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Автор: Grafiati
Опубліковано: 14 березня 2023

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Статті в журналах з теми "Energy management in building"

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Yin, Hang. "Building Management System to support building renovation." Boolean: Snapshots of Doctoral Research at University College Cork, no. 2010 (January 1, 2010): 164–69. http://dx.doi.org/10.33178/boolean.2010.37.

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Анотація:
Many publications have concluded that around 40% of the world’s energy costs are incurred in buildings. The biggest energy users in a building are facilities which cover 40% to 60% of the total energy cost. In recent years, construction work undertaken in building renovation and rehabilitation has increased considerably. Technical renovations have always brought better building management. Modern technology has a more user friendly interface as well as giving us the successful management of building systems and associated reduced costs. In order to implement more energy efficiency in existing buildings, Building Management System (BMS) and Building Information Modelling (BIM) play important roles in the energy & cost savings of the building’s life. This paper emphasises the use of Information and Communication Technology (ICT) to support and justify essential building renovation that will improve a building’s performance and decrease annual energy costs. We will present an introduction to BMS and BIM ...
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El Khaili, Mohamed, Redouane Marhoum, Chaimaa Fouhad, and Hassan Ouajji. "Contribution to Multi-Energy Flow Management for Building Energy Hub." Journal of Ubiquitous Systems and Pervasive Networks 15, no. 01 (March 1, 2021): 27–34. http://dx.doi.org/10.5383/juspn.15.01.004.

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Анотація:
Global demand for primary fossil energy continues to increase. However, the production of energy from fossil fuels, in addition to depleting available reserves, releases millions of tons of Greenhouse Gas (GHG) into the atmosphere. Thus, it is obvious that the high concentration of GHGs in the air disrupts the natural greenhouse effect and consequently causes global warming. The implementation of action plans aimed at reducing greenhouse gas emissions has led all countries to use clean energy sources (sun, earth, wind) called renewable energies and also to rationalize the use of energies whether based on fossil fuels or renewable. Our paper presents a modeling of the demand and its management to ensure an optimization of the energy consumption and the reduction of its bill
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A. Gabbar, Hossam, Ahmed Eldessouky, and Jason Runge. "Evaluation of renewable energy deployment scenarios for building energy management." AIMS Energy 4, no. 5 (2016): 742–61. http://dx.doi.org/10.3934/energy.2016.5.742.

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Yoon, Seok-Ho, Seung-Yeon Kim, Geon-Hee Park, Yi-Kang Kim, Choong-Ho Cho, and Byung-Hun Park. "Multiple power-based building energy management system for efficient management of building energy." Sustainable Cities and Society 42 (October 2018): 462–70. http://dx.doi.org/10.1016/j.scs.2018.08.008.

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Siregar, Marsul, Firma Purbantoro, and Tajuddin Nur. "Implementation of Energy Management Concept and Energy Management System in High Rise Office Building." Jurnal TIARSIE 16, no. 3 (September 30, 2019): 85. http://dx.doi.org/10.32816/tiarsie.v16i3.55.

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Анотація:
Energy Management Concept as part of Green Building Concept is focused to Improve Energy Efficiency Index (EEI) and Water Consumption Index (WCI). The Implementation Energy Management Concept in an office buildings of this study based on the management system model of continual improvement ISO 50001:2011. The purpose of this study was to determine the extent to which the implementation of green building principles in Office Buildings. This study took the case study in an office building in Jakarta Indonesia that has two towers, each tower has 32 floors and 3 basement floors. The method used is descriptive with respect to GREENSHIP Rating Tools for existing building which consists of six categories; Appropriate Site Development (ASD), Energy Efficiency & Conservation (EEC), Water Conservation (WAC), Material Resources & Cycle (MRC), Indoor Air Health & Comfort (IHC) and Building & Environments Management (BEM). The results show that implementation the Energy Management Concept could also made energy performance more efficient, after Implementing through Retrofitting of the Chiller System, Recycle Waste Water, Replacement of Conventional lamp to Energy Saving LED and also Training and Education to all employees and tenants. From comparing data research before implementation of Energy Management Concept in 2014 and after Implementation and retrofitting in 2016, 2017 & 2018, it is found that Energy Efficiency Index (EEI) from 238.8 kwh/m2/Years to 134,04kwh/m2/Year and Water Consumption Index (WCI)From 50 liter/person/Day to 27.18 Liter/person/Day. And the saving cost from electricity bill payments is IDR. 466,803,325.67 / month (18%) and roughly will Break Event Point (BEP) for 3.86 Years
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Kamali, Saeed, Golrokh Khakzar, and Soolmaz Abdali Hajiabadi. "Effect of Building Management System on Energy Saving." Advanced Materials Research 856 (December 2013): 333–37. http://dx.doi.org/10.4028/www.scientific.net/amr.856.333.

