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

Jee-Heon Kim, Nam-Chul Seong and Wonchang Choi
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Jee-Heon Kim: Eco-System Research Center, Gachon University, Seongnam 13120, Korea
Nam-Chul Seong: Eco-System Research Center, Gachon University, Seongnam 13120, Korea
Wonchang Choi: Department of Architectural Engineering, Gachon University, Seongnam 13120, Korea

Sustainability, 2019, vol. 11, issue 23, 1-13

Abstract: 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.

Keywords: cooling load; artificial neural network (ANN); HVAC (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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