Search

Highlights • The Passive House (PH) Standard is shown to facilitate nZEB implementation. • nZEB houses can be constructed at no extra cost over traditionally built dwellings. • The IEQ of the monitored PH outperform current building regulations dwellings. • The nZEB/PH exhibits low heating costs and high occupant satisfaction. • Overheating needs to be addressed through design and operation. Abstract Across Europe dwellings will be constructed to comply with nearly Zero Energy Buildings (nZEB) standards by 2020. This requires highly performing dwellings, an arena previously the preserve of the Passive House (PH) Standard. This study examines the operational performance and cost of a set of nZEB dwellings built as PH dwellings and thereby investigates the potential of the PH typology as a solution to nZEB. This investigation is undertaken with particular focus on the context of social housing, given that this will account for a considerable portion (c.40%) of housebuilding in the next 10 years within some EU jurisdictions. Detailed construction cost comparisons are presented for an nZEB compliant PH dwelling (which is part of a scheme of 12 such dwellings) and compared to a similar dwelling which is constructed to comply with the current building regulations in the Republic of Ireland. The Indoor Environmental (IEQ) conditions, energy consumption and monitored cost of heating and ventilation are also presented. In addition, the paper presents the results of a comprehensive occupant satisfaction survey and extensive post occupancy monitoring and analysis for the scheme of PH dwellings. Finally it compares the monitored performance with that of 20 "standard" and PH dwellings, a number of which are nZEB compliant. While the study uses data across the UK and Ireland it has relevance to a much wider context for the deployment of nZEB in Europe and housing globally, and in particular the provision of social housing. [ABSTRACT FROM AUTHOR]

Highlights • Non-intrusive techniques are proposed to infer occupancy from existing HVAC sensors. • Reheat energy use is reduced by more than 38% with better nighttime setback schedules. • Derived a comprehensive set of candidate anomalous zones using unsupervised learning. • Proposed approach can be readily applied to any commercial building with a BMS. Abstract The Heating Ventilation and Air Conditioning (HVAC) system of commercial buildings traditionally runs on a fixed schedule that does not take occupancy into account despite its huge variation over space and time, thereby wasting a lot of energy in conditioning empty or partially-occupied spaces. Occupancy information is essential to eliminate wasteful energy use with imperceptible impact on building operations and human comfort. This paper investigates the application of non-intrusive techniques to obtain a rough estimate of occupancy from coarse-grained measurements of sensors that are commonly available through the building management system. Various static and adaptive energy-efficient schedules are developed based on this approximate knowledge of occupancy at the level of individual zones. Our experiments in three large commercial buildings confirm that the proposed techniques can uncover the recurring occupancy pattern of the zones, and schedules that incorporate these occupancy patterns can achieve more than 38% reduction in reheat energy consumption while maintaining indoor thermal comfort. [ABSTRACT FROM AUTHOR]

Highlights • Solar-air energy heating system coupled with floor radiation system was presented. • We quantitatively evaluated the contribution of the solar house to energy poverty alleviation. • Energy consumption and indoor temperature were measured and compared. • We analysed the performance of the hybrid heating system in the solar house. Abstract Energy poverty is prominent in rural Qinghai-Tibet region, China. In sacrifice of thermal comfort and health, residents adaptively choose the biomass fuels and cheap coal for heating and cooking, as an approach to energy expenditure reduction. Considering renewable energy utilization is effective to replace the utilization of conventional fuel, this paper aims to examine the potential of using solar house for energy poverty alleviation. Based on a novel hybrid heating system, we renovated a rural building into a solar house in Wangtun village, Qinghai-Tibet region. The energy consumption and indoor air temperature of the building before and after reconstruction were field measured and comparatively analysed. Results indicate that the energy consumed by solar house with the hybrid heating system was 153.3 kWh/day lower than that by the old building. The average air temperature of the living room and two bedrooms in the solar house reached 12.8°C, 11.0°C and 10.2°C, about 8.78°C, 7.61°C and 6.82°C higher than temperatures of the rooms in the old building. Overall, the solar house has considerably improved indoor environments and provided people with better thermal comfort. It is concluded that solar house can be an effective approach to mitigate domestic energy poverty of the rural Qinghai-Tibet region. The findings may also instruct other rural regions in either China or other developing countries to address the issue of energy poverty. [ABSTRACT FROM AUTHOR]

An intensive study on the heating temperature aimed to create a satisfying indoor condition that should meet the residents’ thermal requirements in accordance to their daily behavior pattern. Unlike urban residents who stay indoors for a long time, rural residents constantly live in a dynamic environment: they often leave their residences and get busy with numerous labor activities. Therefore, the thermal sensation questionnaire survey performed in a steady environment is not suitable for them. To study the rural heating temperature in a dynamic environment, a three-years field investigation was performed in South Liaoning province. During the investigation, a continuous tracking method was adopted to record the residents’ behavior; objective environmental parameters test and subjective thermal sensation surveys were rigorously recorded. The results indicate that the residents behavior pattern exerts an influence on the heating temperature by influencing the residents’ clothing insulation. Heating temperature in auxiliary rooms can be directly determined by the residents’ thermal sensation, with the reasonable value of 11   °C. The influence of residents’ behavior on clothing should be considered when determining heating temperature in the master bedroom, the reported value of which is 13   °C. The heating temperature obtained by the thermal sensation fuzzy comprehensive evaluation model in paper reflects the rural residents’ real-life requirements on thermal environment, and it provides a reference for designing rural residences. [ABSTRACT FROM AUTHOR]

This paper investigates the climate change impact on cooling energy consumption of office buildings in four major architectural climate zones in China. The results show that there are apparent differences in the responses of monthly or yearly cooling loads to climatic change in different climates. Dominant factors affecting monthly cooling loads change from dry bulb temperature (DBT) to wet bulb temperature (WBT) with the climate zones changing from severe cold to hot. The yearly cooling load was mainly afftectd by DBT in severe cold climate, whereas the WBT had the dominant effect in the other climates. With the continuous warming climate, the yearly cooling load did not rise significantly for any climate zone except for severe cold climate zone where cooling load had a significant increase ( p  < 0.01). More importantly, the cooling load showed a weak decrease in Guangzhou. These results suggest that different measurements for buildings cooling energy conservation should be made in different climate zones. The humidity effect should be fully considered to improve buildings energy efficiency, especially in the hot climate zones. The continuous increase of temperature in the future does not necessarily increase cooling loads of buildings in the cold, hot summer & cold winter and hot summer & warm winter climate zones, and change trendency of humidity in the future need to be considered to predict cooling energy consumption. [ABSTRACT FROM AUTHOR]

In order to resolve the contradiction between the lack of building energy data and the need of more detailed information about energy performance, this paper presents a simplified building energy performance diagnosis method which can assess the energy performance from building level to system level with only limited information. Based on multiple-parameter regression between the whole building energy bills and outdoor weather data, the proposed diagnosis method uses the regression coefficients to identify the energy use of main systems (i.e., lighting-plug system and cooling/heating system), as well as other detailed information about physical properties of the building and cooling/heating system (i.e., cooling or heating load, the operation of cooling/heating system and the efficiency of cooling/heating system). One case study was conducted in an office building in China to test the application of this diagnosis method. In addition, the regression result of simulated energy consumption served as benchmark data to further diagnose the performance gap between the operation stage and the design stage and help locate the poor performance and key points of energy-saving. Finally, all diagnosis results have been verified by the performance data from advanced energy consumption monitoring system together with field surveys and measurements. [ABSTRACT FROM AUTHOR]

