Estimating the energy-saving potential in national building stocks – A methodology review

Brøgger, Morten; Wittchen, Kim Bjarne

doi:10.1016/j.rser.2017.05.239

Cited by 79 publications

(48 citation statements)

References 23 publications

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“…Statistical bottom-up models for residential consumption are typically developed based on historic consumption data of a sample of representative consumers (or buildings) and additional variables describing those consumers. Compared to engineering models, statistical bottom-up models are less dependent of detailed physical data and assumptions regarding user behavior [16,17]. Common statistical bottom-up modeling techniques are regression and artificial neural networks (ANN).…”

Section: Methods For Modeling Aggregate Hourly Energy Consumptionmentioning

confidence: 99%

Modeling Aggregate Hourly Energy Consumption in a Regional Building Stock

Kipping

Trømborg

2017

Energies

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Sound estimates of future heat and electricity demand with high temporal and spatial resolution are needed for energy system planning, grid design, and evaluating demand-side management options and polices on regional and national levels. In this study, smart meter data on electricity consumption in buildings are combined with cross-sectional building information to model hourly electricity consumption within the household and service sectors on a regional basis in Norway. The same modeling approach is applied to model aggregate hourly district heat consumption in three different consumer groups located in Oslo. A comparison of modeled and metered hourly energy consumption shows that hourly variations and aggregate consumption per county and year are reproduced well by the models. However, for some smaller regions, modeled annual electricity consumption is over-or underestimated by more than 20%. Our results indicate that the presented method is useful for modeling the current and future hourly energy consumption of a regional building stock, but that larger and more detailed training datasets are required to improve the models, and more detailed building stock statistics on regional level are needed to generate useful estimates on aggregate regional energy consumption.

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Section: Methods For Modeling Aggregate Hourly Energy Consumptionmentioning

confidence: 99%

Modeling Aggregate Hourly Energy Consumption in a Regional Building Stock

Kipping

Trømborg

2017

Energies

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“…They are expected to become a key planning tool to seek the most effective energy policies and strategies at the neighborhood, district and city levels [3,4,5]. Bottom-up physics-based engineering [6,7,8,9] urban building energy models (UBEM) [4] forecast the performance of several dozens to thousands of buildings. The approach of UBEM is to apply physical models of heat and mass flow in and around buildings to predict operational energy uses as well as indoor and outdoor environmental conditions for groups of buildings [4].…”

Section: From Building-scale To Urban-scale Energy Modelsmentioning

confidence: 99%

“…Occupant behavior is one of the main reasons for systematic discrepancies between the calculated or expected energy demand in buildings and the actual energy consumption -the performance gap [9,22]. The cause is related to the use of unrealistic input parameters regarding occupant behavior and facilities management in building energy models [22] and the high sensitivity of occupant behavior parameters [23].…”

Section: Occupant Behavior -One Reason For the Performance Gapmentioning

confidence: 99%

“…The main concerns with occupant behavior on the urban-scale are the diversity among buildings of the same occupancy type, that should be accounted for to obtain realistic energy demand patterns [36,37] and the impacts of behavioral changes over time onto building energy demand [9]. Preliminary research on urban-scale occupant behavior models concludes that stochastic approaches, rather than deterministic, should be considered when high resolution temporal behavior and realistic peak loads are important [18,38,39].…”

Section: Occupant Behavior In Urban-scale Building Energy Modelsmentioning

confidence: 99%

“…On the other hand, for studies based on monthly or yearly energy consumption, the effect of human behavior might not be significant due to averaging effects [39]. In their review on building stock modeling Brøgger and Wittchen [9] speculate that it might not be necessary to model the energy demand of individual buildings exactly, but general trends of socioeconomic and behavioral changes have to be captured.…”

Section: Occupant Behavior In Urban-scale Building Energy Modelsmentioning

confidence: 99%

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A review on occupant behavior in urban building energy models

Happle

Fonseca

Schlueter

2018

Energy and Buildings

146

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Urban building energy models aspire to become key planning tools for the holistic optimization of buildings, urban design, and energy systems in neighborhoods and districts. The energy demand of buildings is largely influenced by the behavior of the occupants. The insufficient consideration of occupant behavior is one of the causes to the "performance gap" in buildings -the difference between the simulated and the actually observed energy consumption. On the urban-scale impacts of different occupant behavior modeling approaches onto the various purposes of urban building energy models are still largely unknown. Research shows that the inappropriate choice of occupant behavior model could result in oversized district energy systems, leading to over-investment and low operational efficiency. This work therefore reviews urban building energy models in terms of their occupant behavior modeling approaches. Three categories of approaches are established and discussed: (1) deterministic space-based approaches, (2) stochastic space-based approaches, and (3) stochastic person-based approaches. They are further assessed in terms of their strategy to consider diversity in occupant behavior. Stochastic models, especially stochastic person-based models, seem to be superior to deterministic models. However, there are no stochastic models available yet that can be used for case studies of mixed-districts, comprising buildings of various occupancy types. In the reviewed urban-scale approaches, only single-use residential or office districts are modeled with stochastic techniques. However, people interact with various buildings on a daily basis. Their activities relate to their presence in different spaces at the urban-scale and to their use of appliances in those spaces. Their individual levels of comfort and behavioral patterns govern the control actions towards building systems. Therefore, a novel activity-based multi-agent approach for urban occupant behavior modeling is proposed as alternative to current approaches.Keywords: urban building energy model, energy-related occupant behavior, occupancy, occupant behavior model IntroductionUrban areas house more than half of the global population and cities are responsible for more than 70% of the global carbon dioxide emissions. At the same time, cities are crucial actors in the transition towards sustainable energy Email address: happle@arch.ethz.ch (Gabriel Happle)

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Artificial intelligence implementation framework development for building energy saving

et al. 2020

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In this study, artificial intelligence (AI) control tools were developed to construct an AI implementation framework for energy saving for buildings. Although numerous AI studies related to energy conservation have been conducted, most of them have reported computing algorithms and control effects for single objects. This is the first study to use a framework to integrate five-category AI control tools to execute three-level building energy conservation; the three levels consist of equipment-level control, facility-level control, and whole building energy saving. Energy-saving effects were tested in a real building. The complex three-floor building primarily with a total area of 9072 m 2 serves as an office space and a semiconductor production line. Seventy percent energy consumption comes from air conditioning system and motor power. Twenty percent is lighting system and the other 10% is plug power and office automation equipment. Before implementation, the yearly energy cost reached US$1004339. In 2018, an AI implementation framework was introduced to systematically deploy AI at the site. A total of 47.5%, 37%, and 36.9% of energy was saved at equipment, facility, and whole building levels; up to US$385203 was saved. These energy savings proved the feasibility of the implementation framework. Furthermore, unmet demands of AI studies were met, and an approach to fill the research gap is discussed. K E Y W O R D S artificial intelligence (AI), artificial intelligence implementation framework (AIif), building energy saving, equipment-level control, facility-level control List of Symbols, Abbreviations, and Notation: ω i , weighting coefficients; GS T , the sum of the global similarities between the selected m cases; MV i , the mean difference of the variable i; P j , proportion of the prediction; V 1 , variation of control parameter y; y ic , the neural outputs of variable i for the control; y ip , the neural outputs of variable i for past cases; AI

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Estimating the energy-saving potential in national building stocks – A methodology review

Cited by 79 publications

References 23 publications

Modeling Aggregate Hourly Energy Consumption in a Regional Building Stock

Modeling Aggregate Hourly Energy Consumption in a Regional Building Stock

A review on occupant behavior in urban building energy models

Artificial intelligence implementation framework development for building energy saving

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