Energy performance gap of a nearly Zero Energy Building (nZEB) in Denmark: the influence of occupancy modelling

Carpino, Cristina; Loukou, Evangelia; Heiselberg, Per; Arcuri, Natale

doi:10.1080/09613218.2019.1707639

Cited by 40 publications

(25 citation statements)

References 68 publications

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“…For the same reasons, the HPWHs equipped with electrical boosters were considered as an unchangeable system for the production of DHW. Despite that it affects the results noticeably [38], the influence of occupant behavior was not considered in the energy analysis because the main goal of this paper was the optimization of the building-plant system during the design process For the financial and the macroeconomic projections, the cost-optimal analysis results were displayed in terms of annualized energy-related costs against the fossil contribution in the formation of the annual net primary energy absorbed from the grid. The first were defined by considering the electricity expenses sustained in the lifespan, annualized by a capital recovery factor and incremented by the EEM costs (and CO 2 emission cost in macroeconomic evaluations), obtaining more reduced values when compared with the items provided by the life cycle cost (LCC) analysis.…”

Section: Methodsmentioning

confidence: 99%

The Cost-Optimal Analysis of a Multistory Building in the Mediterranean Area: Financial and Macroeconomic Projections

et al. 2020

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Cost-optimal analysis was pointed out in the 2010/31 European Directive as a tool to evaluate the achievable building energy performance levels as a function of the corresponding costs. These analyses can be carried out by a financial projection for private investors and a macroeconomic approach to establish the minimal energy performance levels. Consequently, the financial projection provides different results that could stimulate private investors toward other cost-optimal solutions that do not match the minimal energy performance levels. For this purpose, both the projections were analyzed in the BEopt environment, developed by NREL, on a multistory building located in two contrasting climatic zones of the Mediterranean area, one cold and the other warm, highlighting the differences. The cost-optimal solutions were identified by a parametric study involving measures that affect thermal losses and solar gains, whereas the air-conditioning plant was left unchanged in order to include a fraction of renewable energy in the coverage of the building demands. Results showed that both the projections produced the same cost-optimal solutions, however, the latter matches the building designed to fulfill the minimal energy performance levels only in the cold climate. Conversely, noticeable deviations were detected in the warm location, therefore minimal energy performance levels should be revised, with preference for less insulated opaque surfaces and better performing glazing systems. Moreover, the macroeconomic scenario returns a more limited distance between the minimal energy performance levels and the cost-optimal solutions, therefore, it is far from the real economic frame sustained by private investors. in more favorable outcomes despite a slight increase in running (operating) costs. Consequently, the building-plant system with the lowest energy demand represents the cost-optimal solution; conversely the latter can be identified as a favorable balance point between energy consumption, investment and operational costs [7]. The 2010/31 EU directive represents the first attempt for the definition of a comparative methodological framework for the calculation of the optimal energy performance levels as a function of the costs, with reference to new and existing buildings [8]. The procedure cannot be generalized across Europe, therefore Member States have to adapt it to the climatic context, the accessibility to the energy infrastructures and the local market. In order to overcome these drawbacks, appropriate guidelines (Regulation 244) [9] were formulated to support the implementation of the cost-optimal procedure in every country by different steps:

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Section: Methodsmentioning

confidence: 99%

The Cost-Optimal Analysis of a Multistory Building in the Mediterranean Area: Financial and Macroeconomic Projections

et al. 2020

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“…One study [85] gathered OB data through virtual reality. Similar to the occupant characteristics information, the captured behavioral information is also largely focused on residential contexts, social and student housing, and a few instances of university buildings (e.g., [5,6,9,10,[12][13][14]16,18,49,50,68,77]). The monitored behavioral information primarily relates to the number of occupants, occupancy schedule, and systems control habits concerning, for instance, operational set-points, the use of appliances and (natural or mechanical) ventilation.…”

Section: Basic Characteristics Of Occupants In the Studiesmentioning

confidence: 99%

“…Indoor conditions were monitored in 20% of the selected studies to evaluate discrepancies between assumed and actual indoor conditions. Air temperature was the most commonly monitored indoor parameter to investigate the performance gap, followed by the relative humidity [7,10,12,[14][15][16]49,72] and the CO 2 concentration levels [10,[14][15][16]27,35,53,54,83,91], which were often used as a proxy for occupancy. Indoor environmental conditions were usually monitored via sensors placed in dwellings, but rarely in non-residential buildings.…”