In Germany, policy-induced energy efficiency improvements typically aim at reducing primary energy consumption. Private households, on the contrary, pursue the maximization of wellbeing or, in microeconomics jargon, the maximization of utility of the occupants. There is a marked difference between upfront-calculated energy performance ratings (EPRs) and actual heat energy consumption (HEC). From an energy and environmental policy point of view, deviations of energy consumption from ex ante calculated EPRs is problematic, as it jeopardizes their usefulness for (prospective) homeowners, policy-makers and researchers relying on EPRs as a guidance. The EPR-HEC gaps reported are, apart from deviations from the mean aggregate values (i.e. heterogeneity), often attributed to anticipated or unanticipated behavioral effects. From an energy economist's point of view, energy rebound induced by lowered costs per unit of energy service due to increased energy efficiency is one explanation. The related literature almost entirely treats building-specific EPRs as universal standards, trying to explain the empirically observed discrepancies. In this paper, we investigate to what extent the current EPR scheme in place in Germany today can address behavioral issues. To this end, based on two different multi-family housing data sets for Germany and regression analysis, we empirically investigate the deviations between EPRs used in the regulatory norms and the HEC levels observed in reality. We find evidence that it is not necessarily the behavioral dimension, but rather the static and mostly technically guided calculation of the EPRs itself that accounts for the major part of the deviations. We conclude that there is indeed a need for further improvements in the field of EPR regulation and methodology. [ABSTRACT FROM AUTHOR]

This paper is the second part of a two-part series which investigates the energy saving potentials in a novel mechanical ventilation system by replacing terminal dampers with decentralized fans. The present study (Part 2) proposes a method to control the main fan as well as the decentralized fans in such a way that the system is balanced for different demand requests. The control method comprises two levels, zone level and system level control, and is based on measuring static pressure in the duct system similar to the energy-efficient control proposed for the conventional ventilation system in Part 1. Two identical experimental mock-ups, one with the dampers and one with the decentralized fans were used in a laboratory environment to evaluate the performance of the novel ventilation system and to compare the two ventilation systems from power use point of view. Experimental results indicate the ability of the proposed method to control the ventilation system with decentralized fans. The measured power use of the two ventilation systems was almost the same for the range of tested total airflow rates, except when the total airflow rate was rather low. Calculations, however, reveal an energy saving potential of around 30% depending on the efficiency of the main fan. [ABSTRACT FROM AUTHOR]

Housing Stock Energy Models (HSEMs) play a determinant role in the study of strategies to decarbonise the UK housing stock. Over the past three decades, a range of national HSEMs have been developed and deployed to estimate the energy demand of the 27 million dwellings that comprise the UK housing stock. However, despite ongoing improvements in the fidelity of both modelling strategies and calibration data, their longevity, usability and reliability have been compromised by a lack of modularity and openness in the underlying algorithms and calibration data sets. To address these shortfalls, a new open and modular platform for the dynamic simulation of national (in the first instance, the UK) housing stocks has been developed—the Energy Hub (EnHub) . This paper describes EnHub’s architecture, its underlying rationale, the datasets it employs, its current scope, examples of its application, and plans for its further development. In this we pay particular attention to the systematic identification of housing archetypes and their corresponding attributes to represent the stock. The scenarios we analyse in our initial applications of EnHub, based on these archetypes, focus on improvements to housing fabric, the efficiency of lights and appliances and of the related behavioural practices of their users. In this we consider a perfect uptake scenario and a conditional (partial) uptake scenario. Results from the disaggregation of energy use throughout the stock for the baseline case and for our scenarios indicate that improvements to solid wall and loft thermal performance are particularly effective, as are reductions in infiltration. Improvements in lights and appliances and reductions in the intensity of their use are largely counteracted by increases in heating demand. Housing archetypes that offer the greatest potential savings are apartments and detached dwellings, owing to their relatively high surface area to volume ratio; in particular for pre-1919 and inter-war epochs. [ABSTRACT FROM AUTHOR]

In the US, building sector consumes approximately 41% of all U.S. primary energy and 70% of all generated electricity. Office building uses the largest percentage of primary and derived energy in the commercial buildings sector. Therefore, energy saving is the primary target of building renovation and equipment retrofit. To estimate energy savings after a renovation or equipment retrofit, measurement and verification (M&V) must be conducted based on energy use models or benchmark tools to track and assess savings. Although some methods and modeling tools are applied more often, there is no well-accepted model formulation methodology and some methods have high uncertainty. This paper discusses a method to formulate the energy use model as an inverse regression model via an advanced and robust high-dimensional data analysis method. According to the inverse regression model, independent variables determined to be most influential to the energy consumption should be metered prior to building retrofit or audit. To select the most influential variables, this research considers a comprehensive set of potential key variables to avoid underspecified model issue. The models established by this method are observed to have great power in predicting the building energy consumption. More importantly, through the variable selection procedure, the method identifies key variables that should be monitored to continuously improve the building energy performance. A simulation study and a case study are presented to show the effectiveness of the new approach. [ABSTRACT FROM AUTHOR]

To promote energy saving in the residential sector Directive 2012/27/EU has set the obligation for buildings supplied by central heating sources to install individual heat metering and accounting systems. However, in social housing, bills based exclusively on individual consumption should be unfair due to some unfavourable situations, such as first and top floors, presence of unheated common spaces, north oriented dwellings. Nevertheless, fair heat accounting rules should be introduced especially in social housing buildings, which are often thermally underperforming with inefficient heating plants and tenants are commonly low-income people and elderly. On the other hand, common regulations for heat accounting providing compensation to avoid inequalities among tenants have not been set and different approaches on this topic are present among EU Member States. In this paper the authors present a new heat accounting method for social housing based on the estimation of extra-consumptions due to building inefficiencies. According to this method, extra-consumptions are charged to all tenants in order to encourage energy efficient retrofit interventions. Finally, the new method has been experimented in a typical social housing building in Italy and compared to other methods applicable in EU, evidencing some advantages and weaknesses. [ABSTRACT FROM AUTHOR]

One of the long-term challenges outlined within the UK Government's Energy White Paper (2011) is to cut both greenhouse gas (GHG) emissions and energy bills by improving household energy efficiency. As such, several energy-related initiatives have, in recent years, been promoted including the Community Energy Saving Programme. In this study, we explore how patterns of user consumption as influenced by occupant awareness and behaviour, can both positively and negatively influence policy initiative delivery. In so doing, we present the results from an extensive pre- and post-retrofit home energy use and performance survey of 150 properties located in Nottingham's Aspley ward, home of one of England's pilot CESP schemes. Our results show that whilst this specific initiative significantly improved home conditions and reduced energy consumption, it failed to achieve the predicted £300 annual savings on household energy bills. This was found to be largely attributed to occupants’ ingrained habits towards household energy use, higher comfort level preferences, (lack of) energy consumption awareness, and insufficient information provided to help residents better manage their home energy use following the retrofit. By exploring some of the core lessons learned from the survey, this research seeks to inform and improve the uptake and delivery of future retrofit initiatives. [ABSTRACT FROM AUTHOR]

