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.
Highlights
Building structures consumes about 35% of the world’s total energy and are responsible for 75% of greenhouse gas emissions [1,2]
Demand management is important for dealing with heating/cooling energy that accounts for the largest proportion of energy consumption in buildings
We propose a model optimization method that improves the cooling load prediction performance of a simple structure using a nonlinear autoregressive exogenous (NARX) feedforward neural network
Summary
Building structures consumes about 35% of the world’s total energy and are responsible for 75% of greenhouse gas emissions [1,2]. The optimized combinations of the variables obtained by K-means and hierarchical clustering methods provided accurate results These researchers found that historical cooling capacity data affected prediction accuracy. Koschwitz et al [9] evaluated monthly load predictions using data-driven thermal loads with two nonlinear autoregressive exogenous recurrent neural networks (NARX RNNs) at different depths and a linear epsilon-insensitive support vector machine (ε-SVM) regression model. Their results indicate that the NARX RNN method provides more accurate predictions than the ε-SVM regression model. We propose a model optimization method that improves the cooling load prediction performance of a simple structure using a NARX feedforward neural network
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