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Model-based control schemes such as model predictive control (MPC) can assist smart-energy systems in achieving higher efficiency and utilization of renewable energy sources. A practical barrier for deploying such control schemes for space heating of residential buildings is the costs related to obtaining the weather data measurements needed for identifying a model that describes the dynamic behaviour of the building. Therefore, this paper reports on a simulation-based study investigating whether there is a significant impact on the performance of MPC schemes when substituting these weather measurements with data from meteorological weather services. Since access to weather forecasts is necessary during the operation of the MPC scheme, this implementation approach draws on data already available to remove the need for weather measurements. The results indicated that this approach only led to a minor performance impact in that heating savings were reduced by 4% while comfort violations increased by less than 0.1 Kh per day on average. The results thereby suggest that the use of data from meteorological forecast services for model identification may constitute a cost-efficient alternative to on-site or near-by weather measurements. [ABSTRACT FROM AUTHOR]

Virtual in-situ calibration (VIC) can be conducted on a large scale, in-situ, to calibrate multiple working sensors in an operational building's energy system based on Bayesian inference. As well as random errors, the VIC can handle various systematic errors that are not covered by a conventional calibration, and it does not require removing working sensors or adding reference sensors as is done in a conventional calibration. For successful calibration under the various working conditions of a system, it is important to figure out hidden factors and their negative impacts on the accuracy of VIC. Through case studies for a LiBr-H 2 O refrigeration system, this study reveals two different sensitivity effects and how they affect the accuracy of VIC. Moreover, to handle the sensitivity issues, a new calibration strategy (named reviving calibration ) is suggested and then evaluated in this work. This paper (1) shows the VIC problem formulation process, (2) explains how the two sensitivity effects influence the calibration accuracy, and (3) proves how and how much the suggested handling strategy solves the negative effects problem. The two case studies demonstrate the reviving calibration results in average 53% and 4% improvements, respectively, for temperature and mass flow rate sensors compared to the existing VIC method. [ABSTRACT FROM AUTHOR]

Assessment of the energy associated with the material production, transportation and construction of buildings provides an opportunity for reducing the use of energy and improving sustainability. Building Information Modeling (BIM) provides a platform to incorporate sustainability information in the design of buildings. However, interoperability of BIM with Life Cycle Assessment (LCA) tools needs further investigation. Previous research in this area has either partially employed BIM; data was exported from the main BIM authoring tool and then auxiliary tools were utilized to evaluate the model, thereby causing a disconnect between the model and analysis resulting in non-interoperable systems, or has ignored the importance of retaining the LCA results within the BIM environment. To address this issue, this paper presents a framework to estimate the embodied energy content within the native BIM environment. The implementation of this framework is illustrated by the development of a tool for estimation of material embodied energy, transportation energy and construction energy. A prototype of the tool has been implemented on a case project to establish the workability of the framework. [ABSTRACT FROM AUTHOR]

In situ measurements of low thermal transmittance façades are required to ensure compliance with energy performance strategies for new nearly zero-energy buildings (nZEB) and with energy policies for the transition of existing building stock to nZEB. The aim of this paper was to enhance the accuracy of the in situ measurement of low U-value façades, employing the widely used ISO 9869-1:2014 HFM method and exploring the limits of its conditions. To refine the testing conditions, three variables were analysed and compared with indications of ISO 9869-1:2014 and the existing literature: the temperature difference, the test duration and the accuracy of equipment. A continuous experimental campaign was conducted in a building mock-up. The findings showed that to accurately measure in situ low U-value façades, the temperature differences must be greater than those indicated in the existing literature. Temperature differences above 19 °C required a test duration of 72 h, while for lower temperature differences the test duration must be prolonged. The accuracy of temperature sensors had a greater impact on the accuracy of measurement in the initial cycles of the test. Likewise, the accuracy of ambient temperature sensors was found to have a considerable influence on the uncertainty of measurements. [ABSTRACT FROM AUTHOR]

Good models for building thermal behaviour are an important part of developing building energy management systems that are capable of reducing energy consumption for space heating through model predictive control. A popular approach to modelling the temperature variations of buildings is grey-box models based on lumped parameter thermal networks. By creating simplified models and calibrating their parameters from measurement data, the resulting model is both accurate and shows good generalisation capabilities. Often, parameters of such models are assumed to be a combination of different physical attributes of the building, hence they have some physical interpretation. In this paper, we investigate the dispersion of parameter estimates by use of randomisation. We show that there is significant dispersion in the parameter estimates when using randomised initial conditions for a numerical optimisation algorithm. Further, we claim that in order to assign a physical interpretation to grey-box model parameters, we require the estimated parameters to converge independently of the initial conditions and different datasets. Despite the dispersion of estimated parameters, the prediction capability of calibrated grey-box models is demonstrated by validating the models on independent data. This shows that the models are usable in a model predictive control system. [ABSTRACT FROM AUTHOR]

The thermo-hygrometric treatment related to the air change in buildings requires a relevant quota of the total energy demand of HVAC systems, especially when the ventilation demand is significant. A correct energy saving strategy therefore should always focus on the use of suitable techniques to reduce this energy consumption. As proved by the modern theories on comfort, less strict values can be accepted for the internal humidity set points without compromising indoor comfort conditions. In addition, Demand Controlled Ventilation (DCV) gives the opportunity to reduce energy requirements. This paper investigates the opportunities offered by the installation of a DCV system and a Building Management System (BMS) able to perform long term monitoring of the HVAC system in a real case study. This refers to a historic building in Venice in the area of the harbour and recently transformed into a modern university facility. Starting from the experimental data recorded by BMS, an optimization of the control strategies and simple tuning actions of the DCV controller were possible. Results validate the use of more flexible set points of indoor relative humidity and long term on-line tuning even in absence of self-learning control systems, as well as they highlight the achievement of remarkable energy savings thanks to these actions. The research demonstrates the fundamental contribution of successive control performance assessments by long term monitoring to individuate possible weaknesses in DCV system management. [ABSTRACT FROM AUTHOR]

Built-up areas tend to comprise a variety of buildings with diverse and complex shapes, functions and construction characteristics. This variety is the source of significant challenges when calculating building energy use at the building stock level. Moreover, the process of developing stock models usually requires large amounts of data that are frequently scarce, nonexistent or at least not publicly available. Under these circumstances, defining a limited set of reference buildings representing the stock is useful to study the actual energy consumption and the potential effects of different energy conservation measures. This paper presents a new method for developing typical residential reference buildings at district level for bottom-up energy modeling purposes. By means of widely and freely available satellite images, an information database of building shapes is created and a clustering analysis of the geometrical features is performed to define a number of archetypes representative of the heating and cooling energy demand of the district. The method is tested and demonstrated through the case study of the Yuzhong District in Chongqing (China) by comparing the Energy Use Intensity (EUI) of the archetypes derived in this way against detailed dynamic simulations. Results show very small differences in the estimated stock energy consumption (+0.03% in heating energy consumption and +2.97% in cooling energy consumption). [ABSTRACT FROM AUTHOR]