[Display omitted] The school building sector has a pivotal role to play in the transition to a low carbon UK economy. School buildings are responsible for 15% of the country's public sector carbon emissions, with space heating currently making up the largest proportion of energy use and associated costs in schools. Children spend a large part of their waking life in school buildings. There is substantial evidence that poor indoor air quality and thermal discomfort can have detrimental impacts on the performance, wellbeing and health of schoolchildren and school staff. Maintaining high indoor environmental quality whilst reducing energy demand and carbon emissions in schools is challenging due to the unique operational characteristics of school environments, e.g. high and intermittent occupancy densities or changes in occupancy patterns throughout the year. Furthermore, existing data show that 81% of the school building stock in England was constructed before 1976. Challenges facing the ageing school building stock may be exacerbated in the context of ongoing and future climate change. In recent decades, building stock modelling has been widely used to quantify and evaluate the current and future energy and indoor environmental quality performance of large numbers of buildings at the neighbourhood, city, regional or national level. Building stock models commonly use building archetypes, which aim to represent the diversity of building stocks through frequently occurring building typologies. The aim of this paper is to introduce the Data dRiven Engine for Archetype Models of Schools (DREAMS), a novel, data-driven, archetype-based school building stock modelling framework. DREAMS enables the detailed representation of the school building stock in England through the statistical analysis of two large scale and highly detailed databases provided by the UK Government: (i) the Property Data Survey Programme (PDSP) from the Department for Education (DfE), and (ii) Display Energy Certificates (DEC). In this paper, the development of 168 building archetypes representing 9,551 primary schools in England is presented. The energy consumption of the English primary school building stock was modelled for a typical year under the current climate using the widely tested and applied building performance software EnergyPlus. For the purposes of modelling validation, the DREAMS space heating demand predictions were compared against average measured energy consumption of the schools that were represented by each archetype. It was demonstrated that the simulated fossil-thermal energy consumption of a typical primary school in England was only 7% higher than measured energy consumption (139 kWh/m2/y simulated, compared to 130 kWh/m2/y measured). The building stock model performs better at predicting the energy performance of naturally ventilated buildings, which constitute 97% of the stock, than that of mechanically ventilated ones. The framework has also shown capabilities in predicting energy consumption on a more localised scale. The London primary school building stock was examined as a case study. School building stock modelling frameworks such as DREAMS can be powerful tools that aid decision-makers to quantify and evaluate the impact of a wide range of building stock-level policies, energy efficiency interventions and climate change scenarios on school energy and indoor environmental performance. [ABSTRACT FROM AUTHOR]

• A comprehensive review of urban building energy modeling approaches and methods. • Generalized UBEM frameworks are needed to account for application-specific models. • UBEM domain requires integration with GIS, BIM and cloud-based computing platforms. • Advanced data-driven methods are promising techniques to model building stocks. The world has witnessed a significant population shift to urban areas over the past few decades. Urban areas account for about two-thirds of the world's total primary energy consumption, of which the urban building sector constitutes a significant proportion approximately 40%. Stakeholders such as urban planners and policy makers face substantial challenges when targeting sustainable energy and climate goals related to the buildings' sector, i.e. to reduce energy use and associated emissions. Urban energy modeling is one possible solution that leverages limited resources to estimate building energy use and support appropriate policy formation. Over the past few years, there have been only a few review studies on urban building energy modeling approaches. These studies lack an in-depth discussion of the challenges and future research opportunities related to data-driven, reduced-order, and simulation-based modeling methods. This paper proposes Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis of approaches, methods and tools used for urban building energy modeling. Furthermore, this paper proposes a generalized framework based on existing literature for different urban energy modeling methods. The aim of this study is to assist urban planners and energy policymakers when choosing appropriate methods to develop and implement in-depth sustainable building energy planning and analysis projects based on limited available resources. [ABSTRACT FROM AUTHOR]

Under the background of global warming, the world is experiencing many extreme climate events. The temperature fluctuation associated with climate change is often considered as a primary driving force of electricity consumption. To better explore the impact of temperature on electricity consumption, this paper aims to analyze the question from the perspective of income growth, i.e., the moderating effect of income growth on the response of urban residential electricity to temperature changes. Using the unbalanced panel data of 23 cities in the Yangtze River Delta Urban Agglomeration from 2004 to 2015, we construct the response model of residential electricity consumption to income growth, heating degree days, cooling degree days and their interactions. The results show that heating demand, cooling demand and income growth significantly increase residential energy consumption. However, urban residential disposable income growth weakens the positive effect of heating demand and cooling demand on residential electricity consumption. Because, urban residents have obvious demand for improving indoor heating comfort and cooling comfort, the moderating effect of income is shown as the crowding-out effect. Residents may adjust the structure of heating energy consumption (from electricity to natural gas) and upgrade their electrical equipment to high energy efficiency equipment, thereby reducing residential electricity consumption. This study also indicates that urban residential energy consumption is very sensitive to changes in permanent populations. The main contribution of this study is proposing the hypothesis of the negative moderating effect of income growth on temperature changes and expanding the response model of residential electricity consumption to temperature changes. Finally, this paper also proposes the adaptive strategies and policy implications for climate change. [ABSTRACT FROM AUTHOR]

• This paper determines the relationship between urban form and building energy use. • The surface-to-volume ratio and obstruction angle of buildings are important. • Consideration of the heating energy type and building use can yield reliable result. • The urban heat island effect exerts an impact on the electricity use in some months. • Correlation variation is caused by the building permit date. In the climate change era, urban geometry serves to ensure sustainability for urban planners since it notably affects building energy consumption. However, previous studies have mainly conducted experimental investigations under hypothetical conditions. Without empirical evidence, it is difficult to implement these findings under real conditions. This paper examined empirical data to determine the urban geometry effect on building energy consumption in a large, complicated urban area. Urban geometric parameters, including the surface-to-volume ratio, obstruction angle, and building orientation, were measured across Seoul, and their correlations with building energy use were analyzed. The results show that the surface-to-volume ratio and obstruction angle are important, despite the empirical data complexity. Additional data analysis indicates that the heating energy type and building use consideration yields more reliable correlation coefficient trends. The potential impacts of natural ventilation and the urban heat island (UHI) effect on building energy consumption were examined. The UHI effect, rather than shadows on buildings, predominantly impacts the building electricity use in August and September. The correlation variation caused by the building permit date was investigated. The results in this paper could help urban planners and designers develop blueprints to control urban geometry and establish relevant design guidelines. [ABSTRACT FROM AUTHOR]

[Display omitted] • Development of two benchmark models for post-world war II residential buildings. • Average energy use intensity per household (archetypes A and B) were 166 and 155 kWh/m2/year. • Models validated with four-year monitoring data on energy consumption. • Households are dominated by senior adults and their occupancy profiles are presented. • Findings on energy needs and use intensity are useful in temperate and continental climates. In the context of the European carbon neutrality targets, building benchmarks are a key issue for the renovation of existing buildings. Although there are various benchmark methods for energy efficiency characterization, their application to the residential sector is still limited. This paper developed two building simulation models for post-world war II houses in Belgium based on data from post-occupancy measurements and field survey campaigns. The study reports the energy characteristics and occupancy profiled of detached single-family houses. An analysis of energy consumption (electricity and natural gas) and a walkthrough survey were conducted between 2016 and 2019. The benchmark model's validity has been further checked against public statistics and verified through model calibration and monthly energy bill comparison. Two reference models representing 633.702 post-WWII single-family houses in Belgium were created and validated. The first archetype has an average energy use intensity of 166 kWh/m2/year and represents detached single-family houses built between 1945 and 1969. The second archetype has an average energy use intensity of 155 kWh/m2/year and represents detached single-family houses built between 1970 and 1990. The paper provides a timely opportunity to evaluate the real performance of post-world war II most common archetypes concerning design assumptions and how building professionals can turn the energy performance gap challenge to their advantage. The findings on energy needs and intensity are useful for creating future renovation scenarios for similar archetypes in Western European countries. [ABSTRACT FROM AUTHOR]

• A mathematical model to calculate the LCOS for reversible fuel cells and Li-ion batteries. • Techno-economic model for a standalone PV solar system in green energy buildings. • Uncertainty and simulation cost analyses of energy storage systems used in green buildings. Energy storage systems (ESSs) were introduced to overcome the risks posed by energy curtailment. In this paper, we compare the Levelized cost of storage (LCOS) for PEM Reversible Fuel Cells, Solid Oxide Fuel Cells, and Lithium-Ion Batteries in three different locations in the United States (Tucson, Seattle, and Rochester) that are characterized by different energy consumption profiles. This research examines three commercial buildings of different sizes: small, medium, and large, with three occupancy profiles: low, medium, and high energy consumption in each of the three locations. The objective is to quantify the impact of uncertain demand and uncertain financial parameters on the LCOS. The results show that all ESSs can represent an attractive alternative to energy curtailment regardless of the building size or the occupancy profile. Further, the results show that fuel cells are a better fit for demand uncertainty as they are more resilient to changes and remain attractive despite that. The uncertainty analysis showed that the minimum LCOS was $0.049/kWh, and it was for a PEM-RFC in a small building in Tucson under high energy consumption behavior. Oppositely, the maximum LCOS was $0.679/kWh, and that was for a large building in Seattle under high energy usage occupancy as well. [ABSTRACT FROM AUTHOR]

