Search

The selection of a thermal comfort model for establishing indoor optimal hygrothermal conditions during the hot period has a major impact on energy consumption of Net Zero Energy Buildings in hot climates. The objective of this paper is to compare the influence of using different thermal comfort models for zero energy buildings in hot climates. The paper compares the impact of applying Fanger's model, Givoni's model, the ASHRAE 55 adaptive comfort model and the EN 15251 adaptive comfort model on energy consumption and comfort performance. Using both the building performance simulation tools ZEBO and EnergyPlus for energy simulation, an existing prototype of a residential apartment module is used to evaluate energy performance and thermal comfort in two parametric series. The first one is the result of coupling natural ventilation and mechanical cooling and the second one is guided coupling natural ventilation, mechanical cooling and ceiling fans. This study shows that the percentage of energy consumption difference meeting the comfort criteria according to ISO 7730 in comparison to EN 15251, ASHRAE 55 or Givoni's model varied up to 16%, 21% and 24.7%, respectively for the presented case study. More energy savings can be expected for buildings in hot climates with greater cooling demands. [ABSTRACT FROM AUTHOR]

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

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]

Polygeneration is an integrated process that produces two or more energy products from one or more primary energy sources (PES). Polygeneration systems have wide applications in utilities, industrial sectors and building sectors. However, there are still very limited applications of the powerful and practical optimization techniques in polygeneration systems of building sectors comparing to their wide applications in industrial sectors. System optimization is one of effective and reasonable tools to exploit the potential of polygeneration systems. Polygeneration offers potential to fulfill the ambitious target of zero energy building (ZEB) because the interdependence of different energy products can be utilized to provide advantage and flexibility to accommodate more renewable energy sources (RES) in the system. Polygeneration based ZEB can be treated as a type of Polygeneration Microgrid (PM) because PM represents a promising solution to supply electricity, heat and cooling efficiently using local energy sources, including RES. The development of PM is closely associated with building applications. Hence, the objective of the present paper is to give a comprehensive review for optimization techniques of polygeneration systems in buildings. The review was conducted based on integrated design, synthesis and operation optimization (IDSOO) for different scales of systems, ranging from the micro-scale for a single house to the large-scale for a residential complex, including PM to identify difference between traditional systems and PM in terms of optimization. The literature was classified according to solution techniques of IDSOO and characteristics of IDSOO in terms of decision variables, objective functions and constraints. The results showed that there is no significant difference between traditional systems and PM in terms of solution techniques while there is subtle difference between traditional systems and PM in terms of objective function and constraints. Optimization of PM has higher requirements for real-time management to integrate more RES. Optimization of micro- and small-scale systems are more complicated than large-scale ones because there are many constraints from technical, legal and policy viewpoints. [ABSTRACT FROM AUTHOR]

As of 2007 more than half of the world’s population is living in urban areas (a figure expected to rise to 60% by 2030). Thus, the liveability of the high-density city is gradually becoming a central point of focus and concern. A successful skyscraper model of urban planning could provide the possibility to increase city-space vertically as opposed to the current continuous expansion outward, which has obvious environmental consequences. However, skyscraper development, as well as all other new construction and gradually the older building stock, has to comply with current strict regulations on building energy efficiency. Contemporary high-rise examples do not present a sustainable solution to an increasing population or as models of prosperity, as they are linked to high-energy demand, environmental and social imbalances. This paper looks at design strategies towards promoting skyscraper energy efficiency by considering a climatically responsive design, where orientation, the thermal properties of the building envelope and the effect of altitude, become the main design tools. Initial simulations were performed for a residential and an office reference structure 100 m high. Different scenarios were implemented for gradually upgrading the building envelope and studying its relationship with the changing microclimate with altitude (wind speed increase and dry bulb temperature drop) between ground and top level. The advanced envelope was then simulated to up to 400 m high (120 stories high), and heating and cooling loads were compared in relation to different building heights and uses. EnergyPlus is used as the main simulation tool as it accounts for wind speed increase and dry bulb temperature drop with height. The location chosen is Tel Aviv, Israel, a city already growing upwards and expected to have a significant increase in skyscraper construction in the coming years. The results of the simulations performed present the base upon which further design strategies can be implemented towards reducing the environmental impact of this challenging building type. [ABSTRACT FROM AUTHOR]