Cites have been identified as one key arena to meet future sustainability challenges. However, if cites are to be part of the transition it must become possible to confirm results of ongoing actions. By the introduction information and communication technologies, it has become easier to collect performance parameters from the built environment, thereby enable more detailed evaluation. The aim of this paper is therefore to examine the potential and limitation of using dynamic and high resolution meter data for evaluation of energy consumption in buildings and households. The novelty of this approach is that dynamic and high resolution meter data can increase the level of detail in evaluation results and ease detection of deviations in the structures performance. However, most benefits are found from the occupant perspective, as more detailed evaluation information enable better inclusion of this stakeholder group. Furthermore this study has shown that the commonly used indicator energy use per heated floor area is an insufficient communication tool when taking holistic approach to building energy evaluation. Limitation to full use of dynamic and high resolution meter data have been identified to data collection and management, preservation of personal integrity and incentives to react on the given evaluation information. [ABSTRACT FROM AUTHOR]

An investigation of building performance through a comfort and operational quality standpoint is not too often discussed for Net-Zero Energy Buildings (NZEB). It would seem important that predications and claims for reducing energy-use in buildings and the move towards NZEB does not compromise comfort in buildings. A high school in Essendon, Victoria (near Melbourne) Australia undertook a retrofitting process to improve its existing library from a functional as well as an environmental standpoint. In the process, several pre- and post-measurements were made of building performance. An advanced packaged unit mechanical system applies a demand control ventilation system with a specialised control that promises uniform air temperature throughout the volume of the conditioned space. This system utilises staged compressors with variable speed drive fans and results in minimal background noise. For the mechanical system to deliver as promised, the building envelope was air pressure sealed to an international standard and double glazed (from single glass) and zoned according to orientation and spatial function. Efficient LED lighting was also applied to a dimming control responsive to occupancy and daylight. This level of building improvement requires before and after measurement procedures to assure and prove a quality performance for a near NZEB. Various measurements of pressurisation, air exchange rates through tracer gas, air temperature stratification, IAQ, as well as a continuous and localised comfort survey measurement all contribute to a pre and post assessment of building performance. This paper discusses several measurement processes considered useful to characterize the performance of an HVAC system in a retrofitted library space. As a result of the building envelope quality, subsequent monitoring of the mechanical conditioning system showed that it could have been reduced to half its specified size. Furthermore, sub-metering of the space indicates that the attention given to the lighting and conditioning systems has reduced their impact, so that plug loads are beginning to dominate and require attention if net zero energy building is to be targeted. [ABSTRACT FROM AUTHOR]

Modern district energy systems are highly complex with several controllable and uncontrollable variables. To effectively manage a multi-vector district requires a holistic perspective in terms of both modelling and optimisation. Current district optimisation strategies found in the literature often consider very simple models for energy generation and conversion technologies. To improve upon the state of the art, more realistic and accurate models must be produced whilst remaining computationally and mathematically simple enough to calculate within short periods. Therefore, this paper provides a comprehensive review of modelling techniques for common district energy conversion technologies including Power-to-Gas. In addition, dynamic building modelling techniques are reviewed, as buildings must be considered active and flexible participants in a district energy system. In both cases, a specific focus is placed on artificial intelligence-based models suitable for implementation in the real-time operational optimisation of multi-vector systems. Future research directions identified from this review include the need to integrate simplified models of energy conversion units, energy distribution networks, dynamic building models and energy storage into a holistic district optimisation framework. Finally, a future district energy management solution is proposed. It leverages semantic modelling to allow interoperability of heterogeneous data sources to provide added value inferencing from contextually enriched information. [ABSTRACT FROM AUTHOR]

Building occupancy is of great importance for energy efficient control of buildings. A large number of works have been developed for the estimation and detection of building occupancy. In this paper, we present a comprehensive review on building occupancy estimation and detection. The estimation and detection systems are divided into different categories based on the involved sensors. Since the fusion of multiple sensors is widely adopted for building occupancy estimation and detection, a review on the systems based on different combination of sensors is also performed. Then, we conduct a comparison of different sensor types for the estimation and detection of occupancy. Finally, some potential future research directions are indicated based on current progresses of the systems. [ABSTRACT FROM AUTHOR]

Natural ventilation is a green building strategy that improves building energy efficiency, indoor thermal environment, and air quality. However, in practice, it is not always clear when and how to utilize the natural ventilation and coordinate its operation with the HVAC system. This paper introduces a reinforcement learning control strategy, specifically through model-free Q-learning, that makes optimal control decisions for HVAC and window systems to minimize both energy consumption and thermal discomfort. This control system evaluates the outdoor and indoor environments (temperature, humidity, solar radiation, and wind speed) at each time step, and responds with the best control decision that targets both immediate and long-term goals. The reinforcement learning control is evaluated through numerical simulation on a building thermal model and compared with a rule-based heuristic control strategy. Case studies in hot-and-humid Miami and warm-and-mild Los Angeles demonstrated the superior performance of reinforcement learning control, which led to 13% and 23% lower HVAC system energy consumption, 62% and 80% lower discomfort degree hours, and 63% and 77% fewer high humidity hours compared to heuristic control. Unlike heuristic control that requires specific knowledge of individual buildings and the creation of exhaustive decision-making scenarios to improve performance, reinforcement learning control guarantees optimality through self-advancement on given goals and cost functions and is able to adapt to stochastic occupancy and occupant behaviors, which is difficult to accommodate by heuristic control. [ABSTRACT FROM AUTHOR]

The requirements concerning energy performance of buildings and their internal installations, particularly HVAC systems, have been growing continuously in Poland and all over the world. The existing, traditional calculation methods following from the static heat exchange model are frequently not sufficient for a reasonable heating design of a building. Both in Poland and elsewhere in the world, methods and software are employed which allow a detailed simulation of the heating and moisture conditions in a building, and also an analysis of the performance of HVAC systems within a building. However, these systems are usually difficult in use and complex. In addition, the development of a simulation model that is sufficiently adequate to the real building requires considerable time involvement of a designer, is time-consuming and laborious (Menberg et al., 2019) [1]. A simplification of the simulation model of a building renders it possible to reduce the costs of computer simulations. The paper analyses in detail the effect of introducing a number of different variants of the simulation model developed in DesignBuilder on the quality of final results obtained. The objective of this analysis is to find simplifications which allow obtaining simulation results which have an acceptable level of deviations from the detailed model, thus facilitating a quick energy performance analysis of a given building. [ABSTRACT FROM AUTHOR]