• The solar louver was proposed to increase direct heat gaining for building. • Effects of blades' angle and characteristics on heat gain of solar louver was quantified. • Indoor temperature was higher and temperature field was more uniform with coupled solar louver. • By comparison with non-coupled solar louver, solar louver in maximum light transmittance mode decreased active heating supply capacity by 33.8%. Promoting solar energy for heating is of great significance for achieving the national goals of "double carbon" goals. Increasing the heat gain of window space could improve the solar energy utilization in congested urban environments. The paper proposed a new heating type for active solar house by solar louver. Thermal effects validation on solar louver were studied by field-test and simulation analysis. A solar louver model was established on the platform of MATLAB and TRNSYS. The simulated results showing that heat gain of solar louver reached the largest when blades' angle was about 10°. Solar louver with high reflectivity and low thermal emissivity could capture more heat gain. The in-situ experiments of active solar house coupling solar louver were carried out in Ordos. From 24th to 29th Jan. 2022, all-day comparative experiments of thermal effects in actual winter-weather were conducted. The experimental results indicating that solar louver in maximum light transmission mode was preferred in active solar house. Compared with the condition of solar louver in overlapped light transmittance mode, maximum light transmission mode could obtain 17.7% higher heat gain. Indoor temperature was more higher and temperature field was more uniform. Moreover, by comparison with the non-coupled solar louver, solar louver in maximum light transmittance mode decreased active heating supply capacity by 33.8%, which could reduce energy consumption of building. The main work here provided a theoretical basis for performance optimization of active solar house. [ABSTRACT FROM AUTHOR]

The rise of clean energy such as solar energy provides a new idea to optimize the energy structure, and Photovoltaic thermal (PVT) heat pump system is one of the mainstream development at present. The wrong sensor data will have a great impact on the control and efficiency of the whole PVT heat pump system. In order to deal with this situation, Virtual in-situ calibration (VIC) based on Bayesian inference and Markov chain Monte Carlo (MCMC) is applied to PVT system. This paper presents a generic sensor fault diagnosis and calibration method for building energy systems using a PVT heat pump system as an example. Firstly, the data generated based on the mathematical model of PVT system is used to test the feasibility of VIC in this system. The results show that VIC can well reduce the systematic error and random error of the sensor. However, with the further study, it is found that in the actual system, it is impossible to establish a mathematical model which can reflect the relationship between the sensors with high accuracy. The VIC may fail or even have worse calibration results. Therefore, in order to solve the above problems, this study suggests using sparse autoencoder model instead of mathematical model. The model proved to be more accurate and to reflect the interconnectedness of the sensors, which helped the implementation of the VIC. The calibration method based on sparse autoencoder (SAE) can calibrate not only a single sensor in the PVT heat pump system, but also multiple sensors in a local area. After sparse autoencoder Virtual in-situ calibration (SAE-VIC) calibration, systematic and random errors of all sensors can be effectively reduced, and the accuracy of sensor calibration can reach more than 90 %. [ABSTRACT FROM AUTHOR]

Energy studies of buildings are becoming more widespread as stakeholders strive to improve energy efficiency and reduce carbon emissions. As a result, there is an increased need for novel numerical techniques to automatically calibrate building energy models (BEMs) for these energy studies. In this paper, a new automated building calibration methodology was developed, which uses a surrogate (i.e. , meta) multilayer perceptron artificial neural network (ANN) to infer unknown building parameters. Typically, in the field of BEM calibration, surrogate models are used as a time-saving technique. Instead of running the building simulation software during each iteration of an optimization search, a surrogate model can approximate the output of the BEM very quickly, leading to much faster optimization of the unknown building parameters. However, we show that, once trained, the surrogate model itself can be used to find the unknown building parameters. Since the surrogate model is differentiable, gradient descent can be used to find the building parameters that minimize the error between true and predicted metered energy consumption. Previous surrogate modeling approaches for calibrating BEMs map only the unknown building parameters to the BEM's output. As a result, they are blind to the effects of other crucial variables like weather and the building's schedules. On the other hand, our surrogate ANN model accounts for these other predictors of energy consumption. With this advantage, our method was able to outperform a powerful black box optimizer when finding 14 unknown building parameter values in a controlled case study with hourly energy data. In a real metered data case study, our ANN method and the black box optimizer performed similarly on average, but our method had less variance in performance across trials. [ABSTRACT FROM AUTHOR]

• A novel frosting suppression control strategy for air source heat pump is developed. • Frosting suppression can be effectively achieved by using the control strategy. • Heating duration under the novel control strategy is increased by up to 157.68 % • Daily defrosting duration and energy consumption are reduced by 36.30 % and 32.96 % • The novel control strategy can meet required heating capacity with a high COP value. Effective frosting suppression was crucial to further wider applications of air source heat pumps (ASHPs). However, existing frosting suppression measures may not be fully implementable in practice due to various reasons such as short service life, complex manufacturing process and high costs. A condensing-frosting performance map was obtained in a previous study to enable a comprehensive evaluation of the frosting suppression and heating performances of a variable speed (VS) ASHP unit. Therefore, the maps could also provide a basis for developing a novel control strategy for frosting suppression, while still meeting the requirement of desired output heating capacity. As a result, an experimental study on developing a novel control strategy based on the condensing-frosting performance maps has been carried out using an experimental VS ASHP unit, and the study results are presented in this paper. The experimental results suggested that the experimental VS ASHP unit under the proposed novel control strategy could not only achieve effective frosting suppression, but also output the required heating capacity at high COP values whenever possible. By using the developed novel control strategy, the heating duration in one cycle could be increased by up to 157.68 %, and the daily defrosting frequency, duration and energy consumption reduced by up to 59.09 %, 36.30 % and 32.96 %, respectively. The proposed novel control strategy was straightforward and simple to implement, and could contribute to the further development of frosting suppression for ASHPs. [ABSTRACT FROM AUTHOR]

Optimal planning and operation studies of modern energy systems are tied up with medium to long-term predictions of energy demand. Deep learning algorithms have recently become significantly useful in this regard because of their capacity to learn load patterns with a high degree of fluctuation and uncertainty. Yet, empirical evaluation of different configurations of deep learning models, for enhancing prediction accuracy, has been mostly ignored in the preceding literature. In this paper, different architectures of deep recurrent neural networks (DRNNs) are explored and adjusted specifically for the purpose of performing medium- and long-term predictions of energy demands. We intend to create a bespoke DRNN for heating and electricity consumption prediction with a 1-h resolution. Moreover, hyperparameter optimization, which is a time-consuming and rigorous task in deep learning algorithms due to their abundance, dependence on the particular application, and empirical nature, is studied comprehensively. In this respect, the space of possible configuration variables is thoroughly explored to identify a collection of hyperparameters that enables the DRNN model to more accurately forecast energy consumption. We also benchmark the proposed algorithm's performance against other data-driven models, namely support vector machine (SVM) and gradient boosting (GB) regression. The proposed model outperforms SVM by 5.4% and GB regression by 7.0% in terms of energy forecasting accuracy. [ABSTRACT FROM AUTHOR]