In recent years, technological advances have substantially extended the capabilities of automation systems in buildings. Despite the achieved advances, automation systems have not been widely adopted by building occupants. This paper presents our investigations on automation preferences of occupants for the control of lighting systems and appliances in residential buildings. A survey was carried out to determine how preferences for level of automation vary by contexts as well as individuals’ personalities and demographic characteristics. The contexts investigated in this study include rescheduling an energy consuming activity, activity-based appliance state control, and lighting control. The collected data from 250 respondents were analyzed using Generalized Linear Mixed Models. Our findings demonstrate that in all context no automation is the least preferred option. For rescheduling an energy consuming activity, an automation level with higher user participation is more preferred. For activity-based appliance state control and lighting control, levels of automation with lower user participation are more preferred. Our findings also indicate that income and education levels and also personality traits of agreeableness, neuroticism and openness to experience affect the preference of particular automation levels over the others. Findings from this study can be used in designing user-centered automation systems that lead to potentially more satisfying operation and hence, could enhance automation acceptability. [ABSTRACT FROM AUTHOR]

One of the major challenge facing the achievement of nZE standards in existing buildings is the economic issue: the evidence of monetary gains of energy savings facing high investment costs seems still rather limited to the investors’ eyes. In this context, LCC methods have gained much importance in recent years. However, they present a limitation due to the notable simplifications and hypothesis usually made for input parameters that may affect the results. In order to overcome this limit, this work suggests a probabilistic LCC based on uncertainty and sensitivity analysis via Monte Carlo methods and illustrates it through a building case study under several retrofit scenarios sighting the target nZE. The methodology allows investigating the economic effectiveness of alternative measures, giving insight into possible ranges of the economic indicator related to a specific design option. The analysis is focused on a micro-economic dimension and based on the availability and reliability of inputs data and on their proper characterization with Probability Density Functions. Variance-based methods for sensitivity analysis are employed to establish the most influential parameters on output uncertainty. The paper demonstrates the potentials of a probabilistic LCC in providing a more realistic decision support about investments for energy efficient projects. [ABSTRACT FROM AUTHOR]

A segmented dynamic dwelling stock model is proven useful for understanding the development and changes of ageing building stocks, which is highly relevant for renovation measures and estimates of energy use and emissions in aggregated building stocks. In this paper, such a model is developed further for detailed analyses of dwelling stock energy demand and exemplified for the Norwegian dwelling stock 1960–2015. The dwelling stock model simulates the development in stock size and composition and is combined with archetype-specific energy intensities to estimate the total energy demand. After calibrating the model results with statistics, the model is used to explore the phenomena and causes of historical changes. A large-scale improvement of the energy efficiency of the Norwegian dwelling stock has taken place through renovation and construction of new dwellings. A historical shift to more efficient energy carriers and heating systems has had an effect on energy savings in the system, of the same size as the effect of the improved energy efficiency of the stock. However, the total average energy savings per m 2 are offset by changes in user heating habits. A significant decrease in average delivered energy intensity per m 2 is only observed after the introduction of heat pumps. [ABSTRACT FROM AUTHOR]

The Spanish residential building stock built between 1940 and 1980 period presents a high energy saving potential as it represents a significant percentage of the total housing stock and it was built without any regulation regarding energy efficiency. Accordingly, the first aim of this paper was to quantify the energy saving potential and the related reduction in CO 2 emissions of the housing stock in those years. The work focuses on the Mediterranean climate of the Comunitat Valenciana Region, therefore the energy efficiency strategies proposed are passive and deal with the envelope of buildings. On the basis of the results obtained, several scenarios with intervention periods of 10, 20 and 30 years were calculated. The conclusion was that with the current rate of annual building renovation, it will be nearly impossible to reach the goals established by the EPBD. Only a deep transformation of the building fabric within this sector could help in achieving them. Finally, it should be noted that the proposed methodology can be applied to other areas with different climates, particularly in Spain. [ABSTRACT FROM AUTHOR]