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Анотація:
Without any doubt, one of the most worldwide challenging and controversial issues in the current century is the energy problem. In most countries, the increase of energy consumption, especially in building, has made energy saving and efficiency strategies important target for energy policies. In general, there are many ways to save energy. The most common method of economizing is within culturalization. For such purpose, building energy management system (BEMS) is considered as the latest idea of energy. Having a dynamic environment, smart buildings are affordable by the integration of four main elements: systems, structure, service, management, and the relationship between them. Intelligent buildings provide these benefits through intelligent control systems. In this paper, while introducing the energy management in buildings, it studies their applications and also their effects on management and optimization of energy consumption. The office building in San Francisco, USA with 66,943 ft2 area is considered as a case study for this research. Energy consumption is reduced 50 percent by implementing BMS in this building.
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Be´nard, C., B. Guerrier, and M. M. Rosset-Loue¨rat. "Optimal Building Energy Management: Part I—Modeling." Journal of Solar Energy Engineering 114, no. 1 (February 1, 1992): 2–12. http://dx.doi.org/10.1115/1.2929978.

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Анотація:
We analyze the advantages of solving building energy management problems with the techniques of optimal control. Our approach consists of describing the dynamic behavior of a heated building with a simple model and controlling the whole system by minimizing a criterion defined for a time horizon of a few days. The two control components are the heat delivered to the building, and the variable heat exchange through the building envelope. In Part I, input (control and meteorological data) and output (indoor temperature) are related through a simplified state-space representation of the building. Part II is devoted to the actual computation of the control input. Results are given for two categories of buildings: The first is characterized by important direct solar gains. The inside structure is of low thermal inertia and so is the heating system. The second type of building is well insulated, with less glazing and less solar gain. The heavy internal structure of the building and the distribution of heat give a large thermal inertia to the system.
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Taesler, Roger. "Climate and building energy management." Energy and Buildings 16, no. 1-2 (January 1991): 599–608. http://dx.doi.org/10.1016/0378-7788(91)90028-2.

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Gabbar, Hossam A., and Ahmed S. Eldessouky. "Energy Semantic Network for Building Energy Management." Intelligent Industrial Systems 1, no. 3 (September 2, 2015): 213–31. http://dx.doi.org/10.1007/s40903-015-0023-8.

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Määttänen, Eeva, Riikka Kyrö, Anna Aaltonen, Anna-Liisa Sarasoja, and Seppo Junnila. "Remote energy management benefits in retail building portfolios." Journal of Facilities Management 12, no. 1 (January 28, 2014): 56–71. http://dx.doi.org/10.1108/jfm-09-2012-0043.

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Анотація:
Purpose – The study aims to investigate the effects of a remote energy management service to the energy consumption of retail buildings. The study focuses on analysing the changes in energy consumption after the implementation of a facility service concept where building processes are optimized with a remote energy management system. The paper seeks to demonstrate that remotely operated building management practices, which allow high competence service for all facilities, have a positive impact, beyond traditional facility services, on energy and environmental performance of buildings. Design/methodology/approach – The research analyses the metered energy consumption of two retail building portfolios comprising altogether 44 properties. Additionally, secondary data are collected from archive reviews, observation and interviews. Findings – The research shows that remote energy management service reduced the total energy consumption during the two-year service period by 12 and 6 per cent depending on the portfolio. Electricity consumption was found to decrease by 7 per cent and heating energy by 26 per cent on the average in the first portfolio, and 7 and 4 per cent in the second one, respectively. Research limitations/implications – Variation between buildings was found to be relatively high as the individual characteristics and history of the different buildings inevitably affect the achieved results. Practical implications – The study indicates that remote energy management offers an effective means to reduce the energy consumption and costs, and ultimately climate impacts derived from buildings. Originality/value – The study adds to the knowledge of facilities management in context to energy management and environmental performance of buildings.
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Дисертації з теми "Energy management in building"

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Seeam, Amar Kumar. "Validation of a building simulation tool for predictive control in energy management systems." Thesis, University of Edinburgh, 2015. http://hdl.handle.net/1842/16196.

