Urban energy simulation: Simplification and reduction of building envelope models

Kim, Eui-Jong; Plessis, Gilles; Hubert, Jean-Luc; Roux, Jean-Jacques

doi:10.1016/j.enbuild.2014.07.066

Cited by 63 publications

(26 citation statements)

References 8 publications

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“…The surface nodes, T we and T wi ( • C), distinctly account for the absorbed (Q s1 ) and transmitted (Q s2 ) solar irradiances. These are in units of W/m 2 , and the calculation method is detailed in Reference [37]. R e and R i (W/ • C) approximately represent the thermal resistances related to outside and inside convective heat transfer; for example, R e = 1/(h c,e × S w ), and R w1 and R w2 are equal to the reciprocals of k w / e × S w .…”

Section: We Dt Wementioning

confidence: 99%

“…From the predicted total horizontal solar irradiance, Q s1 and Q s2 can be calculated as a function of the vertical solar irradiances and the corresponding surface area of walls and windows of the target building [37]. The vertical solar irradiances on the walls and windows in different orientations can be obtained from the total horizontal irradiance using a common direct and diffuse split model [44].…”

Section: Data Averagingmentioning

confidence: 99%

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Learning-Based Predictive Building Energy Model Using Weather Forecasts for Optimal Control of Domestic Energy Systems

et al. 2018

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The aim of this study is to develop a model that can accurately calculate building loads and demand for predictive control. Thus, the building energy model needs to be combined with weather prediction models operated by a model predictive controller to forecast indoor temperatures for specified rates of supplied energy. In this study, a resistance–capacitance (RC) building model is proposed where the parameters of the models are determined by learning. Particle swarm optimization is used as a learning scheme to search for the optimal parameters. Weather prediction models are proposed that use a limited amount of forecasting information fed by local meteorological centers. Assuming that weather forecasting was perfect, hourly outdoor temperatures were accurately predicted; meanwhile, differences were observed in the predicted solar irradiances values. In investigations to verify the proposed method, a seven-resistance, five-capacitance (7R5C) model was tested against a reference model in EnergyPlus using the predicted weather data. The root-mean-square errors of the 7R5C model in the prediction of indoor temperatures on all the specified days were within 0.5 °C when learning was performed using reference data obtained from the previous five days and weather prediction was included. This level of deviation in predictive control is acceptable considering the magnitudes of the loads and demand of the tested building.

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Section: We Dt Wementioning

confidence: 99%

Section: Data Averagingmentioning

confidence: 99%

Learning-Based Predictive Building Energy Model Using Weather Forecasts for Optimal Control of Domestic Energy Systems

et al. 2018

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“…Most of these simplified thermal structures are based on the concept of grey-box models and stochastic linear differential equations (Melgaard 1994;Madsen 2007). In this context, Kim et al (2014) presented a reduction technique for building envelope models in urban simulations to ensure a good trade-off between computation time and precision. In 2015, Nytsch-Geusen and Kaul developed a tool that generates populations of low-order models based on geographic information and statistical methods to calculate energy demand.…”

Section: Introductionmentioning

confidence: 99%

Assessing scalability of a low-voltage distribution grid co-simulation through functional mock-up interface

Reinbold

Protopapadaki

Tavella

et al. 2019

Journal of Building Performance Simulation

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State-of-the-art Modelica tools for modeling and simulating multi-physical systems have reached certain maturity among the building physics community. Hence, simulation is widely used for control, sizing and performance assessment of energy systems. However, serious efficiency issues arise for large-scale models. This article proposes a practical application of co-simulation methods on detailed district energy systems. The aim of this study is to assess performance and scalability of cosimulation through Functional Mock-up Interfaces on a detailed and multi-physical district model. In particular, we propose a comparative analysis between classical simulation and co-simulation methods and a scalability analysis on a growing number of buildings. The models have been implemented using Modelica language and the OpenIDEAS library. A decomposition approach is taken for modeling the entire system, while stochasticity in the inputs is taken into account. Results are presented for various integration scenarios, including a classical integrated simulation for reference and co-simulations involving different master-algorithms within Dymola and DACCOSIM 2017. Scenarios are compared in terms of speed-up and accuracy of principal physical quantities representing key performance indicators such as indoor temperature, current and voltage at building's connection. The analysis shows that co-simulation can run up to 90 times faster than the integrated simulation for 24 buildings, while ensuring acceptable accuracy.

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“…Energetic and environmental performance assessment can be parallely analyzed [9,8]. Extensions on the urban level were made respectively from the aspect of building envelope design for overall energy efficiency [10].…”

Section: Literature Reviewmentioning

confidence: 99%

Energy Performance Simulation for a Residential Building

Harmathy¹,

Magyar²

2017

Proceedings of the 48th International HVAC&R Congress

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Urban energy simulation: Simplification and reduction of building envelope models

Cited by 63 publications

References 8 publications

Learning-Based Predictive Building Energy Model Using Weather Forecasts for Optimal Control of Domestic Energy Systems

Learning-Based Predictive Building Energy Model Using Weather Forecasts for Optimal Control of Domestic Energy Systems

Assessing scalability of a low-voltage distribution grid co-simulation through functional mock-up interface

Energy Performance Simulation for a Residential Building

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