In order to reduce the energy consumption in buildings, more and more energy efficient technologies for the building-HVAC system have been developed. Several studies showed that occupant behaviour has a much larger influence on the energy performance of a building than the thermal process within the building façade. This underlines the need to investigate the influence of occupant behaviour on building performance. This paper presents the results of a questionnaire survey related to occupant behaviours and preferences in residential buildings. Multivariate regression analyses are conducted to outline main patterns and correlations between energy use, building characteristics and occupant behaviours. Results show a strong relationship between occupants’ actions and characteristics of the built environment – in particular the construction year of the building. Furthermore, occupants have a notable impact on building performance: the chosen set point temperature and the total hours of heating system utilization, which both increase with the age of the building, lead to an increase of the fuel consumption. In conclusion, occupant behaviour is affected by the characteristics of the building, which requires additional studies to increase our understanding of occupant behavioural patterns in existing and modern buildings. [ABSTRACT FROM AUTHOR]

The foundations of all methodologies for the measurement and verification (M&V) of energy savings are based on the same five key principles: accuracy, completeness, conservatism, consistency and transparency. The most widely accepted methodologies tend to generalise M&V so as to ensure applicability across the spectrum of energy conservation measures (ECM’s). These do not provide a rigid calculation procedure to follow. This paper aims to bridge the gap between high-level methodologies and the practical application of modelling algorithms, with a particular focus on the industrial buildings sector. This is achieved with the development of a novel, machine learning supported methodology for M&V 2.0 which enables accurate and reliable quantification of savings. A novel and computationally efficient feature selection algorithm and powerful machine learning regression algorithms are employed to maximise the effectiveness of available data. The baseline period energy consumption is modelled using artificial neural networks, support vector machines, k-nearest neighbours and multiple ordinary least squares regression. Improved knowledge discovery and an expanded boundary of analysis allow more complex energy systems be analysed, thus increasing the applicability of M&V. A case study in a large biomedical manufacturing facility is used to demonstrate the methodology’s ability to accurately quantify the savings under real-world conditions. The ECM was found to result in 604,527 kWh of energy savings with 57% uncertainty at a confidence interval of 68%. 20 baseline energy models are developed using an exhaustive approach with the optimal model being used to quantify savings. The range of savings estimated with each model are presented and the acceptability of uncertainty is reviewed. The case study demonstrates the ability of the methodology to perform M&V to an acceptable standard in challenging circumstances. [ABSTRACT FROM AUTHOR]

Life cycle assessment (LCA) is considered to be the most systematic methodology that is widely used in the area of energy analysis. However, embodied energy (EE) and carbon (EC) analysis requires significant time and effort to ensure the reliability of the LCA results. Therefore, a more comprehensive model of conducting the energy analysis is required to provide more realistic EE and EC analysis in turn. Hybrid LCA has been identified as the best model in improving the completeness of EE and EC inventory data. However, such a benefit was not empirically verified extensively, especially in the Malaysian construction industry. This paper demonstrates an extended application of hybrid LCA to Malaysian building design systems, and further investigates the completeness of the model. Finally, the potential for EE and EC reduction through the allocation of low EE and EC intensities of alternative materials, products or components has been evaluated. The results revealed that the hybrid LCA improved the completeness of the EE and EC inventory data compared with other models. By using low EE and EC intensity materials, products or components, a total EE and EC reduction of 43% and 41%, respectively was achieved. The results showed that the proposed methodology can assist designers practically during the early stage of the design process in the Malaysian construction industry. [ABSTRACT FROM AUTHOR]

The objective of this study is to improve the applicability and efficiency of the gas engine driven heat pump with energy storage system when supplying heating and cooling to the different types of buildings. The physical models of four types of buildings, including residential, hotel, office and university building, were sampled and simulated by the DesignBuilder software using the weather data of typical weather year in Tianjin. The building cooling and heating load characteristics of the typical air-conditioning day (17/07/2016) and the typical heating day (07/01/2017) was chosen as the research data. It was found that the four types of buildings had different load demand due to the different function and characteristics of the buildings. The control strategy of the gas engine driven heat pump with energy storage system with four modes, such as Mode L, Mode P, Mode F and Mode O, was developed to meet the various building heating and cooling load demand. The simulation results showed that the annual primary energy ratio of the gas engine driven heat pump with energy storage system is about 21.4% (residential), 35.2% (hotel), 23% (office)and 26.6% (university) higher than traditional gas engine heat pump system. And a compared experiment was given, which demonstrated that the simulation relative errors of the university building cooling load, the cooling/heating capacity of the gas engine driven heat pump with energy storage system, fuel-consumption and primary energy ratio were lower than 12%, except the heating load with the relative error of 35.6%. From these results, the gas engine driven heat pump with energy storage system could be used in different types of buildings with a more stable operation characteristic, higher primary energy ratio and more suitable performance. The results presented in this paper is of some reference basis for optimizing the gas engine driven heat pump with energy storage system and developing a new route for raising cooling/heating quality, energy saving and consumption reducing. [ABSTRACT FROM AUTHOR]

It is important to have practical methods for constructing and learning a good mathematical model for a building’s thermal system in the presence of unmeasured disturbances and using data from closed loop operation. With this goal in mind, this paper presents a mathematical framework that explains the asymptotic behavior of an estimated model under those conditions and that can aid in learning an accurate model. Some analytic results from the literature of system identification are extended and interpreted for building systems. A new identification approach for determining an accurate thermal network (RC) model for a multi-zone building is developed based on the analytic result, and its superior performance over a conventional grey-box modeling approach is demonstrated experimentally. [ABSTRACT FROM AUTHOR]

Solar heating should be recommended strongly for domestic buildings in the Qinghai–Tibetan plateau due to the area's high intensity radiation. In this paper, a novel hybrid solar heating system is presented, that includes a solar kang system, a Trombe wall and a direct gain window. The effects of five combined solar heating operation modes on the indoor hourly temperature for one case domestic building located in the Qinghai–Tibetan plateau were investigated by field measurement and numerical simulation. The optimal operation mode for improving the indoor thermal environment was determined. Furthermore, the effectiveness of each operational mode on the east room was quantitatively evaluated and ranked. The results show that the numerical simulation and field experimental results were well agreed, although there were some differences in specific values. In addition, the results it indicated that a solar kang running all days and Trombe wall running daytime played a significant role in improving the indoor thermal temperature. These relevant findings could provide a reference for solar heating system design and indoor thermal comfort improvement for rural domestic buildings in the Qinghai–Tibetan plateau. [ABSTRACT FROM AUTHOR]