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Анотація:
Buildings are responsible for a significant portion of energy consumption worldwide. Intelligent buildings have been devised as a potential solution, where energy consumption and building use are harmonised. At the heart of the intelligent building is the building energy management system (BEMS), the central platform which manages and coordinates all the building monitoring and control subsystems, such as heating and lighting loads. There is often a disconnect between the BEMS and the building it is installed in, leading to inefficient operation, due to incongruous commissioning of sensors and control systems. In these cases, the BEMS has a lack of knowledge of the building form and function, requiring further complex optimisation, to facilitate efficient all year round operation. Flawed BEMS configurations can then lead to ‘sick buildings’. Recently, building energy performance simulation (BEPS) has been viewed as a conceptual solution to assist in efficient building control. Building energy simulation models offer a virtual environment to test many scenarios of BEMS operation strategies and the ability to quickly evaluate their effects on energy consumption and occupant comfort. Challenges include having an accurate building model, but recent advances in building information modelling (BIM) offer the chance to leverage existing building data, which can be translated into a form understood by the building simulator. This study will address these challenges, by developing and integrating a BEMS, with a BIM for BEPS assisted predictive control, and assessing the outcome and potential of the integration.
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Yeung, Chi-hung, and 楊志雄. "A survey of environmental impacts of building energy codes on energy management in building services installations." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2000. http://hub.hku.hk/bib/B42575424.

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Yeung, Chi-hung. "A survey of environmental impacts of building energy codes on energy management in building services installations." Click to view the E-thesis via HKUTO, 2000. http://sunzi.lib.hku.hk/hkuto/record/B42575424.

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Lee, Sang Hoon. "Management of building energy consumption and energy supply network on campus scale." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/43580.

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Анотація:
Building portfolio management on campus and metropolitan scale involves decisions about energy retrofits, energy resource pooling, and investments in shared energy systems, such as district cooling, community PV and wind power, CHP systems, geothermal systems etc. There are currently no tools that help a portfolio/campus manager make these decisions by rapid comparison of variants. The research has developed an energy supply network management tool at the campus scale. The underlying network energy performance (NEP) model uses (1) an existing energy performance toolkit to quantify the energy performance of building energy consumers on hourly basis, and (2) added modules to calculate hourly average energy generation from a wide variety of energy supply systems. The NEP model supports macro decisions at the generation side (decisions about adding or retrofitting campus wide systems) and consumption side (planning of new building design and retrofit measures). It allows testing different supply topologies by inspecting which consumer nodes should connect to which local suppliers and to which global suppliers, i.e. the electricity and gas utility grids. A prototype software implementation allows a portfolio or campus manager to define the demand and supply nodes on campus scale and manipulate the connections between them through a graphical interface. The NEP model maintains the network topology which is represented by a directed graph with the supply and demand nodes as vertices and their connections as arcs. Every change in the graph automatically triggers an update of the energy generation and consumption pattern, the results of which are shown on campus wide energy performance dashboards. The dissertation shows how the NEP model supports decision making with respect to large-scale building energy system design with a case study of the Georgia Tech campus evaluating the following three assertions: 1. The normative calculations at the individual building scale are accurate enough to support the network energy performance analysis 2. The NEP model supports the study of the tradeoffs between local building retrofits and campus wide energy interventions in renewable systems, under different circumstances 3. The NEP approach is a viable basis for routine campus asset management policies.
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Lu, Hai. "Energy Quality Management for New Building Clusters and Districts." Licentiate thesis, KTH, Installationsteknik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-118561.