Non-residential buildings like healthcare ones contribute to energy consumption and cause a negative environmental impact. This is mainly due to the age of the buildings, their poor level of energy efficiency and the implementation of only a basic maintenance plan. Owing to the very limited budget available for public administrators, Energy Performance Contracting (EPC), that entails the involvement of an Energy Service Company (ESCo), can provide the entire or part of the capital needed for investments aimed at progressively increasing energy efficiency over their service life. In this paper, three acute hospitals and two community clinics built in Italy are analysed, in order to assess the economic feasibility of several energy renovation actions that can be included within EPC contracts. To this purpose, the outcomes of energy audits carried out in 2014 about these buildings are reported, which involved analyses of consumption measured over the previous three years and assessment of use profiles for the development of models to break down the overall consumption and to estimate potential savings. Diverse improvement strategies were assessed, consisting in either isolated or combined renovation actions and tested on the five buildings, whose comparison was carried out in terms of energy saving, pollution reduction and economic feasibility. As a result, the best combined renovation actions per each hospital were recommended, that included – but were not limited to – the improvement of the control and regulation of existing sub-systems without replacement, partial replacement of other sub-systems and integration of renewables. Finally, each scenario was evaluated through the payback period and other economic performance indexes in order to assess the feasibility of the EPC framework when applied to this case study. [ABSTRACT FROM AUTHOR]

Evaluation methods can be used to determine what constitutes good energy performance in a building. With an increasing focus on energy use of buildings worldwide, this evaluation assumes a fundamental importance. This paper provides a comprehensive review of the available methods for analysing, classifying, benchmarking, rating and evaluating energy performance in non-domestic buildings. Methodologies are grouped in five categories: engineering calculations, simulation, statistical methods, machine learning and other methods. Techniques for evaluating buildings are described, their principal applications are shown and limitations are identified. The use of performance evaluation in energy efficiency programmes and standards is mapped. There is a need to further develop interactions between the main modelling techniques to produce simple, robust and validated models. Also, evaluation techniques must be developed to consider comfort or service provision in the buildings as a factor in energy performance. [ABSTRACT FROM AUTHOR]

The total energy demand of data centres has experienced an important increase in the last years. This is why data centres industry and researchers are working on implementing energy efficiency measures in data centre portfolio. The cooling system of these unique infrastructures can account for 40% of the total energy consumption. Therefore, air management plays an important role to reduce operating costs and to increase overall cooling system efficiency. In order to improve and optimize the energy consumption and thermal management in data centres, appropriate metrics are imperative to evaluate their efficiency and performance. The present paper examines the air management of a real data centre by the use of the most promising air metrics. Noticeable air recirculation and hot spots were observed in the facility. Dynamic modelling was used to evaluate the benefit of air inefficiency mitigation. The combination of air management improvement with increasing the supply air temperature and reducing the air flow rate would allow important energy reduction in the cooling system. [ABSTRACT FROM AUTHOR]

Model predictive control (MPC) is an advanced control that can be used for dynamic optimization of HVAC equipment. Although the benefits of this technology have been shown in numerous research papers, currently there is no commercially or publicly available software that allows the analysis of building systems that employ MPC. The lack of detailed and robust tools is preventing more accurate analysis of this technology and the identification of factors that influence its energy saving potential. The modeling environment (ME) presented here is a simulation tool for buildings that employ MPC. It enables a systematic study of primary factors influencing dynamic controls and the savings potential for a given building. The ME is highly modular to enable easy future expansion, and sufficiently fast and robust for implementation in a real building. It uses two commercially available computer programs, with no need for source code modifications or complex connections between programs. A simplified building model is used during the optimization, whereas a more complex building model is used after the optimization. It is shown that a simplified building model can adequately replace a more complex model, resulting in significantly shorter computational times for optimization than those found in the literature. [ABSTRACT FROM AUTHOR]

