Evaluation of Lighting Performance Risk Using Surrogate Model and EnergyPlus

Hu, Jia; Shen, Eric; Gu, Yun Qing

doi:10.1016/j.proeng.2015.08.471

Cited by 9 publications

(4 citation statements)

References 14 publications

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“…The most frequent category is the data-driven models [121,144,[161][162][163][164][165][166][167][168][169][170]148,[171][172][173][174][175][176][177][178][179][180]149,[181][182][183][184][185][186][187][188][189][190]152,[191][192][193][194][155][156][157][158]160], followed by the stochastic OPA modeling methods [153,159,[195][196][197][198][199]…”

Section: Lighting Operationunclassified

“…Furthermore, nonlinear transformation from input variables to output variables enables the designer to make predictions or classifications with regard to lighting controls [161,193]. However, their main drawbacks are that it takes too much time for the training phase [161] and needs to be trained again if the layout of any lamp is changed [163,165,166]. Regression models can help in predicting the lighting consumption of buildings [210] by providing an accurate estimation of the energy consumption compared to the results obtainable with extrapolation methods that use data from office lighting systems [191].…”

Section: Lighting Operationmentioning

confidence: 99%

“…MSE [155][156][157]161,163,168,193,208]; RMSE [121,152,166,183,184,208,210,211]; statistical parameters such as standard deviation, kurtosis, and skewness [165,167,197,209].…”

Section: Lighting Operationmentioning

confidence: 99%

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Modeling occupant behavior in buildings

Carlucci

Simone

Firth

et al. 2020

Building and Environment

150

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In the last four decades several methods have been used to model occupants' presence and actions (OPA) in buildings according to different purposes, available computational power, and technical solutions. This study reviews approaches, methods and key findings related to OPA modeling in buildings. An extensive database of related research documents is systematically constructed, and, using bibliometric analysis techniques, the scientific production and landscape are described. The initial literature screening identified more than 750 studies, out of which 278 publications were selected. They provide an overarching view of the development of OPA modeling methods. The research field has evolved from longitudinal collaborative efforts since the late 1970s and, so far, covers diverse building typologies mostly concentrated in a few climate zones. The modeling approaches in the selected literature are grouped into three categories (rule-based models, stochastic OPA modeling, and data-driven methods) for modeling occupancy-related target functions and a set of occupants' actions (window, solar shading, electric lighting, thermostat adjustment, clothing adjustment and appliance use). The explanatory modeling is conventionally based on the model-based paradigm where occupant behavior is assumed to be stochastic, while the Revised Manuscript with No Changes MarkedClick here to view linked References data-driven paradigm has found wide applications for the predictive modeling of OPA, applicable to control systems. The lack of established standard evaluation protocols was identified as a scientifically important yet rarely addressed research question. In addition, machine learning and deep learning are emerging in recent years as promising methods to address OPA modeling in real-world applications.

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Section: Lighting Operationunclassified

Section: Lighting Operationmentioning

confidence: 99%

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Modeling occupant behavior in buildings

Carlucci

Simone

Firth

et al. 2020

Building and Environment

150

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“…The method employs several heuristics, which are developed with e.g. Delphi method [15] or Brainstorming. Heuristics are recorded as reasoning rules, which in the next stage provide the core for the fuzzy inference system [16].…”

Section: Risk-based Estimation Of Manufacturing Order Costs With Artimentioning

confidence: 99%

Risk-based estimation of manufacturing order costs with artificial intelligence

Kłosowski

Gola

2016

Annals of Computer Science and Information Systems

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Abstract-The following paper discusses the development of a risk-based cost estimation model for completing non-standard manufacturing orders. The model in question is a hybrid of Monte Carlo Simulation (MCS), which constitutes the main module of the applied model. Vector of order risk probability, which is the input data for the MCS module, is highly difficult to assess and is burdened to a considerable degree with subjectivity, therefore it was resolved that it should be generated with the application of artificial intelligence. Depending on the accessibility of historical data, the model incorporates fuzzy logic or artificial neural networks methods. The presented model could provide support to managers responsible for cost estimation, and moreover, after slight modification also in setting deadlines for non-standard manufacturing orders.I.

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Parameter estimation for building energy models using GRcGAN

Shin

Park

2022

Build. Simul.

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Evaluation of Lighting Performance Risk Using Surrogate Model and EnergyPlus

Cited by 9 publications

References 14 publications

Modeling occupant behavior in buildings

Modeling occupant behavior in buildings

Risk-based estimation of manufacturing order costs with artificial intelligence

Parameter estimation for building energy models using GRcGAN

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