas. • Air conditioning thermostat settings are a key driver of energy use. • Insulation and double glazing contribute to more uniform internal temperatures. Abstract This paper investigates the physical and social determinants of energy use in four case study villas in Kuwait. The investigation consisted of a 12 month mixed-method longitudinal monitoring and surveying study followed by building energy modelling. Monitoring and surveying provided empirical data about household energy use, internal temperatures, building and system characteristics and occupant energy-use behaviours. This data was used to ground models to better predict energy use. Models grounded with empirical data show that space cooling accounts for 50% to 75% of total energy use in villas. Results suggest that drivers of cooling energy are varied, with 30% of cooling energy in some villas attributable to heat gains from appliances. Furthermore, while the study found no distinct reduction in measured and modelled annual energy use intensities of villas with improved levels of efficiency (insulation and double glazing), such measures did contribute to more uniform internal temperatures. The study also identified key occupant energy-consuming behaviours including a tendency not to adjust air conditioning (AC) thermostat settings during periods of summer travel and lack of regular AC maintenance. The approach and findings of the study can inform future studies in Kuwait seeking to understand energy use in a nationally representative sample. [ABSTRACT FROM AUTHOR]

Abstract In recent years, technological advances have substantially extended the capabilities of building automation. Despite the achieved advances, evidently, automation have not been widely adopted by occupants in buildings. To enhance automation adoptability, automation procedure in buildings should involve determination of user's preferred automation levels, in different conditions and control contexts and learning preference dynamics in time. In line with this motivation, in this paper we introduce a building automation that learns occupant's preferences continuously to fully or partially control the service systems in buildings based on a set of dynamic rules that are generated with the insight about user's preferences and activities. The algorithmic components of our proposed automation include (1) dynamic command planning, (2) adaptive local learning, and (3) iterative global learning. In order to evaluate these algorithms, we used a combination of real and synthetic user activity and preference data from an office with five occupants and an apartment with one occupant. Based on our results from evaluation of adaptive local learning, after a certain number of days (i.e., 8.5 days in average) the accuracy of predicting participants' preference reached to an acceptable value (i.e., above 85%). About 24% to 75%, 5% to 45%, and 6% to 49% of the total daily energy consumption of the participants could be saved using full automation, adaptive automation and inquisitive automation, respectively. Our results for evaluating iterative global learning algorithm showed that adaptive automation has the highest sum of the rewards from achieved benefit and user satisfaction and inquisitive automation has the second highest reward values. Full automation and no automation came in third and last spots, respectively. [ABSTRACT FROM AUTHOR]

Highlights • Proposed a self-configuring event detector for electricity monitoring in buildings. • The algorithm learns the parameters for detection statistics from the environment. • Presented a comprehensive evaluation on three apartments of the EMBED dataset. • Presented evaluations from different perspectives of human-building interaction. Abstract Monitoring the temporal changes in the operational states of appliances is a key step in inferring the dynamics of operations in smart homes. This information could be leveraged in a variety of energy management applications including energy breakdown of individual loads, inferring the occupancy patterns, and associating the energy use to occupants' activities. The operational states of appliances could be identified through detecting and classifying the events on power time-series. Despite the advancements in the field of event detection, they often require in-situ configuration of model parameters to achieve a higher level of performance according to each new context. In order to address such limitation, in this paper, we have proposed a self-configuring event detection framework for detecting the changes in operational states of appliances. The framework seeks to autonomously learn the contextual characteristics of the loads from the environment and adapt the event detection parameters. The proposed unsupervised framework couples an automated clustering for identifying the recurring motifs, which are representations of the appliances' transient power draw signatures in a given environment and a proximity-based motif matching for detecting the events. The framework was evaluated on EMBED dataset, a publicly available fully labeled electricity disaggregation dataset, collected from three apartments with different categories of the appliances. The evaluations demonstrate that the proposed event detection framework outperforms the conventional event detection in detecting the operational states of different classes of loads across different environments. The proposed framework could also facilitate human-building interactions in training smart home applications by populating motifs to infer the operations of appliances and activities of occupants. [ABSTRACT FROM AUTHOR]