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Анотація:
The level of concern regarding the total energy consumption in new building clusters and urban districts (BCDs) has increased recently. Rising living standards have led to a significant increase in building energy consumption over the past few decades. A great potential for energy savings exists through energy quality management (EQM) for new BCDs. Quality of energy measures the useful work potential of certain energy. EQM in this thesis is defined as reducing energy demand, applying distributed renewable energy sources, and utilizing energy technology in sustainable way. According to this definition, tasks of EQM include energy supply system optimization and energy demand prediction. Based on EQM, the optimization of BCDs’ energy supply systems aims to search for the most appropriate scenario, which is a trade-off between various aspects, such as energy performance and environmental impacts as well as system reliability. A novel multi-objective optimization approach for new BCDs is established in this thesis. Optimization algorithm is known as Genetic Algorithm (GA), which is used to address non-linear optimization problems. Two case studies are included in this thesis: the U.K. eco-town residential BCDs case and the Norway office BCDs case. The U.K. case examines the application possibility of the approach in practical design. Optimization objectives involved in this case are the life-cycle global warming potential of the system and the system exergy efficiency. The total life-cycle global warming potential is minimized while the exergy efficiency is maximized. Different types of energy supply system scenarios are recommended with different optimization objective combinations (equal-importance, slightly exergy efficiency-oriented and slightly environment-oriented). The results show that the proposed approach can feasibly be an optimal design tool in practical use. To provide deeper insights into the problem, the Norway case checks the expansibility of inserting additional objectives into the approach. Loss of Power Supply Probability (LPSP), which is one of the system reliability indicators, is additionally included in the optimization objectives. For this case, the approach guarantees the optimal scenarios that cannot exceed the desired LPSP with minimum life-cycle global warming potential and maximum exergy efficiency. Optimal scenarios with different desired LPSP values (0, 1%, and 5%) are compared. Comparison results demonstrate that optimal scenarios change significantly along with variations of the desired LPSP values. Therefore, system reliability is proven as one of the most important objectives for renewable energy system optimization. In the future, this approach can be applied to complex problems with more objectives. Besides energy supply system optimization, an effective and precise BCDs energy demand model is needed. This model should be capable of providing reliable inputs (energy demand and load profiles) for energy supply system optimization and reducing unnecessary energy consumption. In principle, energy demand in BCDs is a complex task because numerous design criteria influence energy performance, which is hard to plan and pre-calculate. Establishing such a model would require a thorough decision base that prioritizes these design criteria and generally distinguishes the more important criteria from the less important ones. The study uses general survey aims to collect and identify the design criteria that affect the BCDs energy demand model and to evaluate the priorities of each criterion using the fuzzy Analytical Hierarchy Process (AHP) method. Four main criteria – location, building characteristics, government, and outdoor surrounding characteristics – are established, along with 13 secondary criteria. The results show that the use of the AHP method can accurately guide the energy demand model and automatically rank significant criteria. The method can provide the weighting value for each criterion as well as the relative ranking for the energy demand model. This thesis aims to provide a systematic and holistic EQM method for BCDs energy system design at the beginning of the decision-making stage.

QC 20130221

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Doylend, Nicholas. "Evaluating building energy performance : a lifecycle risk management methodology." Thesis, Loughborough University, 2015. https://dspace.lboro.ac.uk/2134/18022.

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Анотація:
There is widespread acceptance of the need to reduce energy consumption within the built environment. Despite this, there are often large discrepancies between the energy performance aspiration and operational reality of modern buildings. The application of existing mitigation measures appears to be piecemeal and lacks a whole-system approach to the problem. This Engineering Doctorate aims to identify common reasons for performance discrepancies and develop a methodology for risk mitigation. Existing literature was reviewed in detail to identify individual factors contributing to the risk of a building failing to meet performance aspirations. Risk factors thus identified were assembled into a taxonomy that forms the basis of a methodology for identifying and evaluating performance risk. A detailed case study was used to investigate performance at whole-building and sub-system levels. A probabilistic approach to estimating system energy consumption was also developed to provide a simple and workable improvement to industry best practice. Analysis of monitoring data revealed that, even after accounting for the absence of unregulated loads in the design estimates, annual operational energy consumption was over twice the design figure. A significant part of this discrepancy was due to the space heating sub-system, which used more than four times its estimated energy consumption, and the domestic hot water sub-system, which used more than twice. These discrepancies were the result of whole-system lifecycle risk factors ranging from design decisions and construction project management to occupant behaviour and staff training. Application of the probabilistic technique to the estimate of domestic hot water consumption revealed that the discrepancies observed could be predicted given the uncertainties in the design assumptions. The risk taxonomy was used to identify factors present in the results of the qualitative case study evaluation. This work has built on practical building evaluation techniques to develop a new way of evaluating both the uncertainty in energy performance estimates and the presence of lifecycle performance risks. These techniques form a risk management methodology that can be applied usefully throughout the project lifecycle.
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Yang, Rui. "Development of Integrated Building Control Systems for Energy and Comfort Management in Intelligent Buildings." University of Toledo / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1384447299.

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Afzalan, Milad. "Data-driven customer energy behavior characterization for distributed energy management." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/99210.

