Research

8054 KiB

Open AccessArticle

Energy Performance Assessment of a 2nd-Generation Vacuum Double Glazing Depending on Vacuum Layer Position and Building Type in South Korea

by Seung-Chul Kim, Jong-Ho Yoon and Ru-Da Lee

Energies 2017, 10(8), 1240; https://doi.org/10.3390/en10081240 - 21 Aug 2017

Cited by 5 | Viewed by 3841

Abstract

(1) Background: The application of high insulation to a building envelope helps reduce the heating load, but increases the cooling load. Evaluating the installation of high insulation glazing to buildings in climate zones with four distinct seasons, as in the case of South [...] Read more.

(1) Background: The application of high insulation to a building envelope helps reduce the heating load, but increases the cooling load. Evaluating the installation of high insulation glazing to buildings in climate zones with four distinct seasons, as in the case of South Korea, is very important; (2) Methods: This study compared the heating energy performance of four types of glazing, inside vacuum double glazing, outside vacuum double glazing, single vacuum glazing, and low-e double glazing, with fixed low-e coating positions on the inside of the room in a mock-up chamber under the same conditions. The annual energy consumption according to the building type was analyzed using a simulation; (3) Results: As the insulation performance of building envelopes has increased, the energy saving rate of inside vacuum double glazing has been increased further in office buildings. In residential buildings, the energy saving rate of inside vacuum double glazing with a low SHGC (solar heat gain coefficient) has become higher than that of outside vacuum double glazing; (4) Conclusions: Since the effects of SHGC on the energy saving rates are greater in high insulation buildings, SHGC should be considered carefully when selecting glazing in climate zones with distinct winter and summer seasons. Full article

(This article belongs to the Special Issue Zero-Carbon Buildings)

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2336 KiB

Open AccessArticle

Optimizing Energy Efficiency in Operating Built Environment Assets through Building Information Modeling: A Case Study

by Ioan Petri, Sylvain Kubicki, Yacine Rezgui, Annie Guerriero and Haijiang Li

Energies 2017, 10(8), 1167; https://doi.org/10.3390/en10081167 - 08 Aug 2017

Cited by 71 | Viewed by 12525

Abstract

Reducing carbon emissions and addressing environmental policies in the construction domain has been intensively explored with solutions ranging from energy efficiency techniques with building informatics to user behavior modelling and monitoring. Such strategies have managed to improve current practices in managing buildings, however [...] Read more.

Reducing carbon emissions and addressing environmental policies in the construction domain has been intensively explored with solutions ranging from energy efficiency techniques with building informatics to user behavior modelling and monitoring. Such strategies have managed to improve current practices in managing buildings, however decarbonizing the built environment and reducing the energy performance gap remains a complex undertaking that requires more comprehensive and sustainable solutions. In this context, building information modelling (BIM), can help the sustainability agenda as the digitalization of product and process information provides a unique opportunity to optimize energy-efficiency-related decisions across the entire lifecycle and supply chain. BIM is foreseen as a means to waste and emissions reduction, performance gap minimization, in-use energy enhancements, and total lifecycle assessment. It also targets the whole supply chain related to design, construction, as well as management and use of facilities, at the different qualifications levels (including blue-collar workers). In this paper, we present how building information modelling can be utilized to address energy efficiency in buildings in the operation phase, greatly contributing to achieving carbon emissions targets. In this paper, we provide two main contributions: (i) we present a BIM-oriented methodology for supporting building energy optimization, based on which we identify few training directions with regards to BIM, and (ii) we provide an application use case as identified in the European research project “Sporte2” to demonstrate the advantages of BIM in energy efficiency with respect to several energy metrics. Full article

(This article belongs to the Special Issue Zero-Carbon Buildings)

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6620 KiB

Open AccessArticle

A Smart Forecasting Approach to District Energy Management

by Baris Yuce, Monjur Mourshed and Yacine Rezgui

Energies 2017, 10(8), 1073; https://doi.org/10.3390/en10081073 - 25 Jul 2017

Cited by 23 | Viewed by 5218

Abstract

This study presents a model for district-level electricity demand forecasting using a set of Artificial Neural Networks (ANNs) (parallel ANNs) based on current energy loads and social parameters such as occupancy. A comprehensive sensitivity analysis is conducted to select the inputs of the [...] Read more.