Section: Empirical Datamentioning

confidence: 99%

“…However, in most reviewed articles, the software applications are used to extract aggregated data to allow benchmarking against monitored data with similar granularities. Common metrics include absolute energy consumption (e.g., in kWh) [16], energy use intensity (e.g., kWh•m −2 •a −1 ) [17], or a similar CO 2 -focused metric (e.g., kg CO 2 •m −2 •a −1 ) [8]. Distinctions are often made between different fuel types and end-uses.…”

Section: Basis For Predicted Energy Usementioning

confidence: 99%

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The Role of Occupants in Buildings’ Energy Performance Gap: Myth or Reality?

et al. 2021

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Buildings’ expected (projected, simulated) energy use frequently does not match actual observations. This is commonly referred to as the energy performance gap. As such, many factors can contribute to the disagreement between expectations and observations. These include, for instance, uncertainty about buildings’ geometry, construction, systems, and weather conditions. However, the role of occupants in the energy performance gap has recently attracted much attention. It has even been suggested that occupants are the main cause of the energy performance gap. This, in turn, has led to suggestions that better models of occupant behavior can reduce the energy performance gap. The present effort aims at the review and evaluation of the evidence for such claims. To this end, a systematic literature search was conducted and relevant publications were identified and reviewed in detail. The review entailed the categorization of the studies according to the scope and strength of the evidence for occupants’ role in the energy performance gap. Moreover, deployed calculation and monitoring methods, normalization procedures, and reported causes and magnitudes of the energy performance gap were documented and evaluated. The results suggest that the role of occupants as significant or exclusive contributors to the energy performance gap is not sufficiently substantiated by evidence.

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“…The deviation between design and actual building performance, known as the performance gap, has been well established in the literature [9][10][11], and is a strong driver for performing M&V [12]. Through measuring and analyzing actual performance, data can be fed back into simulation models allowing the quantification and evaluation of the performance gap [9], as well as the identification of causes and consequent mitigating actions [10,11], such as lack of precision in the definition of realistic boundary conditions at building and settlement level [13]. The measured performance also provides feedback to occupants, thus assisting the transition of occupant behavior to an energy-conscious mindset and enhancing the success of the applied energy strategies and further assisting in closing the performance gap [14].…”

Section: Why Measure and Verify?mentioning

confidence: 99%

Measurement and Verification of Zero Energy Settlements: Lessons Learned from Four Pilot Cases in Europe

et al. 2020

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Measurement and verification (M&V) has become necessary for ensuring intended design performance. Currently, M&V procedures and calculation methods exist for the assessment of Energy Conservation Measures (ECM) for existing buildings, with a focus on reliable baseline model creation and savings estimation, as well as for reducing the computation time, uncertainties, and M&V costs. There is limited application of rigorous M&V procedures in the design, delivery and operation of low/zero energy dwellings and settlements. In the present paper, M&V for four pilot net-zero energy settlements has been designed and implemented. The M&V has been planned, incorporating guidance from existing protocols, linked to the project development phases, and populated with lessons learned through implementation. The resulting framework demonstrates that M&V is not strictly linked to the operational phase of a project but is rather an integral part of the project management and development. Under this scope, M&V is an integrated, iterative process that is accompanied by quality control in every step. Quality control is a significant component of the M&V, and the proposed quality control procedures can support the preparation and implementation of automated M&V. The proposed framework can be useful to project managers for integrating M&V into the project management and development process and explicitly aligning it with the rest of the design and construction procedures.

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Energy performance gap of a nearly Zero Energy Building (nZEB) in Denmark: the influence of occupancy modelling

Cited by 40 publications

References 68 publications

The Cost-Optimal Analysis of a Multistory Building in the Mediterranean Area: Financial and Macroeconomic Projections

The Cost-Optimal Analysis of a Multistory Building in the Mediterranean Area: Financial and Macroeconomic Projections

The Role of Occupants in Buildings’ Energy Performance Gap: Myth or Reality?

Measurement and Verification of Zero Energy Settlements: Lessons Learned from Four Pilot Cases in Europe

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