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Анотація:
With the ever-growing concerns of environmental and climate concerns for energy consumption in our society, it is crucial to develop novel solutions that improve the efficient utilization of distributed energy resources for energy efficiency and demand response (DR). As such, there is a need to develop targeted energy programs, which not only meet the requirement of energy goals for a community but also take the energy use patterns of individual households into account. To this end, a sound understanding of the energy behavior of customers at the neighborhood level is needed, which requires operational analytics on the wealth of energy data from customers and devices. In this dissertation, we focus on data-driven solutions for customer energy behavior characterization with applications to distributed energy management and flexibility provision. To do so, the following problems were studied: (1) how different customers can be segmented for DR events based on their energy-saving potential and balancing peak and off-peak demand, (2) what are the opportunities for extracting Time-of-Use of specific loads for automated DR applications from the whole-house energy data without in-situ training, and (3) how flexibility in customer demand adoption of renewable and distributed resources (e.g., solar panels, battery, and smart loads) can improve the demand-supply problem. In the first study, a segmentation methodology form historical energy data of households is proposed to estimate the energy-saving potential for DR programs at a community level. The proposed approach characterizes certain attributes in time-series data such as frequency, consistency, and peak time usage. The empirical evaluation of real energy data of 400 households shows the successful ranking of different subsets of consumers according to their peak energy reduction potential for the DR event. Specifically, it was shown that the proposed approach could successfully identify the 20-30% of customers who could achieve 50-70% total possible demand reduction for DR. Furthermore, the rebound effect problem (creating undesired peak demand after a DR event) was studied, and it was shown that the proposed approach has the potential of identifying a subset of consumers (~5%-40% with specific loads like AC and electric vehicle) who contribute to balance the peak and off-peak demand. A projection on Austin, TX showed 16MWh reduction during a 2-h event can be achieved by a justified selection of 20% of residential customers. In the second study, the feasibility of inferring time-of-use (ToU) operation of flexible loads for DR applications was investigated. Unlike several efforts that required considerable model parameter selection or training, we sought to infer ToU from machine learning models without in-situ training. As the first part of this study, the ToU inference from low-resolution 15-minute data (smart meter data) was investigated. A framework was introduced which leveraged the smart meter data from a set of neighbor buildings (equipped with plug meters) with similar energy use behavior for training. Through identifying similar buildings in energy use behavior, the machine learning classification models (including neural network, SVM, and random forest) were employed for inference of appliance ToU in buildings by accounting for resident behavior reflected in their energy load shapes from smart meter data. Investigation on electric vehicle (EV) and dryer for 10 buildings over 20 days showed an average F-score of 83% and 71%. As the second part of this study, the ToU inference from high-resolution data (60Hz) was investigated. A self-configuring framework, based on the concept of spectral clustering, was introduced that automatically extracts the appliance signature from historical data in the environment to avoid the problem of model parameter selection. Using the framework, appliance signatures are matched with new events in the electricity signal to identify the ToU of major loads. The results on ~1500 events showed an F-score of >80% for major loads like AC, washing machine, and dishwasher. In the third study, the problem of demand-supply balance, in the presence of varying levels of small-scale distributed resources (solar panel, battery, and smart load) was investigated. The concept of load complementarity between consumers and prosumers for load balancing among a community of ~250 households was investigated. The impact of different scenarios such as varying levels of solar penetration, battery integration level, in addition to users' flexibility for balancing the supply and demand were quantitatively measured. It was shown that (1) even with 100% adoption of solar panels, the renewable supply cannot cover the demand of the network during afternoon times (e.g., after 3 pm), (2) integrating battery for individual households could improve the self-sufficiency by more than 15% during solar generation time, and (3) without any battery, smart loads are also capable of improving the self-sufficiency as an alternative, by providing ~60% of what commercial battery systems would offer. The contribution of this dissertation is through introducing data-driven solutions/investigations for characterizing the energy behavior of households, which could increase the flexibility of the aggregate daily energy load profiles for a community. When combined, the findings of this research can serve to the field of utility-scale energy analytics for the integration of DR and improved reshaping of network energy profiles (i.e., mitigating the peaks and valleys in daily demand profiles).
Doctor of Philosophy
Buildings account for more than 70% of electricity consumption in the U.S., in which more than 40% is associated with the residential sector. During recent years, with the advancement in Information and Communication Technologies (ICT) and the proliferation of data from consumers and devices, data-driven methods have received increasing attention for improving the energy-efficiency initiatives. With the increased adoption of renewable and distributed resources in buildings (e.g., solar panels and storage systems), an important aspect to improve the efficiency by matching the demand and supply is to add flexibility to the energy consumption patterns (e.g., trying to match the times of high energy demand from buildings and renewable generation). In this dissertation, we introduced data-driven solutions using the historical energy data of consumers with application to the flexibility provision. Specific problems include: (1) introducing a ranking score for buildings in a community to detect the candidates that can provide higher energy saving in the future events, (2) estimating the operation time of major energy-intensive appliances by analyzing the whole-house energy data using machine learning models, and (3) investigating the potential of achieving demand-supply balance in communities of buildings under the impact of different levels of solar panels, battery systems, and occupants energy consumption behavior. In the first study, a ranking score was introduced that analyzes the historical energy data from major loads such as washing machines and dishwashers in individual buildings and group the buildings based on their potential for energy saving at different times of the day. The proposed approach was investigated for real data of 400 buildings. The results for EV, washing machine, dishwasher, dryer, and AC show that the approach could successfully rank buildings by their demand reduction potential at critical times of the day. In the second study, machine learning (ML) frameworks were introduced to identify the times of the day that major energy-intensive appliances are operated. To do so, the input of the model was considered as the main circuit electricity information of the whole building either in lower-resolution data (smart meter data) or higher-resolution data (60Hz). Unlike previous studies that required considerable efforts for training the model (e.g, defining specific parameters for mathematical formulation of the appliance model), the aim was to develop data-driven approaches to learn the model either from the same building itself or from the neighbors that have appliance-level metering devices. For the lower-resolution data, the objective was that, if a few samples of buildings have already access to plug meters (i.e., appliance level data), one could estimate the operation time of major appliances through ML models by matching the energy behavior of the buildings, reflected in their smart meter information, with the ones in the neighborhood that have similar behaviors. For the higher-resolution data, an algorithm was introduced that extract the appliance signature (i.e., change in the pattern of electricity signal when an appliance is operated) to create a processed library and match the new events (i.e., times that an appliance is operated) by investigating the similarity with the ones in the processed library. The investigation on major appliances like AC, EV, dryer, and washing machine shows the >80% accuracy on standard performance metrics. In the third study, the impact of adding small-scale distributed resources to individual buildings (solar panels, battery, and users' practice in changing their energy consumption behavior) for matching the demand-supply for the communities was investigated. A community of ~250 buildings was considered to account for realistic uncertain energy behavior across households. It was shown that even when all buildings have a solar panel, during the afternoon times (after 4 pm) in which still ~30% of solar generation is possible, the community could not supply their demand. Furthermore, it was observed that including users' practice in changing their energy consumption behavior and battery could improve the utilization of solar energy around >10%-15%. The results can serve as a guideline for utilities and decision-makers to understand the impact of such different scenarios on improving the utilization of solar adoption. These series of studies in this dissertation contribute to the body of literature by introducing data-driven solutions/investigations for characterizing the energy behavior of households, which could increase the flexibility in energy consumption patterns.
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Marmoux, Pierre-Benoît. "Energy services for high performance buildings and building clusters - towards better energy quality management in the urban built environment." Thesis, KTH, Byggvetenskap, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-98798.