Prior knowledge of occupants’ thermal comfort can facilitate informed control decision of ambient thermal-conditioning in a building environment. This paper investigates the possibility to predict human thermal state (Comfort/Discomfort) from the information of physiological parameters. As gender difference has been widely linked with thermal comfort perception, a four-fold objective is adopted: first, investigating gender differences in subjective thermal perception; second, investigating gender differences in physiological response under different thermal states; third, identifying those physiological features that have the potential to predict thermal state, and fourth, establishing a data-driven thermal state prediction model for each gender group using the identified features. Human subject experiments were conducted, during which five physiological responses (hand skin temperature ST h , hand skin conductance SC h , pulse rate PR, blood oxygen saturation SpO 2 , blood pressure BP) and four subjective responses (thermal comfort, thermal preference, humidity sensation, airflow sensation) were recorded in conjunction with a thermal sensation survey while environmental conditions varied from cold/cool to neutral levels (18°C − 27 ∘ C). Additionally, derivative features namely change rate (FOG) and mean squared gradient (MSG) of each physiological parameter were examined. Rigorous statistical analysis and subsequent predictive modeling utilizing Random Forest algorithm were implemented. Results demonstrate significant gender difference in several subjective and physiological responses. The features identified for males (SpO 2 -MSG/ST h /ST h -FOG/ST h -MSG/SC h /SC h -FOG/SC h -MSG) and females (ST h -FOG/ST h -MSG/SC h /SC h -MSG/PR/PR-MSG) could accurately predict 92.86% and 94.29% of thermal states, respectively. This study indicates that the thermal state of a person can be identified by monitoring physiological parameters from non-intrusive body locations using wearable sensing technology. [ABSTRACT FROM AUTHOR]

This paper presents the development of a symbolic transformation based strategy with interpretability and visualisation for building energy performance assessment. The strategy was developed using shape definition language based symbolic transformation and hierarchical clustering. Advanced visualisation techniques including dendrogram, heatmap and calendar view were used to assist in understanding building energy usage behaviours. A comparison of this proposed strategy with a Symbolic Aggregate approXimation (SAX) based strategy was also performed. The performance of the proposed strategy was tested and evaluated using the three-year hourly heating energy and electricity usage data of a higher education building. The result demonstrated that the proposed strategy can identify distinct building energy usage behaviours. The visualisation techniques used also assisted the information discovery process. The discovered information helped to understand building energy usage patterns. The comparison of the proposed strategy with the SAX based strategy showed that the proposed strategy outperformed the SAX based strategy for the case building tested in terms of the variations in building energy usage. This proposed strategy can also be potentially used to evaluate the operational performance of building heating, ventilation and air-conditioning (HVAC) systems. [ABSTRACT FROM AUTHOR]

A major challenge facing the buildings sector is the absence of continuous commissioning and the lack of performance monitoring and evaluation leading to buildings energy performance gaps between predicted and actual measured performance. Aiming to better characterize, evaluate and bridge these gaps, the paper proposes an online building energy performance monitoring and evaluation tool ObepME, serving as a basis for fault detection and diagnostics and forming a backbone for continuous commissioning. A calibrated building dynamic energy model is developed and employed to automatically run on a daily basis and simulate the building transient performance for the previous day. The simulated energy consumption results form a baseline to which the actual collected data are compared to evaluate the dynamic energy performance gap. The OU44 University building in Denmark is considered as a case study to implement the proposed framework. A holistic energy model was developed in EnergyPlus and calibrated employing data from various building meters, collected weather conditions, generated occupancy schedules and systems operational parameters and set-points. The calibrated model was employed in the ObepME tool to automatically and continuously monitor and evaluate the OU44 building energy performance, on the level of the whole building and individual energy systems consumption, throughout the period from February to mid-March 2017. The reported dynamic energy performance gap was around −2.85%, −3.47% and 5.48% for heating, total electricity and ventilation system electricity consumption. In addition, specific observations were made on a daily basis in terms of the overall electricity, heating, lighting and ventilation energy consumption as highlighted by the ObepME tool. The ObepME tool is currently running automatically as a part of the OU44 building continuous commissioning and performance evaluation aiming to identify possible discrepancies and deviations paving the way for a methodical preventive fault detection and diagnostics process on various levels in the building. [ABSTRACT FROM AUTHOR]

Polycarbonate panels could be considered as a suitable and cheap solution for walls, roofs, and sheds in non-residential buildings and, at the same time, granular silica aerogel is one of the most promising nano-materials for energy saving in buildings. In the paper, three types of advanced multiwall PC panels (thickness 16, 25, and 40 mm) with translucent granular aerogel were investigated by experimental (thermal and optical) and numerical characterization. By comparing thermal performance of air and aerogel-filled PC systems, it can be noticed that the impact of the aerogel is remarkable: the reduction in U-value is 46%-68%, depending on the aerogel layer thickness. U-value is 1.4 W/m 2 K for the 16 mm thickness sample and it is 0.6 W/m 2 K when the thickness increases up to 40 mm. The systems keep their performance in horizontal position, when they are used as roofs. Light transmittance is 0.61 and 0.42 for 16 mm and 40 mm respectively and the reduction with respect to air-filled panels is acceptable (15%) for 16 mm and significant (40%) for 40 mm thickness. The aerogel has also a remarkable impact on the reflectance spectrum , especially between 400 and 1400 nm. The solar factor is 0.58 for 25 mm thickness, quite similar to the low-e glazing one. Finally, energy simulations for a case study showed that aerogel-filled PC systems outperform conventional double glazing systems both for heating and cooling energy demands. However, when compared to low-e glazings, the benefits of the translucent material (also considering the highest thickness) in the interspace are lower for heating and negligible for cooling energy demands. The aerogel-filled polycarbonate systems could be a valid solution for non-residential buildings, enhancing the thermal performance and the light control of the building envelope, especially when they are used as roofs. [ABSTRACT FROM AUTHOR]

Residential energy consumption level, structure, and potentiality are crucial indices reflecting economic and social development. In rural China, energy poverty is still prevalent, and economic poverty is a critical factor that deteriorates energy poverty. This paper combines income- and asset-based measures of wealth to provide a more dynamic perspective on the effects of economic poverty on energy poverty. Results show that for the household which in an average asset or income level, when its assets and income increase by 10%, the probability of a household choosing clean fuels increases by 0.007 and 0.002, respectively. Rural household prefers gas than electricity when household income and assets increase. To solve the energy poverty problem in rural areas, governments need to take measures to improve the accumulation of wealth, raise the infrastructure level and increase the employment rate. [ABSTRACT FROM AUTHOR]

A fine-grained regulation of the HVAC emitters, capable of providing heat and cool only where effectively needed, can lead to a significant energy saving. This paper presents the results from an experimental test of an energy-efficient sub-zonal heating management system, based on an innovative comfort sensor. The objective is to demonstrate how the real-time PMV (Predicted Mean Vote) measurement of different positions in a room can be used to apply optimal rules for the climate control. The case study is an office, located in Central Italy, equipped with two separately controllable electrical heaters. The heating system has been coupled with a low-cost, IR-based comfort sensor, named Comfort Eye, to regulate the heating output of each heater in function of the local comfort conditions. A PID (Proportional–integral–derivative) controller, tuned by fuzzy logic, uses the PMV measured in the respective sub-zone as controlled variable, regulates the power of each heater. The system ran for one winter day and results have been compared with a reference condition, representative of the typical ON/OFF control of the room. The reference condition has been created with the same heating system, but without the sub-zonal division. The comparison, considering the specific application presented, turned out that the sub-zonal control system could achieve an energy saving up to 17% with respect to the typical ON/OFF control with a slight improvement of thermal comfort, reduced deviation from the neutral condition (PMV = 0). This shows that the possibility of measuring comfort distributions is crucial to achieve optimal environmental control. [ABSTRACT FROM AUTHOR]

