Predicting Heating Load in Energy-Efficient Buildings Through Machine Learning Techniques

Hossein Moayedi,Zongjie Lyu,Loke Kok Foong,Dieu Tien Bui,Anastasios Dounis

doi:10.3390/app9204338

Abstract

The heating load calculation is the first step of the iterative heating, ventilation, and air conditioning (HVAC) design procedure. In this study, we employed six machine learning techniques, namely multi-layer perceptron regressor (MLPr), lazy locally weighted learning (LLWL), alternating model tree (AMT), random forest (RF), ElasticNet (ENet), and radial basis function regression (RBFr) for the problem of designing energy-efficient buildings. After that, these approaches were used to specify a relationship among the parameters of input and output in terms of the energy performance of buildings. The calculated outcomes for datasets from each of the above-mentioned models were analyzed based on various known statistical indexes like root relative squared error (RRSE), root mean squared error (RMSE), mean absolute error (MAE), correlation coefficient (R2), and relative absolute error (RAE). It was found that between the discussed machine learning-based solutions of MLPr, LLWL, AMT, RF, ENet, and RBFr, the RF was nominated as the most appropriate predictive network. The RF network outcomes determined the R2, MAE, RMSE, RAE, and RRSE for the training dataset to be 0.9997, 0.19, 0.2399, 2.078, and 2.3795, respectively. The RF network outcomes determined the R2, MAE, RMSE, RAE, and RRSE for the testing dataset to be 0.9989, 0.3385, 0.4649, 3.6813, and 4.5995, respectively. These results show the superiority of the presented RF model in estimation of early heating load in energy-efficient buildings.

Highlights

IntroductionArtificial intelligence-based methods have been dramatically applied by scientists in different fields of study, in energy systems engineering (such as in Nguyen et al [1]and Najafi et al [2])
In recent decades, artificial intelligence-based methods have been dramatically applied by scientists in different fields of study, in energy systems engineering
For realizing the best artificial intelligence (AI) model to meet this goal, this study provides and compares five well-known models that are widely used by researchers [4,5,6,7,8]

Summary

Introduction

Artificial intelligence-based methods have been dramatically applied by scientists in different fields of study, in energy systems engineering (such as in Nguyen et al [1]and Najafi et al [2]). The world’s energy consumption is still maintained at a high value and even though many countries have taken some reasonable measures it is expected that energy consumption will increase in future. Many believe that this is because of the rapid expansion of economy and the improvement of living requirements. Similar to other research in the fields of science and technology, AI techniques have widespread application in order to put forward reasonable evaluation in many engineering problems [9,10,11,12,13,14,15,16,17] of the energy consumption in buildings. In the field of energy management, neural networks have emerged as one of the effective prediction tools [30,31,32,33]

Objectives

Results

Conclusion