An Enhanced Sampling-Based Approach to Urban Energy Modelling

Ghiassi, Neda; Hammerberg, Kristopher; Taheri, Mahnameh; Pont, Ulrich; Sunanta, Owat; Mahdavi, Ardeshir

doi:10.26868/25222708.2015.2161

Cited by 6 publications

(2 citation statements)

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“…Ideally, a prescriptive indicator should allow a qualitative assessment of a building similar to calculated KPIs or aggregated simulation results, but with by far less effort. Some of the mentioned research efforts ( [12][13][14 [15] and [19]) unraveled that it is difficult to integrate the different influencing parameters on thermal building performance into one single PI. Rather, the combination of different PIs via mathematical/statistical methods, for instance multiple regressions, is required to arrive at a PI that shows a similar behavior as a KPI such as the heating demand.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Prescriptive Indicators for Building Performance - A Ranking Based Approach

Pont

Mahdavi

2019

AMM

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In recent discussions on the evaluation methodology of different aspects of building performance, the idea of so-called prescriptive indicators was proposed. These indicators are simple benchmark values of a building, and do not require any complex calculation or simulation. They are regularly based on certain design parameters pertaining to geometric or semantic aspects of the building, such as compactness and mean weighted U-value. Their purpose – amongst others – is to equip building planners with a very quick method to estimate the performance of their building designs in early design stages and to categorize its performance. Moreover, such prescriptive indicators could be considered an alternative concept to the current practice of energy certification in Europe. The energy certificate calculation methodologies in most countries did increase in complexity in the past years. As a result, the issuing of energy certificates has become a time-consuming and cumbersome process. Moreover, the quality of results of energy certificates became questioned in recent years due to uncertainties connected to input data assumptions and widely interpretable guidelines regarding the issuing. Prescriptive indicators, if their derivation is properly documented, can at least mitigate the issue regarding issuing guidelines due to their simple character. A important research question, however, is the relation between key performance indicators, which are the results of energy certification or building performance simulation, and prescriptive indicators. This contribution suggests a methodology based on rank comparison that might help to identify prescriptive indicators that are similar in their sensitivity on design changes as certain key performance indicators are.

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Section: Introductionmentioning

confidence: 99%

Evaluation of Prescriptive Indicators for Building Performance - A Ranking Based Approach

Pont

Mahdavi

2019

AMM

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“…The methodology is based on the exploitation of a representative building energy model and the use of parametric simulation to create a database for the training of the DDM. The representative energy model could be representative for example of the outcome of a clustering analysis procedure conducted on an urban case study Ghiassi et al (2015). The paper is structured as follows.…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven Modelling Approach for Large Scale Demand Profiling of Residential Buildings

Tardioli¹,

Kerrigan²,

Oates³

et al. 2017

Building Simulation Conference Proceedings

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In this paper the traditional use of data-driven models (DDM) as forecasting tools is coupled with parametric simulation to create a building modelling framework for demand profiling of a large number of buildings of the same typology. Most studies to date utilising DDM have been conducted on single buildings, with less evidence of the role that DDM may have as a modelling technique for application at scale. The proposed methodology is based on the use of a simulation-based building energy modelling tool and a parametric simulator to create a large dataset consisting of 4096 different building model scenarios. Three DDM techniques are utilised; Support Vector Machines, Neural Networks and Generalised Linear Models, these are trained and tested using the generated simulation dataset. Results, at an hourly resolution, show that DDM approaches can correctly emulate the outputs of the building simulation software with mean absolute error ranging from 4 to 9 percent for different DDM algorithms.

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Building energy demand modeling

2019

Urban Energy Systems for Low-Carbon Cities

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An Enhanced Sampling-Based Approach to Urban Energy Modelling

Cited by 6 publications

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Evaluation of Prescriptive Indicators for Building Performance - A Ranking Based Approach

Evaluation of Prescriptive Indicators for Building Performance - A Ranking Based Approach

A Data-Driven Modelling Approach for Large Scale Demand Profiling of Residential Buildings

Building energy demand modeling

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