Increasing the building renovation rate is a key initiative to drive energy efficiency in the sector and contribute to the European Green Deal. Within this paper, the BIM-based building renovation decision support system RenoDSS is described. RenoDSS (i) automatically generates renovation scenarios based on user preferences and the IFC representation (including second level space boundaries data) of the current building configuration (baseline), (ii) calculates baseline and renovation scenario KPIs, and (iii) identifies the most suitable scenarios by comparing the scenarios' energy, finance, and sustainability KPIs. Validation conducted at two pilot sites shows that RenoDSS reduces the time for (i) identifying potential renovation scenarios, (ii) calculating their energy and LCA/LCC KPIs, and (iii) producing an IFC file for each renovation scenario by around 80% compared to tool-supported manual means. RenoDSS is used by (i) renovation designers that want to optimize the thermal building hull in combination with PV, solar thermal, and HVAC, as well as (ii) decision makers who want to collaboratively identify the most promising renovation option out of the ones generated by RenoDSS. [ABSTRACT FROM AUTHOR]

Effective planning and targeted implementation of building energy efficiency programs explicitly require robust knowledge on significance pattern of involving factors for precision model-based estimates of potential energy savings. Though extensively adopted for factor significance characterization, prior sensitivity analysis (SA) frameworks fail to capture typical influences attributed to commonly used categorical factors. Moreover, they often assume the assessed factors are independent which can be invalid in many cases to generate misleading outcomes. To overcome these observable defects, this paper develops a novel integrated stochastic level-based sensitivity analysis approach for exhaustively patterning effectual significance of all available building factors of varied type while incorporating diverse factor interactions on validated reliability. While the integration of categorical factors is achieved through factor-type dependent variation levelling, the random factor interactions are apprehended by stochastic sampling and nonlinear support vector network computing to accommodate collinearity trap. Multivariate clustering identifies significance pattern via a composite multi-statistic sensitivity metric derived from stochastic energy responses. Case results of 289 U.S. residential houses validate its utility. This presented approach benefits to perform reliable full sensitivity investigations and analyses under collinearity for building energy systems with verifiable robustness. [ABSTRACT FROM AUTHOR]

The optimization of energy consumption in buildings' HVAC systems plays a crucial role in reducing greenhouse gas emissions worldwide. In new and deeply-renovated buildings, characterized by modulating terminal units and aiming at the maximum exploitation of renewables, an accurate hydraulic balance of the distribution network may become critical, resulting in a significant increase of pumping energy consumptions and a deterioration of indoor thermal comfort conditions. In the present paper, we compare the performance of traditional manual balancing valves and new pressure independent control valves (PICVs), used to balance the hydronic loop of HVAC systems. To perform this analysis, a new MATLAB-Simulink model has been specifically developed to simulate the behavior of PICVs and has been used in an application case study. The efficiency of manual and pressure independent control valves is evaluated numerically by simulating a multi-zone distribution network under variable operating conditions and considering different control strategies of the circulating pump. Results show that in off-design conditions, when some of the branches of the hydraulic network are closed, traditional manual balancing valves are not able to guarantee the nominal mass flow rate in the remaining loops. On the contrary, no significant variations of water mass flow rate are observed when PICVs are adopted, even in partial load conditions. Despite their additional cost, PICVs allow to ensure better indoor thermal comfort sensations for individuals, avoiding under/over-heating of rooms and, moreover, yield a decrease of the pump electric consumption with respect to traditional manual balancing valves. In addition, based on the obtained results, general rules concerning the adoption of PICVs are provided, depending on the extension of the hydraulic loop and the adopted pump control logic. The reported results highlight the role that PICVs can play to ensure energy savings in HVAC systems without penalizing users' indoor comfort conditions. [ABSTRACT FROM AUTHOR]

• Investigated climate change impacts on office building performance in Brussels. • Applied a long-term climate change sensitive system sizing and design approach. • Time-integrated discomfort analysis showed a major surge in overheating by 2100. • Long-term climate change resistivity is ensured in reference building until 2100. • Primary energy use and GHG emission findings are useful in mixed humid climates. As climate change continues, it is expected that the risk of overheating will rise in both new and existing buildings in mixed humid climate zones in Europe. This study introduced a novel climate change sensitive sizing and design approach for cooling and heating systems in nearly zero-energy office buildings in Brussels, Belgium, for different weather scenarios. This approach considered the long-term effects of climate change on building performance. The climate change effects were assessed using current and future climate data from the regional atmospheric model, MAR. To demonstrate the approach, a case study of a nearly zero-energy office building in Brussels was conducted. The reference building model was first calibrated using monthly energy use data from the year 2019 using ASHRAE Guideline 14. Then, the building was evaluated with different HVAC strategies and their performances were quantified. The results indicated an increase in overheating as high as 1.2 °C and cooling energy use as high as 13.5 kWh/m2 and a decrease in overcooling as low as 0.3 °C and heating energy use as low as 10.9 kWh/m2 in the reference building by the end of the century. In addition, due to climate change sensitive sizing and design approach coupled with optimal sizing, the reference building was climate change resistant towards the worst-case scenario by end of the century with up to 3.7 for climate change overheating resistivity and 20.2 for climate change overcooling resistivity. Finally, the paper provided recommendations for future practice and research based on the study findings. [ABSTRACT FROM AUTHOR]

• Building performance simulation is powerful but computationally expensive. • Machine Learning (ML) surrogate models can reduce simulation times significantly. • Building energy and peak loads are predicted under different operational settings. • Extreme gradient boosting outperformed other methods. • Linear regression showed the lowest training time with competitive predictive power. Building Performance Simulation (BPS) is a powerful and widely used technique to evaluate building design and operation strategies prior to construction or retrofitting. However, BPS models often have high computational costs, which is particularly limiting for applications that require a significantly large number of simulations, such as building design optimization or uncertainty analyses. To overcome this gap, researchers have turned to surrogate modeling, where a mathematical model, such as a machine learning algorithm, is trained to mimic the performance of a BPS, allowing to test numerous building design/operation configurations at low computational costs. Past studies have applied surrogate BPS modeling to predict the impact of building design parameters on energy performance. However, few have considered building operational parameters, such as occupancy, equipment and lighting usage, and thermostat setpoints, which significantly impact energy consumption and peak loads, especially in harsh climate conditions. This paper presents a unique evaluation and comparison of machine learning algorithms as surrogates to BPS predictions of building performance (energy consumption and peak loads) under different operational settings. Results indicate that Extreme Gradient Boosting outperformed all other methods in predictive accuracy, with R2 values reaching as high as 0.99 for some models. In contrast, linear regression models were the fastest to train and easiest to interpret while still achieving competitive prediction accuracies (R2 values > 0.9). This work provides direct evidence of the machine learning surrogate models' ability to accurately predict building performance under different operational settings. It also offers unique insights into the strengths and weaknesses of white-box and black-box predictive modeling approaches and the effect of dataset size on the results. [ABSTRACT FROM AUTHOR]

• Provided a platform to test various residential building control systems. • Used probability of occupancy to determine setback temperature when unoccupied. • Integrated optimizer with adaptive and occupancy control. • Generated dynamic electricity tariff based on wholesale electricity pricing. • Evaluated the peak load shaving potential with dynamic pricing and optimization. Energy consumption in buildings continues to rise with increased deployment of energy-consuming equipment such as Heating, Ventilation, and Air Conditioning (HVAC) amid a growing world economy. Renewable energy is projected to comprise a majority of the future electricity supply, but the intermittent nature of renewables means that consumption must respond to dynamic supply for optimal utilization. This paper proposes a novel HVAC control strategy for residential buildings using the adaptive comfort model, considering occupancy through probability and real-time information, and optimizing the HVAC schedule to reduce cost, maintain thermal comfort, and respond to the dynamic availability of renewable energy while being generalizable to different situations. To validate this approach, the Universal CPS Environment for Federation (UCEF) co-simulation platform is used to connect advanced building controls with the building energy simulation software EnergyPlus. Simulations are performed for a residential building in Sacramento, CA during a typical summer week. Economic impacts, energy consumption, and thermal comfort are analyzed for traditional, adaptive, and occupancy-based control strategies under demand-based, tiered, and fixed electric tariff systems. Simulation results show that occupancy consideration, adaptive thermal comfort, and optimization can reduce cost by 50.1 %, electricity consumption by 52.9 %, and discomfort by 56.2 % compared to traditional fixed setpoints. The ability of the proposed HVAC control strategy to shift energy consumption away from peak times under a demand-based tariff system is qualitatively analyzed and findings suggest that maximum load-shifting on a grid-scale is attained using occupancy consideration with optimized control and demand-based pricing. For individual residential buildings, similar economic benefits can be gained using the less-complex adaptive HVAC control strategy with existing tiered or simple electric tariff systems. [ABSTRACT FROM AUTHOR]