Obtained as a research result of conducted project, this paper presents an innovative, energy efficient multipurpose system for a sustainable globally cost-effective building's solar energy use and developed methodology for its dynamic analysis and optimization. The initial research and development goal was to create a cost-effective technical solution for replacing fossil fuel and electricity with solar energy for water heating for different purposes (for pools, sanitary water, washing) in one SPA. After successful realization of the initial goal, the study was proceeded and as a result, the created advanced system has been enriched with AC performance. The study success was based on understanding and combined measurements and by BPS made predictions of AC loads and solar radiation dynamics as well as on the determination of the synergetic relations between all relevant quantities. Further, by the performed BPS dynamic simulations for geographically spread buildings locations, it has been shown that the final result of the conducted scientific engineering R&D work has been the created system of confirmed prestigious to the sustainability relevant performance – globally cost-effective building integrated photovoltaic powered heat pump (HP), assisted by waste water heat recovery, for solar AC, water heating and saving. [ABSTRACT FROM AUTHOR]

This paper provides a comparative study of state-of-the-art means of predicting occupancy for smart heating control applications. We focus on approaches that predict the occupancy state of a home using occupancy schedules – that is, past records of the occupancy state. We ran our analysis on actual occupancy schedules covering several months for 45 homes. Our results show that state-of-the-art, schedule-based occupancy prediction algorithms achieve an overall prediction accuracy of over 80%. We also show that the performance of these algorithms is close to the theoretical upper bound expressed by the predictability of the input schedules. Building upon these results, we used ISO 13790-standard modelling techniques to analyse the energy savings that can be achieved by smart heating controllers that use occupancy predictors. Furthermore, we investigated the trade-off between achievable savings (typically 6–17% on average) and the risk of comfort loss for household residents. [ABSTRACT FROM AUTHOR]

The global trend toward a low carbon, sustainable economy is widely acknowledged to create a large number of jobs across many sectors of the economy. The deployment of renewable and energy efficiency projects represent a large share of the so called green jobs and help retain current levels of employment especially in countries facing the socio-economic threat of increasing unemployment rates. These effects should be taken into account during the process of energy planning, but due to difficulties in their quantification this is not always feasible. This paper utilizes the input–output analysis and the adjusted earnings gain approach for assessing the employment effects associated with the implementation of energy saving interventions in the building sector, with a view to produce results that can be easily incorporated into cost–benefit analyses. The proposed framework, which comprises four basic steps, namely identification of all the potential changes in employment due to energy efficiency interventions, quantification of these employment effects in physical terms, calculation of the net present value of the estimated employment effects and monetization, has been implemented for three energy efficiency interventions widely implemented in Greek buildings. The results of the analysis clearly show that the exploitation of energy saving technologies in the Greek building stock generates significant employment benefits, ranging between €0.11 and 0.23 million per €1 million of investment in the base case scenario and reaching the 10–24% of the energy costs savings attributed to the implementation of these interventions throughout their entire lifetime. [ABSTRACT FROM AUTHOR]

Abstract: This paper describes a small-scale experimental direct-expansion ground source heat pump system in typical clay soils for space heating of a building in Jinzhou, China. It uses R22 as the refrigerant, consisting of four single U-shaped copper ground heat exchangers with a uniform depth of 20m. A special circular bend is designed on the copper tube to enhance the lubricant oil return of the scroll compressor. The operation performance and energy efficiency of the DX-GSHP system is tested in the heating mode. Results show that the present DX-GSHP system is reliable and effective. During the heating period, when the heating capacity of the indoor fan coil is 6.41kW, the room temperature varies from 18 to 20°C even if the door is opened or closed frequently. The average COP of the heat pump unit and the whole DX-GSHP system are 3.12 and 2.88, respectively, and the average heat-transfer rate of GHEs per depth is 54.4W/m. In addition, the influence of the opening of the electronic expansion valve on the operation performance is analyzed. The present results can provide useful guides for the design and further improvement of DX-GSHP systems under similar geological conditions. [Copyright &y& Elsevier]

