Effect of input variables on cooling load prediction accuracy of an office building

Ding, Yan; Zhang, Qiang; Yuan, Tianhao; Fan, Yang

doi:10.1016/j.applthermaleng.2017.09.007

Cited by 87 publications

(23 citation statements)

References 32 publications

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“…They simulated a building using dynamic occupancy area and rate as the input parameters and found that building occupancy data played an important role in load prediction and improved the prediction accuracy significantly. Ding et al [7] also investigated the effects of changes in input variables on prediction accuracy. Their analysis utilized an ANN model and support vector machine with combinations of eight input variables.…”

Section: Introductionmentioning

confidence: 99%

Cooling Load Forecasting via Predictive Optimization of a Nonlinear Autoregressive Exogenous (NARX) Neural Network Model

2019

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Accurate calculations and predictions of heating and cooling loads in buildings play an important role in the development and implementation of building energy management plans. This study aims to improve the forecasting accuracy of cooling load predictions using an optimized nonlinear autoregressive exogenous (NARX) neural network model. The preprocessing of training data and optimization of parameters were investigated for model optimization. In predictive models of cooling loads, the removal of missing values and the adjustment of structural parameters have been shown to help improve the predictive performance of a neural network model. In this study, preprocessing the training data eliminated missing values for times when the heating, ventilation, and air-conditioning system is not running. Also, the structural and learning parameters were adjusted to optimize the model parameters.

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Section: Introductionmentioning

confidence: 99%

Cooling Load Forecasting via Predictive Optimization of a Nonlinear Autoregressive Exogenous (NARX) Neural Network Model

2019

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“…12. Note that the estimation of the hourly peak cooling demand is of particular importance during the building design phase as miscalculations can lead to over-sizing or under-sizing of the HVAC systems (Ding et al 2018).…”

Section: Space Cooling Demandmentioning

confidence: 99%

Impact of exterior convective heat transfer coefficient models on the energy demand prediction of buildings with different geometry

et al. 2019

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Accurate models for exterior convective heat transfer coefficients (CHTC) are important for predicting building energy demand. A detailed review of the literature indicates that existing CHTC models take into account the impact of building geometry either incompletely, or not at all. To the best of our knowledge, research on the impact of exterior CHTC models on the predicted energy performance of buildings with different geometry has not yet been performed. This paper, therefore, investigates the influence of CHTC models on the calculated energy demand of buildings with varying geometry. Building energy simulations are performed for three groups: buildings with Hb (building height) > Wb (building width), buildings with Hb < Wb and buildings with Hb = Wb. Six commonly used CHTC models and a new generalized CHTC model are considered. The generalized CHTC model is expressed as a function of Hb and Wb. The simulations are performed for low and high thermal resistances of the building envelope. The results show that the different CHTC models provide significantly different predictions for the building energy demand. While for annual heating demand, deviations of −14.5% are found, for the annual cooling demand a maximum deviation of +42.0% is obtained, compared to the generalized CHTC model. This study underlines the need for the CHTC models to consider building geometry in their expressions, especially for high-rise buildings. For low-rise builgings, the observed deviations between the existing and the generalized CHTC model are less pronounced.

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“…To verify the accuracy of simulated building cooling loads based on the representative interior load, the Bland-Altman plot method is applied to compare simulated building cooling loads with the measured actual loads. The verification parameter is [27]…”

Section: Modeling and Uncertainty Estimation Of Building Cooling Loadmentioning

confidence: 99%

An Occupant-Oriented Calculation Method of Building Interior Cooling Load Design

et al. 2018

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Given continued improvement in the thermal performance of building envelopes, interior disturbances caused by occupant behavior now have the greatest impact on building loads and energy consumption. The accurate calculation of interior load during design stage was emphasized in this paper, and a new method was proposed. Indoor occupants were considered as the core of interior disturbances, and the relationship with other interior disturbances was explored. The interior heat release was arbitrarily combined with the representative cooling load to be utilized in building cooling load calculation. Field surveys were conducted in three typical university buildings: an office building, a teaching building, and a library, located in a university in Tianjin, China. The oversized chillers supplying cooling for the buildings resulted from the overestimating of the indoor occupant number and the power density of electric appliances. Through quantitative analysis, it was observed that the maximum representative interior loads were 196.43, 329.94, and 402.58 W/person, respectively, for the case buildings, at least 50% less than the empirical design data. Compared to the measured cooling load during the testing period, the accuracy of the modified cooling load was greater than 90%. This research is intended to serve as a reference for calculating and optimizing the design loads of cooling systems.

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Effect of input variables on cooling load prediction accuracy of an office building

Cited by 87 publications

References 32 publications

Cooling Load Forecasting via Predictive Optimization of a Nonlinear Autoregressive Exogenous (NARX) Neural Network Model

Cooling Load Forecasting via Predictive Optimization of a Nonlinear Autoregressive Exogenous (NARX) Neural Network Model

Impact of exterior convective heat transfer coefficient models on the energy demand prediction of buildings with different geometry

An Occupant-Oriented Calculation Method of Building Interior Cooling Load Design

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