The dominance of operational energy and related greenhouse gas (GHG) emissions of most existing buildings is decreasing in new construction, when primary fossil energy of building operation decreases as result of the implementation of energy efficiency measures as well as a decarbonisation of national energy mixes. Stakeholders therefore have a growing interest in understanding the possibilities for reducing embodied impacts in buildings. In the IEA EBC project ‘Annex 57’ a broad call for case studies was launched with the aim to identify design strategies for reducing embodied energy and GHG emissions (EEG) from buildings. The aim of this paper is to identify and provide a collected and comprehensive overview of quantitative reduction potentials of the particular EEG reduction strategies which should be considered by the stakeholders engaged in, and with the capacity to influence the outcome of, individual building projects. This is done by a systematic analysis of the Annex 57 case study collection as well as additional scientific literature. While it should be noted that the actual EEG savings at building level illustrated in this collection of studies are only applicable to each specific case, importantly this multiple cross-case analysis has provided rigorous evidence of the considerable potential to reduce embodied impacts in the design and construction of new and refurbished buildings. [ABSTRACT FROM AUTHOR]

Large scale smart meter deployments have resulted in popularization of sensor-based electricity forecasting which relies on historical sensor data to infer future energy consumption. Although those approaches have been very successful, they require significant quantities of historical data, often over extended periods of time, to train machine learning models and achieve accurate predictions. New buildings and buildings with newly installed meters have small historical datasets that are insufficient to create accurate predictions. Transfer learning methods have been proposed as a way to use cross-domain datasets to improve predictions. However, these methods do not consider the effects of seasonality within domains. Consequently, this paper proposes Hephaestus, a novel transfer learning method for cross-building energy forecasting based on time series multi-feature regression with seasonal and trend adjustment. This method enables energy prediction with merged data from similar buildings with different distributions and different seasonal profiles. Thus, it improves energy prediction accuracy for a new building with limited data by using datasets from other similar buildings. Hephaestus works in the pre- and post- processing phases and therefore can be used with any standard machine learning algorithm. The case study presented here demonstrates that the proposed approach can improve energy prediction for a school by 11.2% by using additional data from other schools. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive review of the open literature on motivations, methods and applications of linking stratified airflow simulation to building energy simulation (BES). First, we reviewed the motivations for coupling prediction models for building energy and indoor environment. This review classified various exchanged data in different applications as interface data and state data , and found that choosing different data sets may lead to varying performance of stability, convergence, and speed for the co-simulation. Second, our review shows that an external coupling scheme is substantially more popular in implementations of co-simulation than an internal coupling scheme. The external coupling is shown to be generally faster in computational speed, as well as easier to implement, maintain and expand than the internal coupling. Third, the external coupling can be carried out in different data synchronization schemes, including static coupling and dynamic coupling. In comparison, the static coupling that performs data exchange only once is computationally faster and more stable than the dynamic coupling. However, concerning accuracy, the dynamic coupling that requires multiple times of data exchange is more accurate than the static coupling. Furthermore, the review identified that the implementation of the external coupling can be achieved through customized interfaces, middleware, and standard interfaces. The customized interface is straightforward but may be limited to a specific coupling application. The middleware is versatile and user-friendly but usually limited in data synchronization schemes. The standard interface is versatile and promising, but may be difficult to implement. Current applications of the co-simulation are mainly energy performance evaluation and control studies. Finally, we discussed the limitations of the current research and provided an overview for future research. [ABSTRACT FROM AUTHOR]

This paper proposed a hybrid Logarithmic Mean Divisia Index (LMDI) model that decomposes the change of total energy consumption (TEC) in the building sector to three different dimensions: 1) key influencing effects that include area effect, structure effect, population intensity, value intensity, and energy intensity; 2) three phases that include material production, construction, and operation; and 3) geographically distributed regions. Then, the model was validated in China's building sector based on the data collected from 30 provinces during 2007–2015. The key findings show that: (1) the increased final energy consumption in the building industry accounted for 53.3% of that in China. (2) Building material production contributed the most (69.2%) to the energy changes, followed by building operation (29.6%) and construction (1.2%). (3) The change of TEC was attributed the most to the area effect (90.4%) and the energy intensity effect (42.4%). (4) Zhejiang, Jiangsu, Hubei, Hebei, Fujian and Shandong contributed the most (47%) to the increase of China's building energy use. These findings provide an insightful understanding of the building energy changes and also a quantifiable justification for making building-specific energy policies, which are discussed at the end of the study. [ABSTRACT FROM AUTHOR]

The paper contributes to the fulfilment of the environmental targets of the EU by introducing an innovative solution for envelopes of nearly zero-energy buildings (nZEB). It presents the development of an environmentally friendly alternative to aluminium curtain wall systems for new constructions or renovations. The research and development were from the early stages led by design strategies for low embodied energy and embodied carbon structures. The first technical concept was analysed using comparative life cycle assessment (LCA). Prototypes were tested to verify their technical performance (air- and water tightness, fire resistance, acoustic properties, short- and long-term hygrothermal monitoring). The final pre-production design was subjected to a detailed LCA, and various possibilities for its improvement were suggested and assessed. The early-stage LCA showed a high potential for reduction of the embodied environmental impact by utilization of natural materials. The results of the experimental testing proved that thus developed curtain wall system fulfilled the standard technical requirements. The detailed pre-production LCA study proved that the fabrication of one panel of the curtain wall system being developed causes significantly lower environmental impact compared with a panel of a common aluminium curtain wall system. [ABSTRACT FROM AUTHOR]

Residential buildings in Kuwait account for almost 60% of the country's national electrical power generated, considerably greater than all other sectors or building types. This paper identifies key drivers of energy use in Kuwaiti villas based on a survey of 250 households undertaken throughout the six districts of Kuwait. The survey consists of a cross-sectional interviewer-administered questionnaire designed to gather detailed information about building physical characteristics, occupants’ socio-demographic background and energy-use behaviours. Survey data is analysed using both statistical descriptive methods and multiple linear regression analysis to identify key determinants of energy consumption in a sample of Kuwaiti villas. Analysis indicates that an occupant driven cooling behaviour (air-conditioning thermostat temperature set points) is the major driver of energy use, followed by the number of rooms and the number of occupants. Together, such drivers significantly explain 32% of the variability in energy consumption Survey findings provide descriptive information about Kuwaiti households and insights into the key drivers of energy use to better inform further research and policy interventions in this field. [ABSTRACT FROM AUTHOR]