This paper investigates the use of dilated causal convolutional neural networks for fine-grained temporal forecasting of building zone states. Specifically, we build and evaluate models using a small set of exogenous features (e.g., external temperature) to autoregressively predict zone airflow setpoints every minute for a 24-h prediction window. We carefully explore the trade-off between generality and specificity in these models, training and evaluating them based on zone, zone type, month, season, and combinations thereof. When evaluated for a commercial office building in Eastern Washington with 16 zones served by variable air volume air handling units, we find that the highest performance comes from a zone-specific, season-agnostic approach; with it, we obtain an R 2 of 0.704 (averaged over zones) and an average normalized root mean square error (nRMSE) of 0.111. In contrast, the most general model (trained across all zones and seasons) yields an R 2 of only 0.416 and a nRMSE of 0.168, while a baseline zone-specific reduced order model obtains 0.443 R 2 and 0.159 nRMSE. We also report on factors affecting airflow forecasting performance, on the ability of models trained on a specific zone to generalize to other zones, and on the capability of those models trained on a specific month to generalize to other months. [ABSTRACT FROM AUTHOR]

[Display omitted] • 30,000 full-text building-related articles have been extracted for text mining. • Data-science, energy efficiency, and lifecycle phases relations have been drawn. • Word embeddings model has been used to extract the relationship between keywords. • Gaps, opportunities, and potential future directions have been discussed. The ever-changing data science landscape is fueling innovation in the built environment context by providing new and more effective means of converting large raw data sets into value for professionals in the design, construction and operations of buildings. The literature developed due to this convergence has rapidly increased in recent years, making it difficult for traditional review approaches to cover all related papers. Therefore, this paper applies a natural language processing (NLP) method to provide an exhaustive and quantitative review.Approximately 30,000 scientific publications were retrieved from the Elsevier API to extract the relationship between data sources, data science techniques, and building energy efficiency applications across the life cycle of buildings. The text-mining and NLP analysis reveals that data sciences techniques are applied more for operation phase applications such as fault detection and diagnosis (FDD), while being under-explored in design and commissioning phases. In addition, it is pointed out that more data science techniques that are to be investigated for various applications. For example, generative adversarial networks (GANs) has potential in facilitating parametric design; transfer learning is a promising path to promoting the application of optimal building operation; [ABSTRACT FROM AUTHOR]

[Display omitted] • Discussed the current status of cooing technologies in data centers. • Introduced and reviewed existing phase change cooing in data centers. • Compared and summarized the characteristics of phase change cooling systems. • Analyzed the application limitations of phase change cooling technologies. • Explored the future research direction of phase change cooling technologies. With the advent of big data era, data centers (DCs) related energy use accounts for approximately 3% of the global electric power consumption. As the augmentation of data-processing performance and thermal density in DCs, its energy use will undoubtedly continuously burst. Meanwhile, the potential safety hazards, caused by IT equipment local overheating, threaten the safe operation and restrict the further development of DCs. Thus, efficient cooling approaches should be applied in DCs to ensure its safe operation and optimize its thermal environment and cooling efficiency. Phase change cooling (PCC) technology is regarded as one of the effective and widely-used cooling methods, which have been applied in DCs for several years. In this paper, the up-to-date PCC technologies are reviewed and summarized, as well as the latest progress in DC cooling field. Four main PCC technologies are discussed in this paper, namely independent heat pipe cooling, integrated heat pipe cooling, two-phase immersion cooling, and cold storage systems. Finally, the shortcomings of the current researches on the PCC methods are summarized, while some suggestions for future researches are provided to promote the application of PCC technologies and achieve safe operations and energy savings in data centers. [ABSTRACT FROM AUTHOR]

Based on household-level survey data collected from Qinghai Province of China, we estimated the greenhouse gas (GHG) and air pollutant emissions from spatial perspectives, including household energy consumption in pastoral, agropastoral, and agricultural zones. The findings suggest that the total annual GHG and pollutant emissions per capita in the area was 2296.32 kg per year. The highest amount of pollutants was emitted from the pastoral zones, followed by the agropastoral and agricultural zones. CO 2 is the primary GHG emitted by household energy consumption. Dung burning was the cause of the high PM 2.5 emissions in the pastoral areas, while the use of coal was the primary cause of GHG and pollutant emissions in the agropastoral and agricultural zones. • We used empirical data from rural households in Qinghai, China. • This paper indicates the reliance of local energy consumption on non-commercial energy. • We estimate greenhouse gas and pollutant emissions resulting from solid-fuel combustion. • We compare indoor pollutant emissions from pastoral, agropastoral, and agricultural zones spatially. • This paper provides policy recommendations for energy transition towards clean energy. Energy consumption in the household sector has become an important issue in China's energy consumption and an important unit of China's clean energy transformation. Currently, the potential air pollution, carbon emissions and health risks caused by energy consumption in many areas cannot be ignored, and refined and regionalized index-based research data necessary to support decision making are lacking. Based on household-level survey data collected from Qinghai Province, China, we estimated greenhouse gas (GHG) and air pollutant emissions from spatial perspectives, including household energy consumption in pastoral, agropastoral, and agricultural zones. The findings suggest that the total annual GHG and pollutant emissions per capita in the area was 2296.32 kg per year. The highest amount of pollutants was emitted from the pastoral zones, followed by the agropastoral and agricultural zones. CO 2 is the primary GHG emitted by household energy consumption. Dung burning was the cause of the high PM 2.5 emissions in the pastoral areas, while the use of coal was the primary cause of GHG and pollutant emissions in the agropastoral and agricultural zones. These findings highlight the need to integrate household energy policies with rural development to enable a complete transition towards cleaner fuels. [ABSTRACT FROM AUTHOR]

Accurate building energy prediction plays an important role in improving the energy efficiency of buildings. This paper proposes a homogeneous ensemble approach, i.e., use of Random Forest (RF), for hourly building energy prediction. The approach was adopted to predict the hourly electricity usage of two educational buildings in North Central Florida. The RF models trained with different parameter settings were compared to investigate the impact of parameter setting on the prediction performance of the model. The results indicated that RF was not very sensitive to the number of variables ( mtry ) and using empirical mtry is preferable because it saves time and is more accurate. RF was compared with regression tree (RT) and Support Vector Regression (SVR) to validate the superiority of RF in building energy prediction. The prediction performances of RF measured by performance index (PI) were 14–25% and 5–5.5% better than RT and SVR, respectively, indicating that RF was the best prediction model in the comparison. Moreover, an analysis based on the variable importance of RF was performed to identify the most influential features during different semesters. The results showed that the most influential features vary depending on the semester, indicating the existence of different operational conditions for the tested buildings. A further comparison between RF trained with yearly and monthly data indicated that the energy usage prediction for educational buildings could be improved by taking into consideration their energy behavior changes during different semesters. [ABSTRACT FROM AUTHOR]