Abstract European renewable energy developments have so far focussed on electricity generation, with relatively modest progress in renewable heating. Partly this is due to the temporal mismatch between solar irradiation availability and residential heating demand profiles. Seasonal thermal energy storage (STES) has been proven in several pilot projects and is market ready, albeit not currently economical. This paper sets out to assess the potential contribution of STES to increasing the renewable heating fraction in residential buildings. An existing mixed integer linear program (MILP) is extended to consider STES and applied to optimize the energy supply system for a typical residential district with efficient new-build apartment buildings, in the context of five contrasting scenarios. Achieving 100% renewable heat supply requires significant capacities of seasonal storages and is associated with substantially (14%) higher cost than in the reference scenario. To achieve a 60% renewable heat supply fraction under today's framework conditions, the cost increase compared to the reference scenario is only marginal (1%). The results in three future scenarios reflecting possible conditions in 2030 demonstrate that even higher levels of renewable heat supply could soon become economical. Overall the recommendation is to aim for renewable heat supply levels of around 60–80% combined with demand side measures such as improved insulation. Further work should focus on more systematically exploring the relationship between the grid renewable electricity fraction, available solar collector area and the optimal renewable heat integration strategy. [ABSTRACT FROM AUTHOR]

Highlights • The aim of this research is to investigate the thermal performance of the prototype building to assess overheating in the summer, particularly the long-term heatwave period in the UK. • To understand the impact of energy efficient technologies on occupants' wellbeing and health in order to identify occupants' thermal comfort level in these types of dwellings. • The study adopts a quantitative research design based on indoor monitoring, in-situ measurements and dynamic simulation modelling. • This clearly indicates that due to this prototype house's structural construction material behaviour in a heatwave and its location (southwest facing orientation), it is susceptible to very high overheating risk. • The results found from the monitoring during a long-term heatwave in the summer of 2018 provided strong evidence for overheating and thereby thermal discomfort in many occupied spaces, particularly on the first floor followed by several spaces on the ground floor. Meanwhile, all the occupied spaces appeared to be well above the acceptable thermal comfort benchmark throughout the data collection period. • To calibrate the building performance, as well as the building materials and components adopted in the model, the measured indoor environment data were scrutinised in conjunction with the IES simulation results. Abstract There is growing evidence that terraced houses—thermally lightweight, well insulated, naturally ventilated with three exposed wall surfaces—are at risk of overheating, especially in south-eastern England. The aim of this study is to evaluate the building performance and develop a reliable building simulation, which will be employed in the second phase of the study: developing affordable and feasible passive design strategies to support the energy-efficient building systems of the construction industry. This paper reports on the results from the first phase of the study where a quantitative methodology, including indoor and outdoor environmental monitoring, in-situ measurements and building simulation modelling, was adopted. The performance of a case study was modelled and simulated via employing Integrated Environmental Solutions (IES) software suite. The results from the base-case were analysed according to the adaptive thermal comfort of Chartered Institution of Building Services Engineers (CIBSE) Technical Memorandum 52 guidelines: The Limits of Thermal Comfort—Avoiding Overheating in European Buildings. The spaces studied within the case study house were observed to exceed the acceptable limits of thermal comfort; particularly, the large bedroom within this zone exceeded the upper limit for overheating up to 11 h daily. Furthermore, the results from the monitoring study indicate a high risk of summertime overheating across all the case study settings, especially during short-term peaks in outdoor temperatures. The main reasons for the problematic thermal performance were identified as well-insulated and fully air-tight building fabric, the lack of sufficient ventilation through the living spaces and excessive heat gains through the composite cladding material. [ABSTRACT FROM AUTHOR]

Abstract In this paper, a combined solar chimney and water wall is proposed to provide ventilation as well as moderate heating to an attached room. A transient heat balance model (THBM) is established to predict the thermal performance of the combined system under mild winter conditions in Sydney, Australia. The effects of the glass panel thickness, air gap width, water column thickness, surface tinting and the relative position of solar chimney and water wall are studied respectively. The results show that the combined solar chimney and water wall is capable of providing ventilation throughout the day and night while maintaining the warmth level of the room even though it continuously draws fresh air from the ambient into the room. It is found that increasing the glass panel thickness or reducing the water column thickness enhances the thermal performance of the combined system by increasing both the room temperature and the ventilation rate. On the other hand, increasing the air gap width helps to increase the ventilation rate, but will cause the room temperature to decrease. In addition, between the two configurations, one with the water wall in front of the solar chimney (referred to as the WW-SC configuration) and the other with the solar chimney in front of the water wall (referred to as the SC-WW configuration), the ventilation rate and room temperature obtained with the SC-WW configuration are higher, and thus the latter configuration is more favorable. Furthermore, it is found that surface tinting generally degrades the thermal performance of the proposed system. [ABSTRACT FROM AUTHOR]