This study presents a model for district-level electricity demand forecasting using a set of Artificial Neural Networks (ANNs) (parallel ANNs) based on current energy loads and social parameters such as occupancy. A comprehensive sensitivity analysis is conducted to select the inputs of the ANN by considering external weather conditions, occupancy type, main income providers’ employment status and related variables for the fuel poverty index. Moreover, a detailed parameter tuning is conducted using various configurations for each individual ANN. The study also demonstrates the strength of the parallel ANN models in different seasons of the years. In the proposed district level energy forecasting model, the training and testing stages of parallel ANNs utilise dataset of a group of six buildings. The aim of each individual ANN is to predict electricity consumption and the aggregated demand in sub-hourly time-steps. The inputs of each ANN are determined using Principal Component Analysis (PCA) and Multiple Regression Analysis (MRA) methods. The accuracy and consistency of ANN predictions are evaluated using Pearson coefficient and average percentage error, and against four seasons: winter, spring, summer, and autumn. The lowest prediction error for the aggregated demand is about 4.51% for winter season and the largest prediction error is found as 8.82% for spring season. The results demonstrate that peak demand can be predicted successfully, and utilised to forecast and provide demand-side flexibility to the aggregators for effective management of district energy systems. Full article

(This article belongs to the Special Issue Zero-Carbon Buildings)

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2344 KiB

Open AccessArticle

Multi-Objective Optimization of Building Energy Design to Reconcile Collective and Private Perspectives: CO₂-eq vs. Discounted Payback Time

by Mohamed Hamdy and Gerardo Maria Mauro

Energies 2017, 10(7), 1016; https://doi.org/10.3390/en10071016 - 18 Jul 2017

Cited by 22 | Viewed by 4324

Abstract

Building energy design is a multi-objective optimization problem where collective and private perspectives conflict each other. For instance, whereas the collectivity pursues the minimization of environmental impact, the private pursues the maximization of financial viability. Solving such trade-off design problems usually involves a [...] Read more.

Building energy design is a multi-objective optimization problem where collective and private perspectives conflict each other. For instance, whereas the collectivity pursues the minimization of environmental impact, the private pursues the maximization of financial viability. Solving such trade-off design problems usually involves a big computational cost for exploring a huge solution domain including a large number of design options. To reduce that computational cost, a bi-objective simulation-based optimization algorithm, developed in a previous study, is applied in the present investigation. The algorithm is implemented for minimizing the CO₂-eq emissions and the discounted payback time (DPB) of a single-family house in cold climate, where 13,456 design solutions including building envelope and heating system options are explored and compared to a predefined reference case. The whole building life is considered by assuming a calculation period of 30 years. The results show that the type of heating system significantly affects energy performance; notably, the ground source heat pump leads to the highest reduction in CO₂-eq emissions, around 1300 kgCO₂-eq/m², with 17 year DPB; the oil fire boiler can provide the lowest DPB, equal to 8.5 years, with 850 kgCO₂-eq/m² reduction. In addition, it is shown that using too high levels of thermal insulation is not an effective solution as it causes unacceptable levels of summertime overheating. Finally a multi-objective decision making approach is proposed in order to enable the stakeholders to choice among the optimal solutions according to the weight given to each objective, and thus to each perspective. Full article

(This article belongs to the Special Issue Zero-Carbon Buildings)

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1015 KiB

Open AccessArticle

An Assisted Workflow for the Early Design of Nearly Zero Emission Healthcare Buildings

by Hassan A. Sleiman, Steffen Hempel, Roberto Traversari and Sander Bruinenberg

Energies 2017, 10(7), 993; https://doi.org/10.3390/en10070993 - 13 Jul 2017

Cited by 12 | Viewed by 5661

Abstract

Energy efficiency in buildings is one of the main goals of many governmental policies due to their high impact on the carbon dioxide emissions in Europe. One of these targets is to reduce the energy consumption in healthcare buildings, which are known to [...] Read more.