Temperature and humidity independent control (THIC) has been demonstrated as an effective way to improve the performance for air-conditioning systems. As an alternative option of THIC method, a double evaporating temperature (DET) chiller using zeotropic mixture refrigerants of R32 and R236fa has been proposed to substitute the traditional water-cooled chiller. The low temperature chilled water of 7 °C and high temperature chilled water of 17 °C will be produced simultaneously by this DET chiller, and the latent and sensible cooling loads of buildings can be afforded by the produced low temperature chilled water and high temperature chilled water respectively. In this paper, the experiments with different concentration ratios of R32 to R236fa are carried out on a 4.0-kW DET chiller. To further improve the energy performance of the DET chiller, the exergy analysis is used to investigate the effects of chilled water temperature and mass concentration of zeotropic mixture on the DET chiller. The exergy loss for each component is discussed in detail to identify the inefficient components, and then indicate the ways to improve the performance of DET chiller. The results show that the DET chiller achieves the lowest exergy loss and highest exergy efficiency when the mass concentration ratio of R32 to R236fa is 40%: 60%. Furthermore, the design temperature of high temperature chilled water should be at 16 °C to improve the energy utilization of DET chiller. This study will help to understand the design and operation for the DET chiller based air-conditioning systems. [ABSTRACT FROM AUTHOR]

Historic buildings constitute a large amount of the European building stock and their preservation is of paramount importance for cultural, economic and also environmental reasons, as their conservation is regarded as more sustainable with respect to demolition and reconstruction. However, historic buildings require frequent repair and conservation works, whose environmental impact is still to be evaluated. In the present paper the sustainability of conservation intervention is evaluated by the LCA analysis method, focusing in particular on cleaning materials and technologies, with reference to conservation sites located in Bologna, Italy. After identifying the most used cleaning materials and technologies, the time necessary for achieving an equal cleaning effectiveness was selected as functional unit and the LCA analysis was carried out, along with a quantitative evaluation of some other key aspects of the different methods (workers’ health, acoustic impact and waste produced in the building site). The results highlight that the different technologies involve remarkably different environmental impacts, but that in some cases their impact could be easily reduced by substituting some secondary materials in the cleaning process, without affecting the effectiveness of cleaning. The results also show that the methods involving the lowest environmental impact are not necessarily the best ones in terms of safety and waste production in the building site. [ABSTRACT FROM AUTHOR]

Energy consumption models play an integral part in energy management and conservation, as it pertains to buildings. It can assist in evaluating building energy efficiency, in carrying out building commissioning, and in identifying and diagnosing building system faults. This review takes an in-depth look at energy-demand prediction models for buildings in that it delves into recent developments in building energy approaches used to predict energy usage. By enlisting current approaches to the modelling of buildings, methods for building energy simulations can be categorized into four level classes as follows: (i) data-driven approaches; (ii) physics-based approaches; (iii) large scale building energy forecasting approaches; and (iv) hybrid approaches. The focus of this review is to target the data-driven approach and large-scale building energy predicting-based approaches. Here the data driven approaches can be categorized by (1) artificial neural network based approaches; (2) clustering based approaches; (3) statistical and machine learning-based approaches; and (4) support vector machine based approaches. From there, the type of data-driven based approach is further grouped by (a) benchmarking models; (b) energy-mapping models; (c) energy forecasting models; and (d) energy profiling models. Large-scale building-energy prediction techniques is then categorized as follows: (1) white-box based approaches; (2) black-box based approaches, and (3) grey-box based approaches. The current study explores first-rate data-driven based approaches about building energy analysis for industrial, commercial, domestic, etc., within a rural and urban setting. This review paper is based on the necessity of identifying points of departure and research opportunities for urban and rural-level analyses of building level energy performance. A variety of issues are explored which include: energy performance metrics; end-use of different building types; multiple levels of granularity; and urban and rural scales. Each technique encompasses a variety of input information as well as varying calculations or simulation models along with furnishing contrasting outcomes that suggest a variety of usages. A thorough review of each technique is presented in this study. This review highlights strengths, shortcomings, and purpose of the methods of numerous data-mining based approaches. A comprehensive review of energy forecasting models that are specified in the literature part is also provided. [ABSTRACT FROM AUTHOR]

Energy retrofit of existing buildings was highlighted as an efficient massive action to decrease energy consumption and emissions of greenhouse gases. But, detecting the optimal retrofit strategies for groups of buildings is nowadays a highly complex problem. Their energy consumptions are influenced by building-related factors (climate, building envelope, building services and systems) and by user-related ones (building operation and maintenance, occupant behavior, and indoor environmental quality). Detecting the contributions of each factor and grouping buildings with similarities –in order to establish similar retrofit strategies – are the main issues that can be faced by statistical multivariate methods. In this paper, we present a new and broader view to propose retrofit strategies adapted to a climate change scenario and analyzed from the economic and energy-poverty points of view, by using multivariate and clustering techniques that include building-related and user-related metrics influencing the energy consumption of groups of buildings. A group of 10 single-family houses in Argentina were selected as a case-study. The contributions of eleven building-related driving metrics and four user-related ones to the energy consumption were analyzed. Then, the more representative house of the cluster was selected for a retrofit analysis for current weather conditions and for future weather under a climate change scenario. The analysis also included an economic assessment in relation to the energy poverty. The higher CV values found in the user-related metrics highlight the influence of occupants in the energy consumption that can result in huge gaps between real and predicted energy performance of buildings. This holistic study contributes to reveal the internal structure of energy consumption and to generate useful knowledge about energy retrofit of the built environment in cities, particularly for those householders which are more susceptible to suffer the adverse effects of energy poverty and climate change. [ABSTRACT FROM AUTHOR]

Developing BEPS models which predict energy usage to a high degree of accuracy can be extremely time consuming. As a result, assumptions are often made regarding the input data required. Making these assumptions without introducing a significant amount of uncertainty to the model can be difficult, and requires experience. Even so, rules of thumb from one geographic region are not automatically transferrable to other regions. This paper develops a methodology which can be used to determine useful guidelines for defining the most influential input data for an accurate BEPS model. Differential sensitivity analysis is carried out on parametric data gathered from five archetype dwelling models. The sensitivity analysis results are used in order to form a guideline minimum set of accurately defined input data. Although the guidelines formed apply specifically to Irish residential dwellings, the methodology and processes used in defining the guidelines is highly repeatable. The guideline minimum data set was applied to practical examples in order to be validated. Existing buildings were modelled, and only the parameters within the minimum data set are accurately defined. All building models predict annual energy usage to within 10% of actual measured data, with seasonal energy profiles well-matching. [ABSTRACT FROM AUTHOR]

This paper presents a hybrid building modelling method with a reduced modelling and calibration effort. The method combines a physics-based model, which describes the general behaviour of the system, with a machine learning algorithm trained to correct the physics-based model’s systematic errors. To exemplify the method, a highly simplified grey-box model is used as the physics-based part and a Gaussian process as the machine learning part. It is shown that the hybrid model improves the temperature and energy predictions of the grey-box model while having a lower generalization error than the pure Gaussian process. Specifically, the hybrid approach achieved a day-ahead zone temperature prediction error ca. 0.1 K (RMSE) lower than the grey-box model. As for the energy prediction, the hybrid model obtained an error of 3% compared to 8% for the grey-box model. In comparison to the Gaussian process, the hybrid approach achieved better predictions in all cases. The improvements were especially high when the models were trained with small datasets: 0.7 K in the temperature prediction and 25 percentage points in the energy prediction. [ABSTRACT FROM AUTHOR]

