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Data Driven Heating Energy Load Forecast Modeling Enhanced by Nonlinear Autoregressive Exogenous Neural Networks

Jeong-A Ryu and Seongju Ch
Korea Advanced Institute of Science and Technology/Civil and Environmental Engineering, Daejeon, South Korea
Abstract—As the building sector consumes considerable portion of energy worldwide, effective management of building energy is of great importance. In this regard, forecasting building energy consumption is essential to use and manage the energy efficiently. This paper describes hourly heating energy load forecasting method with the load dataset of National Renewable Energy Laboratory (NREL)'s Research Support Facility (RSF) in the United States using both typical Artificial Neural Network and Nonlinear Autoregressive with Exogenous Inputs (NARX) Neural Network. The accuracy of the model is evaluated by MBE (Mean Bias Error) and CvRMSE (Coefficient of Variation of the Root Mean Square Error). The NARX neural network model showed a better performance than typical ANN model and it is confirmed that the model satisfies the acceptable error range proposed by ASHRAE guideline 14. This research explored a way to build a better performing neural network model for heating energy load prediction based on accumulated dataset.

Index Terms—Building Energy, Heating Load Forecasting, Artificial Neural Network (ANN), Nonlinear Autoregressive with Exogenous Inputs (NARX) Neural Network

Cite: Jeong-A Ryu and Seongju Ch, "Data Driven Heating Energy Load Forecast Modeling Enhanced by Nonlinear Autoregressive Exogenous Neural Networks," International Journal of Structural and Civil Engineering Research, Vol. 8, No. 3, pp. 246-252, August 2019. doi: 10.18178/ijscer.8.3.246-252
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