Abstract: Energy efficiency in buildings is essential for economic, social, and environmental reasons. Under current European legislation, buildings have to be certified with an energy efficiency index. To provide a fair comparison of the energy performance in buildings we need a global and unified representation. In this paper we propose the Energy Efficiency Ontology (EEOnt) to provide a foundation upon which this requirement can be built. EEOnt extends the applicability of other ontological models of buildings incorporating energy efficiency data and knowledge that can be calculated with high spatial and temporal resolution in what we call an Energy Efficiency Landscape (EELB). These EELB''s provide significant information which allows a detailed diagnosis of the energy efficiency within the building. Consequently, EEOnt is capable of representing buildings and energy efficiency in a unified way. Furthermore, it supplies a tool for managing the energy efficiency in buildings and constructing national inventories. These two features link the global and particular needs of governments and users, thus becoming key to both improve the energy efficiency in buildings and to implement viable certification schemes. [Copyright &y& Elsevier]

Abstract: This paper presents the Energy, Carbon and Cost Assessment for Building Stocks (ECCABS) model, which is a bottom-up model to assess energy-saving measures (ESM) and carbon dioxide (CO2) mitigation strategies in building stocks. The model is based on a one-zone hourly heat balance that calculates the net energy demand for a number of buildings representative of the building stock and an additional code for the input and output data. The model generates results in terms of net and delivered energy, associated CO2 emissions, and the costs of implementing different ESM. The results are extended to the entire building stock by means of weighting factors. Empirical and comparative validations of the heat-balance modelling of single buildings are presented. The building stock modelling is validated against the current Swedish residential stock, for which the results of the modelling are in agreement with the statistical data. Using the model to assess a number of ESM reveals that the energy usage of the Swedish residential sector can be reduced by 55% and the associated CO2 emissions can be reduced by 63%, with most of the ESM being cost–effective. The applicability of the model to countries other than Sweden is under investigation. [Copyright &y& Elsevier]

Abstract: Retrofitting of existing buildings offers significant opportunities for reducing global energy consumption and greenhouse gas emissions. This is being considered as one of main approaches to achieving sustainability in the built environment at relatively low cost and high uptake rates. Although there are a wide range of retrofit technologies readily available, methods to identify the most cost-effective retrofit measures for particular projects is still a major technical challenge. This paper provides a systematic approach to proper selection and identification of the best retrofit options for existing buildings. The generic building retrofit problem and key issues that are involved in building retrofit investment decisions are presented. Major retrofit activities are also briefly discussed, such as energy auditing, building performance assessment, quantification of energy benefits, economic analysis, risk assessment, and measurement and verification (M&V) of energy savings, all of which are essential to the success of a building retrofit project. An overview of the research and development as well as application of the retrofit technologies in existing buildings is also provided. The aim of this work is to provide building researchers and practitioners with a better understanding of how to effectively conduct a building retrofit to promote energy conservation and sustainability. [Copyright &y& Elsevier]

• New methodology to characterise the heating demand of building stocks at city level. • The approach is applied to Bilbao city, by constructing 17 archetype buildings. • Evidence-based calibration and validation procedures increase models' reliability. • Aggregated model results only differ around 4% from real historical data. • The methodology is verified completely feasible with existing public data sources. In the current transition towards a net-zero GHG emissions economy, residential building sector is one of the sectors with highest potential for energy efficiency. In order to assist the implementation of energy saving policies, in which the role of local authorities is crucial, the bottom-up engineering approach is the most suitable method for a reliable characterisation of the energy consumption in an existing building stock. In the present paper a methodology is proposed to characterise the space heating energy consumption of a residential stock at city level, based on archetype buildings. The methodology is focused on addressing the challenges which often such approach involves. An evidence-based calibration procedure is proposed to address occupant behavioural patterns. Attention is also given to the constructed models results validation process, by comparing them with collected real data. The methodology is applied to the residential building stock of the city of Bilbao, where seventeen typologies of archetype buildings are constructed. The results demonstrate the practicability of accurately reproducing the existing space heating energy consumption of a city-scale residential building stock with available public data sources. The methodology has been conceived to be easily replicable to any city. [ABSTRACT FROM AUTHOR]