Energy efficiency in buildings is one of the main goals of many governmental policies due to their high impact on the carbon dioxide emissions in Europe. One of these targets is to reduce the energy consumption in healthcare buildings, which are known to be among the most energy-demanding building types. Although design decisions made at early design phases have a significant impact on the energy performance of the realized buildings, only a small portion of possible early designs is analyzed, which does not ensure an optimal building design. We propose an automated early design support workflow, accompanied by a set of tools, for achieving nearly zero emission healthcare buildings. It is intended to be used by decision makers during the early design phase. It starts with the user-defined brief and the design rules, which are the input for the Early Design Configurator (EDC). The EDC generates multiple design alternatives following an evolutionary algorithm while trying to satisfy user requirements and geometric constraints. The generated alternatives are then validated by means of an Early Design Validator (EDV), and then, early energy and cost assessments are made using two early assessment tools. A user-friendly dashboard is used to guide the user and to illustrate the workflow results, whereas the chosen alternative at the end of the workflow is considered as the starting point for the next design phases. Our proposal has been implemented using Building Information Models (BIM) and validated by means of a case study on a healthcare building and several real demonstrations from different countries in the context of the European project STREAMER. Full article

(This article belongs to the Special Issue Zero-Carbon Buildings)

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12639 KiB

Open AccessArticle

Optimal Energy Management of Combined Cooling, Heat and Power in Different Demand Type Buildings Considering Seasonal Demand Variations

by Akhtar Hussain, Van-Hai Bui, Hak-Man Kim, Yong-Hoon Im and Jae-Yong Lee

Energies 2017, 10(6), 789; https://doi.org/10.3390/en10060789 - 08 Jun 2017

Cited by 31 | Viewed by 5169

Abstract

In this paper, an optimal energy management strategy for a cooperative multi-microgrid system with combined cooling, heat and power (CCHP) is proposed and has been verified for a test case of building microgrids (BMGs). Three different demand types of buildings are considered and [...] Read more.

In this paper, an optimal energy management strategy for a cooperative multi-microgrid system with combined cooling, heat and power (CCHP) is proposed and has been verified for a test case of building microgrids (BMGs). Three different demand types of buildings are considered and the BMGs are assumed to be equipped with their own combined heat and power (CHP) generators. In addition, the BMGs are also connected to an external energy network (EEN), which contains a large CHP, an adsorption chiller (ADC), a thermal storage tank, and an electric heat pump (EHP). By trading the excess electricity and heat energy with the utility grid and EEN, each BMG can fulfill its energy demands. Seasonal energy demand variations have been evaluated by selecting a representative day for the two extreme seasons (summer and winter) of the year, among the real profiles of year-round data on electricity, heating, and cooling usage of all the three selected buildings. Especially, the thermal energy management aspect is emphasized where, bi-lateral heat trading between the energy supplier and the consumers, so-called energy prosumer concept, has been realized. An optimization model based on mixed integer linear programming has been developed for minimizing the daily operation cost of the EEN while fulfilling the energy demands of the BMGs. Simulation results have demonstrated the effectiveness of the proposed strategy. Full article

(This article belongs to the Special Issue Zero-Carbon Buildings)

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2675 KiB

Open AccessArticle

Multi-Objective Optimization for Energy Performance Improvement of Residential Buildings: A Comparative Study

by Kangji Li, Lei Pan, Wenping Xue, Hui Jiang and Hanping Mao

Energies 2017, 10(2), 245; https://doi.org/10.3390/en10020245 - 17 Feb 2017

Cited by 52 | Viewed by 8506

Abstract

Numerous conflicting criteria exist in building design optimization, such as energy consumption, greenhouse gas emission and indoor thermal performance. Different simulation-based optimization strategies and various optimization algorithms have been developed. A few of them are analyzed and compared in solving building design problems. [...] Read more.