Non-intrusive appliance load monitoring is a preferred technique to provide appliance-level energy usage information without deploying submeters and benefits smart building technologies such as energy management and home automation. Load signature construction, an indispensable design step for identifying residential appliances, is still an open challenge in non-intrusive appliance load monitoring. To improve the identification accuracy of the appliance loads with only on/off states, this paper constructs an admittance-based load signature called resolution-enhanced admittance (REA). With the voltage and current signal serving as the input and output respectively, the aggregate of on-off appliance loads is interpreted as the transfer function of a linear time-invariant system, namely the load admittance. Different from existing load signatures, the phase information of load admittances is accurately estimated by computing fundamental and harmonic frequencies in high resolution. To verify the feasibility and effectiveness of the proposed load signature, a laboratory testbed is built to generate datasets with high sampling rate for subsequent signature construction and load identification. The experimental results show that REA improves the accuracy of non-intrusive load monitoring and reflects more intrinsic properties of appliance loads compared to state-of-art load signatures. [ABSTRACT FROM AUTHOR]

In this paper, a model of homeowners’ energy efficiency retrofit choices is presented based on the database of a project implemented by the national energy efficiency retrofit scheme in Croatia during 2015 and 2016. The analysis was performed on the cleaned dataset of 4610 implemented projects in privately owned family houses. We have analyzed available data to answer how much money consumers are willing to pay for energy refurbishment and how this corresponds to household characteristics. We have developed linear regression models for estimating costs and energy savings of different measures and have further applied multinomial and nested discrete choice models on investment choices made by homeowners. The resulting models provide estimations of willingness to pay for energy efficiency refurbishment in private family houses in relation to income class the owners belong to. The results show that homeowners in all income classes have a similar level of willingness-to-pay for similar energy efficiency measures, with main differences being their capability to invest. With the current level of subsidies, only three of four observed measures show a significant level of free-riders. This suggests there could be a better allocation of public funds, where less money could be spent for subsidies to generate a similar amount of savings. Furthermore, the proposed model replicates existing experience and provides insight into inconsistencies which appear when the effectiveness of implemented measures is analyzed. [ABSTRACT FROM AUTHOR]

The forecasting of the thermal power demand is essential to support the development of advanced strategies for the management of local resources on the consumer side, such as heating ventilation and air conditioning (HVAC) equipment in buildings. In this paper, a novel hybrid methodology is presented for the short-term load forecasting of HVAC thermal power demand in smart buildings based on a data-driven approach. The methodology implements an estimation of the building's activity in order to improve the dynamics responsiveness and context awareness of the demand prediction system, thus improving its accuracy by taking into account the usage pattern of the building. A dedicated activity prediction model supported by a recurrent neural network is built considering this specific indicator, which is then integrated with a power demand model built with an adaptive neuro-fuzzy inference system. Since the power demand is not directly available, an estimation method is proposed, which permits the indirect monitoring of the aggregated power consumption of the terminal units. The presented methodology is validated experimentally in terms of accuracy and performance using real data from a research building, showing that the accuracy of the power prediction can be improved when using a specialized modeling structure to estimate the building's activity. [ABSTRACT FROM AUTHOR]

The energy consumption in a multi-chiller system is very significant and its consumption will be increased by inappropriate operating of the chillers. Accordingly, the economic dispatch in multi-chiller systems becomes an important issue for saving energy. Such issue is known as optimal chiller loading, which is solved by applying different optimization methods. This study employs exchange market algorithm to obtain optimal set points of the economic dispatch problem on chiller plants, which is inspired by trading the shares on a stock market. Exchange market algorithm is proposed for solving continuous non-linear optimization problems and it is comprised of two searching and two absorbing operators. The decision variable of the optimal chiller loading is partial load ratio of each chiller and the objective function of the optimal chiller loading problem is minimizing the power consumption while satisfying the cooling demand. This paper adopts three case studies to evaluate the performance of exchange market algorithm and compares it with other optimization methods applied on the optimal chiller loading problem. Results show that exchange market algorithm is an efficient method for solving optimal chiller loading problem in chiller plants, and is adaptable for usage in air-conditioning systems. In addition, the comparison results show that the exchange market algorithm provides better solutions than previous optimization methods applied on the optimal chiller loading method in terms of convergence speed and total consumption of electrical energy. [ABSTRACT FROM AUTHOR]

A building energy benchmark indicates the reasonable level of energy demand, and it facilitates the policy making when facing with uncertain information at the planning stage. Traditional benchmarks are established based on statistical indicators or physical simulations of prototype buildings, while the diversified consumption levels of various buildings are rarely considered carefully. Pointing to this, a data-driven benchmarking methodology is explored based on the Lorenz curve, which is a specific measurement tool of income inequality. In this paper, the Lorenz curve is used to identify the distribution inequality of energy consumption among buildings. In particular, the Lorenz curves of energy consumption were firstly derived from 195 buildings of three building types in Beijing. Afterwards, a variable was extracted from the fitting Lorenz curve, in order to quantify the nonlinear distribution principles of energy use intensity along with the accumulative building area. Finally, empirical benchmarking formulae for the three building types were achieved together with stepwise applications. The validation showed that relative errors of accumulated energy consumption for offices, hotels and shopping malls were −5.27%, 3.57% and −3.56% respectively. This data-driven approach is developed based on the limited information, and it attempts to assist energy-policy planners in establishing energy benchmarking targets and promoting energy efficiency. [ABSTRACT FROM AUTHOR]

The use of energy in the livestock sector will rise in the years to come due to the increasing demand for animal proteins from a word population that will reach 9.15 billion people before 2050. Livestock houses for intensive animal farming are characterized by high energy consumption due to climate control that is needed to guarantee animals’ welfare and to maximize their production. Currently, there are neither international standards calculation models nor commercial tools for the estimation of this energy consumption. To fill this gap, this paper presents a simulation model (based on a customization of the hourly model of ISO 13790) for the estimation of the energy consumption due to climate control of broiler houses. This model provides the energy consumption for heating, cooling and ventilation and the main indoor environmental parameters for the evaluation of the animals’ welfare. The model was validated comparing its outputs with a dataset obtained through a monitoring campaign carried out in a broiler house during a production cycle. This research has several practical application: it can increase the knowledge about energy consumption in the livestock sector and the model can be also a useful tool for agricultural engineers and farmers. [ABSTRACT FROM AUTHOR]

The use of Maisotsenko Cycle (M-Cycle) has enhanced the domain of evaporative cooling technologies to sub-wet bulb temperature cooling while ensuring moisture control. Several studies have demonstrated the use of cross-flow heat & mass exchanger (HMX) offers higher cooling capacity; however, it has lower cooling effectiveness and Energy Efficiency Ratio (EER). In contrast, a counter-flow (HMX) offers high cooling effectiveness with lower cooling capacity. In this paper, the performance of counter-flow HMX is enhanced by addition of alumina nanoparticles in feed water due to enhanced heat and mass transfer characteristics of nanofluids compared to original base fluid. Here, a mathematical model is formulated by incorporating the nanofluids in a selected control volume. The developed model is solved numerically on a discretized HMX length. Initially, the model is benchmarked against previously published results using water as base fluid. A comparison between HMX performance using water and alumina nanofluid is performed in terms of Performance Enhancement Ratio (PER). PER indicates 1–18% increase in cooling effectiveness, 18–43% increase in cooling capacity and 9–19% increase in EER by using alumina in water when working air temperature is increased from 20   °C to 45   °C. Similarly, an increase in PER is also observed by changing air velocity. Increase of 41% is observed in cooling capacity and 18% increase in EER is observed by changing particle volume fraction from 0 to 2%. This research identifies ways to reduce the carbon emissions of a building by increasing the energy efficiency of existing evaporative cooling technology using nanofluids. [ABSTRACT FROM AUTHOR]