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Анотація:
With an increasing awareness of energy consumption and CO 2emission in the population, several initiatives to reduce CO2emissions have been presented all around the world. The main part of these initiatives is a reduction of the energy consumption for existing buildings, while the others concern the building of eco-districts with low-energy infrastructures and even zero-energy infrastructures. In this idea of reducing the energy consumption and of developing new clean areas, this master thesis will deal with the high energy quality services for new urban districts. In the scope of this master thesis project, the new concept of sustainable cities and of clusters of buildings will be approached in order to clearly understand the future challenges that the world’s population is going to face during this century. Indeed, due to the current alarming environmental crisis, the need to reduce human impacts on the environment is growing more and more and is becoming inescapable. We will present a way to react to the current situation and to counteract it thanks to new clean technologies and to new analysis approaches, like the exergy concept. Through this report, we are going to analyze the concepts of sustainable cities and clusters of buildings as systems, and focus on their energy aspects in order to set indoor climate parameters and energy supply parameters to ensure high energy quality services supplies to high performance buildings. Thanks to the approach of the exergy concept, passive and active systems such as nocturnal ventilation or floor heating and cooling systems have been highlighted in order to realize the ‘energy saving’ opportunities that our close environment offers. This work will be summarized in a methodology that will present a way to optimize the energy use of all services aspects in a building and the environmental friendly characteristics of the energy resources mix, which will supply the buildings’ low energy demands.
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Mauser, Ingo [Verfasser], and H. [Akademischer Betreuer] Schmeck. "Multi-modal Building Energy Management / Ingo Mauser ; Betreuer: H. Schmeck." Karlsruhe : KIT-Bibliothek, 2017. http://d-nb.info/113602154X/34.

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Книги з теми "Energy management in building"

1

Associates, Paul Overy and. Building energy management systems. Dublin: Irish Energy Centre, 1996.

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2

P.S.A. Specialist Services. Energy management: A checklist for building services. Watford: Building Research Establishment, 1992.

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3

University College Dublin. Buildings and Services Department. and Irish Energy Centre, eds. Building energy management systems at UCD Belfield. Dublin: Irish Energy Centre, 1996.

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4

Energy management and operating costs in buildings. London: E & FN Spon, 1997.

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5

Hyde, Timothy Ronald. Integrated building energy management and condition monitoring ssystems. Manchester: University of Manchester, 1995.

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6

Chartered Institution of Building Services Engineers and Great Britain. Department of Trade and Industry, eds. Commissioning management. London: CIBSE, 2003.