Actual national and international energy strategies generally encourage the use of renewable energy sources. Thermal energy storage (TES) offers various opportunities in the design of renewable energy systems. Thermochemical heat storage has gained popularity among researches because of higher energy density and lower heat loss compared to sensible and latent heat storage. On the other side solar energy has been recognized as one of the renewable energy sources with the most potential. This paper reviews thermochemical heat storage technologies and systems with emphasis on systems involving solar energy utilization in buildings. The studies are reviewed based on used storage materials, system configuration as well as models to predict and optimize system performance. [ABSTRACT FROM AUTHOR]

This paper summarizes initial activities developed under the ETFE-MFM project, an European EU-FP7 funded initiative focused on the design, manufacturing and testing of a smart solution for current façade multifunctional requirements in the building sector; as energy harvesting, glazing, thermal isolation and lighting. The system is based on Ethylene Tetrafluoroethylene (ETFE), a polymeric material with increasing interest in textile architecture. The aim of this work is to provide a standardized ETFE module acting as a flexible LED display harvested by photovoltaics. In this way, LED strips, organic solar cells and flexible electronics have been embedded in an ETFE structure and a lamination process has been developed using Ethyl Vinyl Acetate (EVA) as interlayer material. This process has been optimized in order to provide good optical and mechanical performance without affecting the functionality of the components. The resulting ETFE membrane has been framed leading to a 1.5 × 1.5 m 2 prototype that it has been vertically installed in a building roof for monitoring and testing from Nov. 1st, 2016 to Sept. 30th, 2017. As main result, it can be said that the module had an average production of 43.45Wh per day with maximum values between 80 Wh and 90 Wh. Taking into account that monitored energy consumption for video displaying is 35.32 W, this yields to an average self-standing mode of 74 min. Up to our knowledge, this constitutes a new approach to design and manufacture a textile architecture module with these features. [ABSTRACT FROM AUTHOR]

Thermal comfort standards are essential to determine a good indoor climate as well as to optimize energy use inside a building. The Predicted Mean Vote (PMV) model is used to determine the indoor comfort limits in a conditioned building. However, PMV model often exaggerates thermal sensation, as it does not include adaptation undertaken by the subjects in the real environment. These adaptive effects depend upon factors like local climatic condition, ethnicity, culture, etc. In this paper the results of adaptive thermal comfort based field studies conducted in 3 naturally ventilated office buildings of cold and cloudy climate in north east India are presented. The variation in thermal sensation, thermal preference, clothing insulation, neutral temperatures and other behavioral adaptive measures undertaken by the subjects like taking hot and cold beverages, number of showers in order to feel comfortable are discussed. The comfortable thermal sensation votes are plotted on the psychometric chart and a comparison with the comfort zone prescribed by ASHRAE is made. The subjects were found to be comfortable in cooler temperature than that prescribed in the standard, and thereby a modification in the comfort zone, which reflects the adaptive action of the subjects for the region is proposed. [ABSTRACT FROM AUTHOR]

This paper presents a two-step approach to forecasting city-wide building energy demand. The initial engineering estimates with input from typical buildings are used as the priori beliefs, and are transformed into the posteriori distributions that describe energy consumption patterns of plots. This two-step approach takes advantage of the engineering estimate in analysis of physical factors that determine building energy consumption and uses demand regression to further correct the priori engineering estimates based on the observed energy consumption of plots. The results of a case study shows that the two-step approach makes the standard deviation of the predicting factors within 0.001 kWh/(m 2 ·a) compared with 6.186 kWh/(m 2 ·a) of the engineering estimate or 53.020 kWh/(m 2 ·a) of the demand regression. It means that the bottom-up two-step approach has a high confidence in forecasting the city-wide building energy demand if the priori engineering estimates are critically accepted and Bayesian analysis is performed according to the observed energy consumption of plots. It is a general methodology and can be applied in most cities to forecast the city-wide building energy demand aiming to enable policymakers to establish the medium or long-term targets related to buildings stock energy consumption and associated CO2 emissions. [ABSTRACT FROM AUTHOR]

Accurately predicting energy usage in buildings is of great importance in various efforts on improving building energy efficiencies such as fault detection and diagnostics, building-grid interactions, and building commissioning. Data-driven approach and first-principle approach are two commonly used methods in developing models for predicting building energy use. In this paper, several data-driven methods including multiple linear regression, adaptive linear filter algorithms (least mean square (LMS), normalized least mean square (nLMS), and recursive least square (RLS)), and Gaussian mixture model regression (GMMR) are employed to predict hourly energy usages in two buildings. One building is a synthetic large-size office building from DOE reference building models. The hourly building energy consumption was predicted using the energy simulation model for one year under Chicago climate. The other building is an existing office building located in Des Moines, Iowa. The actual hourly building energy consumption of the existing building was obtained through building submeters. The accuracies of these data-driven models for predicting energy usages of the two buildings are compared. The GMMR models outperform the adaptive filter methods in this study. Both the GMMR and adaptive filter methods meet the model calibration criteria defined by the ASHRAE Guideline 14. [ABSTRACT FROM AUTHOR]

Building retrofits provide a large opportunity to significantly reduce energy consumption in the buildings sector. Traditional building retrofits focus on equipment upgrades, often at the end of equipment life or failure, and result in replacement with marginally improved similar technology and limited energy savings. The Integrated System (IS) retrofit approach enables much greater energy savings by leveraging interactive effects between end use systems, enabling downsized or lower energy technologies. This paper presents a case study in Hawaii quantifying the benefits of an IS retrofit approach compared to two traditional retrofit approaches: a Standard Practice of upgrading equipment to meet minimum code requirements, and an Improved Practice of upgrading equipment to a higher efficiency. The IS approach showed an energy savings of 84% over existing building energy use, much higher than the traditional approaches of 13% and 33%. The IS retrofit also demonstrated the greatest energy cost savings potential. While the degree of savings realized from the IS approach will vary by building and climate, these findings indicate that savings on the order of 50% and greater are not possible without an IS approach. It is therefore recommended that the IS approach be universally adopted to achieve deep energy savings. [ABSTRACT FROM AUTHOR]