The focus of carbon management is shifting from operational carbon to embodied carbon as a result of the improved operational energy efficiency of buildings. Measuring and managing embodied carbon right from the early stages of projects will unlock a range of opportunities to achieve the highest possible emissions reduction which could not be achieved otherwise during the latter stages. However, measuring embodied carbon during the early stages of design is challenging and highly uncertain due to the unavailability of detailed design information. Therefore, the research presented in this paper addresses this problem in a structured and an objective way. A parametric embodied carbon prediction model was developed using regression analysis to estimate embodied carbon when only minimal design information is available and with less uncertainty. The model was developed by collecting historical data of office buildings in the UK from four different data sources and estimating embodied carbon by combining several estimating techniques. Wall to floor ratio and the number of basements were identified as the model predictors with a model fit of 48.1% (R 2 ). A five-fold cross-validation ensured that the model predicts within the acceptable accuracy range for new data. The developed model had an accuracy of ±89.35% which is within the acceptable accuracy range for an early stage prediction model. In addition, the need for standardising embodied carbon measurements and to develop embodied carbon benchmarks to facilitate embodied carbon estimating throughout the project lifecycle were emphasised. [ABSTRACT FROM AUTHOR]

Greenhouse gas emissions have been recognized as one of the major cause of the global warming phenomena. The built environment accounts for more than 40% of the overall energy consumption and 36% of the overall CO2 emissions in Europe. Recent studies show that housing is one of the most responsible sector for world ecological impacts. The European Parliament developed the concept of Nearly Zero Energy Buildings (NZEB), characterized by a very low energy demand and a high renewable energy on-site production. In fact, energy efficiency is the first step towards the ambitious aim to reduce of 80% by 2050 the EU carbon emissions. The zero-energy building target is an achievable goal, which relies on a careful design that encompasses a synergy between passive and low-energy strategies. However, considering the whole life cycle of buildings, NZEBs reduce the operational energy close to zero, increasing the relevancy of the embodied energy, which occurs during the construction phase. Balancing the values of the operational and embodied energy is necessary to minimize buildings footprint on the environment. In this paper the renovation and re-use of the Atika building, a demonstrative energy-efficient building, is presented as case study of an environmental efficient methodology for energy retrofitting. The case relies on the methodology developed by Active House, a holistic vision for sustainable buildings labeling. [ABSTRACT FROM AUTHOR]

The use of heat pumps in district heating is one of the most promising technologies for enhancing the efficiency of systems and for meeting the 2030 and 2050 European energy and climate targets. It is essential to assess the available data, the practice and experience of planners and engineers to determine heat pump placement, connection and operational modes in district heating networks, as the systems ought to be capable of covering residential heat requirements for all year around operation. The main selection criteria for the appropriate heat pump system are the heat source, the heat pump technology and the heat requirements. These parameters form a technical triangle, which should be used as a comprehensive instrument to enable heat pump integration into residential district heating. The triangle allows the definition of the bidirectional interdependences and the design of a high efficiency, sustainable heat pump based system. This paper presents the solution to scenarios of how to integrate heat pumps into district heating, in terms of heat pump placement, connection and operational modes based on available heat sources and heat requirement profiles and their environmental impact. [ABSTRACT FROM AUTHOR]

Some highly energy efficient window attachment products are available today, but more rapid market adoption would be facilitated by fair performance metrics. It is important to have validated simulation tools to provide a basis for this analysis. This paper outlines a review and validation of the ISO 15099 center-of-glass zero-solar-load heat transfer correlations for windows with cellular shades. Thermal transmittance was measured experimentally, simulated using computational fluid dynamics (CFD) analysis, and simulated utilizing correlations from ISO 15099 as implemented in Berkeley Lab WINDOW and THERM software. CFD analysis showed ISO 15099 underestimates heat flux of rectangular cavities by up to 60% when aspect ratio (AR) = 1 and overestimates heat flux up to 20% when AR = 0.5. CFD analysis also showed that wave-type surfaces of cellular shades have less than 2% impact on heat flux through the cavities and less than 5% for natural convection of room-side surface. WINDOW was shown to accurately represent heat flux of the measured configurations to a mean relative error of 0.5% and standard deviation of 3.8%. Several shade parameters showed significant influence on correlation accuracy, including distance between shade and glass, inconsistency in cell stretch, size of perimeter gaps, and the mounting hardware. [ABSTRACT FROM AUTHOR]

Heat recovery devices can help ventilation systems conserve energy; However, they cause a pressure drop in the system, and hence the system incurs an electricity consumption. In the present paper, the maximum allowable pressure drop of the recovery units is achieved from thermodynamic and economic points of view. The novelty of the research is that, unlike the existing criterion which does not differ for different cities, the introduced criteria can be calculated for each city with individual weather, economic, power plants, and energy conditions. Although the methods presented to calculate the criteria consider a broad range of effective parameters, there is no need to use complex energy simulation methods. Moreover, for each city, four types of criteria are introduced, which allows the city authorities to choose the best ones according to their city conditions, and then calculate the chosen criteria by the data of the city. In order to gain the criteria, modified enthalpy-hours and bin method variables are introduced to calculate sensible and latent energy consumption due to ventilation. The criteria are numerically calculated for Kerman city in Iran, and the results show that the recovery type and the chosen viewpoint has a considerable effect on the allowable pressure drop. The sensitivity of the results to different variables is analyzed in the end. [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 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]

In this paper, a free running centrifugal fan inside a rectangular duct is investigated via measurements and CFD. A rectangular cuboid-shaped body is mounted downstream of the free running centrifugal fan, covering most of the space next to the fan wheel, leaving only a small channel for the air near the duct walls to pass through. We call this cube a ‘pressure regain unit’ (PRU). The cube increases the fan efficiency by reducing vortices downstream of the fan and tranfering a larger part of the kinetic energy into static pressure. Experiments are conducted with several PRU geometry variations. The measurements show that an increase in efficiency of 10% is possible compared to a free running fan in an empty duct. Subsequently, a numerical analysis is performed to analyze the effect which leads to efficiency increase. Comparing the installation with and without PRU shows that the main difference is a recirculation area behind the fan wheel when no PRU is installed. Higher level of turbulence and strong shear layers in the empty duct cause efficiency losses. The turbulence level and the amount of shear layers are reduced by the PRU. Both experimental and numerical data show an energy saving potential by optimizing the downstream region of the free running centrifugal fan. [ABSTRACT FROM AUTHOR]

With the world rapidly urbanizing, addressing the energy intensive urban built environment is becoming increasingly important. Cities across the United States and the world are turning to energy benchmarking as a means of understanding the relative energy efficiency of their building stock and identifying potential opportunities to reduce energy usage. Benchmarking utilizes building characteristics and energy use data to measure a building’s energy consumption against a performance baseline and derive a level of energy efficiency. Over twenty cities in the United States and many others across the world have passed laws mandating the collection and disclosure of energy use data to enable benchmarking and pinpoint potential energy saving opportunities. However, municipalities are struggling to convert this data into actionable insights and identify which buildings are prime candidates for energy efficiency interventions. Although an extensive body of work exists on benchmarking building energy performance, previous works are limited in their ability to leverage such emerging data streams and conduct analysis at the city scale. Moreover, previous methods are largely based on black-box models that limit the interpretability of results and in turn hinder the ability of policy-makers to employ such models in their policy design and decision-making processes. In this paper, we propose DUE-B, a new Data-driven Urban Energy Benchmarking methodology based on recursive partitioning and stochastic frontier analysis. To test DUE-B, we evaluate its performance using real energy and building data from over 10,000 buildings in New York City, and we compare the results to other common benchmarking models using the Kendall tau-b correlation coefficient. Results indicate that DUE-B is more robust than conventional benchmarking methods in respect to identifying subsets of efficient and inefficient buildings. Furthermore, we highlight how results from DUE-B can be utilized by municipal officials and other policy-makers to target inefficient buildings for energy efficiency interventions, incentives, and programs. Specifically, we indicate how DUE-B can be utilized by municipalities to target the most inefficient buildings for subsidized onsite energy audits and less inefficient buildings for less capital-intensive energy efficiency strategies such as incentives and educational programs. In the end, more robust benchmarking methods like DUE-B have the potential to enhance the efficacy of municipal energy efficiency programs and help transition cities to a more sustainable energy future. [ABSTRACT FROM AUTHOR]