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7

C, Sherratt A. F., Construction Industry Conference Centre, and Chartered Institution of Building Services Engineers., eds. Energy management in buildings. London: Hutchinson, 1986.

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8

Mulholland, John. Energy management in buildings. Lincoln: IEMA, 2003.

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9

Papadopoulou, Elena V. M. Energy Management in Buildings Using Photovoltaics. London: Springer London, 2012.

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10

Building energy management systems: Applications to low energy HVAC and natural ventilation control. 2nd ed. London: E & FN Spon, 2000.

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Частини книг з теми "Energy management in building"

1

Tokuç, Ayça. "Building Energy Management." In Encyclopedia of Sustainable Management, 1–7. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-02006-4_82-1.

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2

Ramli, Nor Azuana, and Mel Keytingan M. Shapi. "Building Energy Management." In Control of Smart Buildings, 37–73. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0375-5_3.

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3

Elder, Keith E. "Building Envelope." In Energy Management Handbook, 233–60. Ninth edition. | Louisville, Kentucky : Fairmont Press, Inc., [2018]: River Publishers, 2020. http://dx.doi.org/10.1201/9781003151364-9.

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4

Barooah, Prabir. "Building Energy Management System." In Encyclopedia of Systems and Control, 1–7. London: Springer London, 2019. http://dx.doi.org/10.1007/978-1-4471-5102-9_100083-1.

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5

Barooah, Prabir. "Building Energy Management System." In Encyclopedia of Systems and Control, 180–87. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-44184-5_100083.

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6

Sayed, Khairy, and Hossam A. Gabbar. "Building Energy Management Systems (BEMS)." In Energy Conservation in Residential, Commercial, and Industrial Facilities, 15–81. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2018. http://dx.doi.org/10.1002/9781119422099.ch2.

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7

So, Albert Ting-pat, and Wai Lok Chan. "Building Automation and Energy Management." In The International Series on Asian Studies in Computer and Information Science, 41–46. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-5019-8_7.

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Baggini, Angelo, and Annalisa Marra. "Building Automation, Control and Management Systems." In Electrical Energy Efficiency, 71–124. Chichester, UK: John Wiley & Sons, Ltd, 2012. http://dx.doi.org/10.1002/9781119990048.ch4.

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9

Luoranen, Mika, and Samuli Honkapuro. "Controlling the Building Energy Footprint." In Encyclopedia of Sustainable Management, 1–9. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-02006-4_420-1.

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10

Yao, Runming, and Alan Short. "Energy Efficient Building Design." In Design and Management of Sustainable Built Environments, 179–202. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-4781-7_10.

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Тези доповідей конференцій з теми "Energy management in building"

1

"Building energy management technologies." In IECON 2014 - 40th Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2014. http://dx.doi.org/10.1109/iecon.2014.7049312.

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2

Marchiori, Alan, Qi Han, William C. Navidi, and Lieko Earle. "Building the case for automated building energy management." In the Fourth ACM Workshop. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2422531.2422536.

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3

Rahimian, Mina, Daniel Cardoso-Llach, and Lisa Domenica Iulo. "Participatory Energy Management in Building Networks." In First International Symposium on Sustainable Human–Building Ecosystems. Reston, VA: American Society of Civil Engineers, 2015. http://dx.doi.org/10.1061/9780784479681.003.

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4

Chandra, Rohit, Soumen Banerjee, and Sanjib Kumar Panda. "Building energy management system for transactive energy framework." In 2018 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES). IEEE, 2018. http://dx.doi.org/10.1109/pedes.2018.8707803.

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5

Azzi, D. "Multivariable modelling for building energy management." In IEE Colloquium on Modelling and Simulation for Thermal Management. IEE, 1997. http://dx.doi.org/10.1049/ic:19970268.

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6

Krishnamurthy, Karthik, Pradeep Singh, and Nikhil Sriraman. "GeoBMS for Better Building Energy Management." In ASME 2019 13th International Conference on Energy Sustainability collocated with the ASME 2019 Heat Transfer Summer Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/es2019-3901.