The paper investigates whether air-water heat exchanger with longitudinal fins along the air flow direction are an appropriate means to reduce pressure loss in mechanical ventilation systems, compared to conventional heating coils. To answer this question, theoretical calculations regarding pressure loss and heat transfer are performed and the results are checked by measurements. It is found that a longitudinal fin heat exchanger with 30 radially arranged fins and an overall length of 830 mm transfers the same heat flow as a conventional heating coil (∅ 125). The pressure loss only adds up to about 60% to 70%, so a reduction of 30% to 40% can be achieved. Additionally it is expected, that the longitudinal fin heat exchanger is more suitable for efficient low temperature heating systems, because of the counter-current heat transfer. The disadvantage of its increased length can be compensated by integrating the longitudinal fin heat exchanger in a duct silencer, which is required anyway. Hence, the energy efficiency of mechanical ventilation system can be increased. [ABSTRACT FROM AUTHOR]

With the gradual increase of building energy consumption in recent years, the application of phase change energy storage materials in buildings has become the focus of researches. Phase change material (PCM) embedded in the envelope can increase the building inertia to keep the temperature stable. It can also store energy and release energy when needed. In this paper, the system of PCM radiant floor combined with PCM wall attached with sunspace was established. The thermal characteristics and the energy consumption of the building are studied through analyzing the data from the experimental investigation including the temperature and the heat flux. Two different buildings were built between the PCM room and the conventional room with the same dimension and building. The main test conditions are divided into passive and active control stages by comparing the air temperatures, envelope temperatures, phase transition temperatures, heat fluxes and power consumptions between the two rooms. It is concluded that the average room temperature of the experimental room is 7.15 °C higher than that of the conventional room in the passive energy storage stage. While the average energy-saving rate of PCM room was 54.27% compared with the conventional room in the active control stage. [ABSTRACT FROM AUTHOR]

Building operations account for the largest proportion of energy use throughout the building life cycle. The energy saving potential is considerable taking into account the existence of a wide variety of building operation deficiencies. The advancement in information technologies has made modern buildings to be not only energy-intensive, but also information-intensive. Massive amounts of building operational data, which are in essence the reflection of actual building operating conditions, are available for knowledge discovery. It is very promising to extract potentially useful insights from big building operational data, based on which actionable measures for energy efficiency enhancement are devised. Data mining is an advanced technology for analyzing big data. It consists of two main types of data analytics, i.e., supervised and unsupervised analytics. Despite of the power of supervised analytics in predictive modeling, unsupervised analytics are more practical and promising in discovering novel knowledge given limited prior knowledge. This paper provides a comprehensive review on the current utilization of unsupervised data analytics in mining massive building operational data. The commonly used unsupervised analytics are summarized according to their knowledge representations and applications. The challenges and opportunities are elaborated as guidance for future research in this multi-disciplinary field. [ABSTRACT FROM AUTHOR]

With increased use of variable refrigerant flow (VRF) systems in the U.S. building sector, interests in capability and rationality of various building energy modeling tools to simulate VRF systems are rising. This paper presents the detailed procedures for model calibration of a VRF system with a dedicated outdoor air system (DOAS) by comparing to detailed measured data from an occupancy emulated small office building. The building energy model is first developed based on as-built drawings, and building and system characteristics available. The whole building energy modeling tool used for the study is U.S. DOE’s EnergyPlus version 8.1. The initial model is, then, calibrated with the hourly measured data from the target building and VRF-DOAS system. In a detailed calibration procedures of the VRF-DOAS, the original EnergyPlus source code is modified to enable the modeling of the specific VRF-DOAS installed in the building. After a proper calibration during cooling and heating seasons, the VRF-DOAS model can reasonably predict the performance of the actual VRF-DOAS system based on the criteria from ASHRAE Guideline 14-2014. The calibration results show that hourly CV-RMSE and NMBE would be 15.7% and 3.8%, respectively, which is deemed to be calibrated. The whole-building energy usage after calibration of the VRF-DOAS model is 1.9% (78.8 kWh) lower than that of the measurements during comparison period. [ABSTRACT FROM AUTHOR]

This paper presents test methods and results of assessment data necessary for determination of building energy consumption, based on measurements in an occupied typical single-family house in Poland. Comprehensive on-site measurements included the building envelope, heating system, domestic hot water system, ventilation system with heat recovery and indoor environment quality. A new wireless measurement system was used. The results obtained from the thermal diagnostics were used to analyze the energy consumption of the house in four different successive building modernization stages. The steps undertaken included improvement of wall and window insulation, installation of mechanical ventilation with heat recovery, improvement of house airtightness and replacement of the boiler and the hot water storage vessel with more efficient ones. Such steps are typical activities undertaken without a prior energy analysis by owners of single-family houses. Before thermal renovation the indicator of non-renewable primary energy was 243 kWh/(m 2 y) and after renovation this decreased to 147 kWh/(m 2 y). Proper selection of thermo-modernization measures would allow to reach the lower value of this indicator. However, it still would be higher than the required for newly built single-family houses in Poland. The execution of thermal diagnostics both before and after thermal renovation of the house enable the correct performance of that process and the achievement of greater energy savings. [ABSTRACT FROM AUTHOR]

There are several techniques for the performance of energy studies in buildings, where infrared thermography is widely used for the study of the composition of the envelopes, to find faults in the building materials and in the composition of the envelopes with influence on their thermal behaviour, as well as to detect areas with humidity. Common thermographic building interpretations are performed by a human operator, which involves a high level of subjectivity and mainly relies on the expertise of the operator. With the aim at minimizing this subjectivity and maximizing the accuracy of the inspections, this paper presents a procedure for the automation of thermographic building inspections mainly focused on thermal bridges. The procedure, in addition to detecting the thermal bridges by their geometric characteristics and their temperature differences with the surroundings, includes the computation of the thermophysical property of linear thermal transmittance of each candidate to thermal bridge, thus implying their characterization in addition to their detection. With this addition, together with a previous process of rectification of the thermal images analysed, the accuracy of the detection of thermal bridges regarding existing methodologies is improved in 15% considering the false positives and negatives obtained in each methodology. [ABSTRACT FROM AUTHOR]

Technological improvements of HVAC-systems, buildings and other energy consuming products can support a reduction of CO 2 emissions. The implementation of energy efficient technology is important to reach the climatic goals of the Paris agreement. As cities are the main contributors of greenhouse gas emissions, they offer great potential for implementation of energy efficiency measures and emission reduction. Thus, the question arises on how to support the implementation process. The main challenge is not only the implementation and usage of technologies, but also the optimisation of existing local instruments, processes and frameworks to efficiently support the implementation of energy strategies in communities. This paper summarises results of the Annex 63 − Implementation of Energy Strategies in Communities − within the Energy in Buildings and Communities Program (EBC) of the International Energy Agency (IEA). It includes procedures and best-practice examples to implement optimized energy strategies in communities. The implementation strategies deals with visions and targets, renewable energy strategies, legal frameworks, design of urban competition processes, tools supporting the decision making process, monitoring, stakeholder engagement, socio-economic criteria and organisation structures. For all of these strategic measures, specific guidelines were elaborated together with a worldwide network consisting of representatives from cities, urban and energy planners, consultants, universities and many other stakeholder groups. [ABSTRACT FROM AUTHOR]