Abstract Dwellings with no heating, ventilation and air conditioning (HVAC) systems are commonly found in many countries. The long-term thermal performance of these buildings can be assessed based on hourly data of occupant thermal discomfort integrated over the required timespan (e.g. total degree hours of discomfort per year). This approach can be easily applied when simulation is adopted in the assessment, but field studies using this approach are rare as they would require complex, costly and long measurement/survey campaigns. This paper addresses the challenges on conducting field studies on long-term thermal performance of dwellings with no HVAC system by introducing a novel performance indicator: the Seasonal Thermal Sensation Vote (S-TSV). S-TSV adopts the standard 7-point thermal sensation scale and is based on the perceived overall thermal sensation recalled by the user of the building for specific seasons and times of day. The new performance indicator is not intended to replace existing ones, but to complement them in the understanding of the complex thermal performance processes taking place in buildings with no HVAC. S-TSV was applied in a field study targeting a small sample of dwellings in Brazil. Results demonstrate the capabilities of S-TSV to describe trends in buildings performance in this sample. S-TSV also assisted on the identification of relationships between such performance and some independent variables addressed in this field study (e.g. windows operation, footwear and income), considering a threshold of p -values <0.05 on the chi-square statistic test. [ABSTRACT FROM AUTHOR]

Highlights • A novel cloud computing application for measurement and verification 2.0 is presented. • IntelliMav offers a solution for near real-time energy savings verification. • Powerful machine learning regression techniques are automatically applied to energy data. • Parallel processing using powerful cloud computing infrastructure reduces resources required. • A real-world case study demonstrates the ease of use and benefits of the application. Abstract Energy conservation measures (ECMs) are implemented in all sectors with the objective of improving the efficiency with which energy is consumed. Measurement and verification (M&V) is required to verify the performance of every ECM to ensure its successful implementation and operation. The methodologies implemented to achieve this are currently evolving to a more dynamic state, known as measurement and verification 2.0, through the use of automated and advanced analytics. The primary barrier to the adoption of M&V 2.0 practices are the tools available to practitioners. This paper aims to populate the knowledge gap in the industrial buildings sector by presenting a novel cloud computing-based application, IntelliMaV, that applies advanced machine learning techniques on large datasets to automatically verify the performance of ECMs in near real-time. Additionally, a performance deviation detection system is incorporated, ensuring persistence of savings beyond the typical period of analysis in M&V. IntelliMaV allows M&V practitioners to quantify energy savings with minimum levels of uncertainty by applying powerful analytics to data readily available in industrial facilities. The use of a cloud computing-based architecture reduces the resources required on-site and decreases the time required to train the baseline energy model through the use of parallel processing. The robust nature of the application ensures it is applicable across the broad spectrum of ECMs in the industrial buildings sector. A case study carried out in a large biomedical manufacturing facility demonstrates the ease of use of the application and the benefits realised through its adoption. The energy savings from an ECM were calculated to be 2,353,225 kWh/yr with 25.5% uncertainty at a 90% confidence interval. [ABSTRACT FROM AUTHOR]

Highlights • The impact of new Italian weather data on building simulation was analysed. • Two simulations were carried out with a dated and an up-dated dataset, for 52 locations. • Italian representative multifamily house models were used. • For each model, two cases were studied: uninsulated and insulated constructions. • The results highlight great differences in ideal energy. Abstract Dynamic thermal simulation is attracting the interest of designers thanks to the availability of numerical codes. However, one of the main problems facing the potential users is hourly-weather data availability. In Italy, the main source for the data is drawn upon the IGDG database. It is provided by the EnergyPlus weather site and the related measurements were collected between 1951 and 1970. A new set of files, gathered between 1989 and 2014, has recently become available. Nevertheless, it needs additional work to be directly implemented in energy simulation codes, which can lead users to download and apply the already available IGDG database files. In order to evaluate the ensuing effects, 52 Italian weather file locations were selected, and two simulations were carried out on seven multifamily-house models considering the difference between the results obtained using the old and new weather data. Since the locations of the two databases do not coincide, a matching method was implemented. This paper compares the simulation results arising from the choice of the weather file pertaining to the two databases. The weather files were selected within a 50 km range and 50 m elevation difference, to minimize misrepresentative results due to different climatic conditions. The models are typical of the most widespread building typology of the Italian building stock. For each model, two building fabric types were considered, the former was poorly insulated, the latter was well insulated thanks to an advanced refurbishment activity. Simulations were carried out with the EnergyPlus software. [ABSTRACT FROM AUTHOR]