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Анотація:
Abstract Optimization of building energy usage presents an impactful and readily addressable industry opportunity. Commercial building operators have, over the past decade, invested in on-premise Building Management Systems (BMSs) to centrally monitor and operate building sensors and controllers. BMS configurations degrade over time due to changes in building occupancy patterns as well as from ongoing sensor and controller upgrades. Recent studies reveal that an additional 10% energy savings opportunity would be available if optimal BMS configurations were sustained. Building operators face significant challenges in keeping BMS configurations optimized. The reasons are many. First, most BMSs offer proprietary interfaces that require custom, one-off integrations for remote access. Second, inconsistent BMS data representation makes it hard to aggregate and analyze performance data in order to operate systems with maximum efficiency. Third, BMSs are often designed as single user applications, creating complications to support multiple stakeholders that collectively dictate optimal usage. We propose a hybrid cloud/on-premise model that addresses the limitations of current, on-premise BMS implementations and incorporates the benefits of new cloud technologies. Our hybrid model employs a cloud-based infrastructure “middle layer” (which we call GeoBMS) that connects the “top layer” of building performance applications with the “bottom layer” of existing brownfield BMS implementations. GeoBMS addresses BMS inaccessibility through virtualization; inconsistent data representation through common cloud data models; and lack of multi-stakeholder access through global authentication. Through published APIs, GeoBMS enables the creation of innovative building performance applications. Applications use GeoBMS APIs to access previously unavailable on-premise BMS functionality and configuration data. We illustrate using a proof-of-concept application (which we call EnergyOptimize) that optimizes energy consumption for a museum case-example.
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7

Zucker, Gerhard, Usman Habib, Max Blochle, Alexander Wendt, Samer Schaat, and Lydia Chaido Siafara. "Building energy management and data analytics." In 2015 International Symposium on Smart Electric Distribution Systems and Technologies (EDST). IEEE, 2015. http://dx.doi.org/10.1109/sedst.2015.7315253.

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8

Jamil, Majid, and Sonam Mittal. "Building Energy Management System: A Review." In 2017 14th IEEE India Council International Conference (INDICON). IEEE, 2017. http://dx.doi.org/10.1109/indicon.2017.8488004.

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9

Saurav, Kumar, and Vijay Arya. "Efficient Management of Building Energy Resources." In 2019 11th International Conference on Communication Systems & Networks (COMSNETS). IEEE, 2019. http://dx.doi.org/10.1109/comsnets.2019.8711105.

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Hettiarachchi, D. G., G. M. A. Jaward, V. P. V. Tharaka, J. M. D. S. Jeewandara, and K. T. M. U. Hemapala. "IoT Based Building Energy Management System." In 2021 3rd International Conference on Electrical Engineering (EECon). IEEE, 2021. http://dx.doi.org/10.1109/eecon52960.2021.9580866.

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Звіти організацій з теми "Energy management in building"

1

Rahman, Saifur. Building Energy Management Open Source Software. Office of Scientific and Technical Information (OSTI), August 2017. http://dx.doi.org/10.2172/1376213.

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2

Unknown. BUILDING TRIBAL CAPABILITIES IN ENERGY AND ENVIRONMENTAL MANAGEMENT. Office of Scientific and Technical Information (OSTI), March 2000. http://dx.doi.org/10.2172/767398.

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3

Galvin, James, and Trevor Bailey. Scalable Deployment of Advanced Building Energy Management Systems. Fort Belvoir, VA: Defense Technical Information Center, June 2013. http://dx.doi.org/10.21236/ada600339.

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4

Adetola, Veronica, Sunil Ahuja, Trevor Bailey, Bing Dong, Taimoor Khawaja, Dong Luo, Zheng O Neill, and Madhusudana Shashanka. Scalable Deployment of Advanced Building Energy Management Systems. Fort Belvoir, VA: Defense Technical Information Center, May 2013. http://dx.doi.org/10.21236/ada600343.

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5

Lopez, Mary. BUILDING TRIBAL CAPABILITIES IN ENERGY AND ENVIRONMENTAL MANAGEMENT. Office of Scientific and Technical Information (OSTI), July 2003. http://dx.doi.org/10.2172/821595.

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6

Zavala, Victor M. Building-Wide, Adaptive Energy Management Systems for High-Performance Buildings: Final CRADA Report. Office of Scientific and Technical Information (OSTI), October 2016. http://dx.doi.org/10.2172/1334081.

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7

Hernandez, Adriana. HVAC & Building Management Control System Energy Efficiency Replacements. Office of Scientific and Technical Information (OSTI), September 2012. http://dx.doi.org/10.2172/1063877.

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Rowe, Anthony, Mario Berges, and Christopher Martin. An Extensible Sensing and Control Platform for Building Energy Management. Office of Scientific and Technical Information (OSTI), April 2016. http://dx.doi.org/10.2172/1245109.

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9

Zavala, V. M., C. Thomas, M. Zimmerman, and A. Ott. Next-generation building energy management systems and implications for electricity markets. Office of Scientific and Technical Information (OSTI), August 2011. http://dx.doi.org/10.2172/1024600.

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10

Johra, Hicham. What is building energy flexibility – demand response? Department of the Built Environment, Aalborg University, 2023. http://dx.doi.org/10.54337/aau518320296.

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