Numerous conflicting criteria exist in building design optimization, such as energy consumption, greenhouse gas emission and indoor thermal performance. Different simulation-based optimization strategies and various optimization algorithms have been developed. A few of them are analyzed and compared in solving building design problems. This paper presents an efficient optimization framework to facilitate optimization designs with the aid of commercial simulation software and MATLAB. The performances of three optimization strategies, including the proposed approach, GenOpt method and artificial neural network (ANN) method, are investigated using a case study of a simple building energy model. Results show that the proposed optimization framework has competitive performances compared with the GenOpt method. Further, in another practical case, four popular multi-objective algorithms, e.g., the non-dominated sorting genetic algorithm (NSGA-II), multi-objective particle swarm optimization (MOPSO), the multi-objective genetic algorithm (MOGA) and multi-objective differential evolution (MODE), are realized using the propose optimization framework and compared with three criteria. Results indicate that MODE achieves close-to-optimal solutions with the best diversity and execution time. An uncompetitive result is achieved by the MOPSO in this case study. Full article

(This article belongs to the Special Issue Zero-Carbon Buildings)

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1320 KiB

Open AccessArticle

Exploring the Environment/Energy Pareto Optimal Front of an Office Room Using Computational Fluid Dynamics-Based Interactive Optimization Method

by Kangji Li, Wenping Xue and Guohai Liu

Energies 2017, 10(2), 231; https://doi.org/10.3390/en10020231 - 15 Feb 2017

Cited by 10 | Viewed by 4767

Abstract

This paper is concerned with the development of a high-resolution and control-friendly optimization framework in enclosed environments that helps improve thermal comfort, indoor air quality (IAQ), and energy costs of heating, ventilation and air conditioning (HVAC) system simultaneously. A computational fluid dynamics (CFD)-based [...] Read more.

This paper is concerned with the development of a high-resolution and control-friendly optimization framework in enclosed environments that helps improve thermal comfort, indoor air quality (IAQ), and energy costs of heating, ventilation and air conditioning (HVAC) system simultaneously. A computational fluid dynamics (CFD)-based optimization method which couples algorithms implemented in Matlab with CFD simulation is proposed. The key part of this method is a data interactive mechanism which efficiently passes parameters between CFD simulations and optimization functions. A two-person office room is modeled for the numerical optimization. The multi-objective evolutionary algorithm—non-dominated-and-crowding Sorting Genetic Algorithm II (NSGA-II)—is realized to explore the environment/energy Pareto front of the enclosed space. Performance analysis will demonstrate the effectiveness of the presented optimization method. Full article

(This article belongs to the Special Issue Zero-Carbon Buildings)

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3231 KiB

Open AccessArticle

Development of an Nearly Zero Emission Building (nZEB) Life Cycle Cost Assessment Tool for Fast Decision Making in the Early Design Phase

by Hae Jin Kang

Energies 2017, 10(1), 59; https://doi.org/10.3390/en10010059 - 06 Jan 2017

Cited by 17 | Viewed by 5917

Abstract

An economic feasibility optimization method for the life cycle cost (LCC) has been developed to apply energy saving techniques in the early design stages of a building. The method was developed using default data (e.g., operation schedules), energy consumption prediction equations and cost [...] Read more.

An economic feasibility optimization method for the life cycle cost (LCC) has been developed to apply energy saving techniques in the early design stages of a building. The method was developed using default data (e.g., operation schedules), energy consumption prediction equations and cost prediction equations utilizing design variables considered in the early design phase. With certain equations developed, an LCC model was constructed using the computational program MATLAB, to create an automated optimization process. To verify the results from the newly developed assessment tool, a case study on an office building was performed to outline the results of the designer’s proposed model and the cost optimal model. Full article

(This article belongs to the Special Issue Zero-Carbon Buildings)

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