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

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.