Sensitivity analysis to reduce duplicated features in ANN training for district heat demand prediction

Chen, Si, Ren, Yaxing, Friedrich, Daniel , Yu, Zhibin and Yu, James (2020) Sensitivity analysis to reduce duplicated features in ANN training for district heat demand prediction. Energy and AI, 2 . p. 100028. ISSN 2666-5468

Full content URL:

Full text not available from this repository.

Item Type:Article
Item Status:Live Archive


Artificial neural network (ANN) has become an important method to model the nonlinear relationships between weather conditions, building characteristics and its heat demand. Due to the large amount of training data required for ANN training, data reduction and feature selection are important to simplify the training. However, in building heat demand prediction, many weather-related input variables contain duplicated features. This paper develops a sensitivity analysis approach to analyse the correlation between input variables and to detect the variables that have high importance but contain duplicated features. The proposed approach is validated in a case study that predicts the heat demand of a district heating network containing tens of buildings at a university campus. The results show that the proposed approach detected and removed several unnecessary input variables and helped the ANN model to reduce approximately 20% training time compared with the traditional methods while maintaining the prediction accuracy. It indicates that the approach can be applied for analysing large number of input variables to help improving the training efficiency of ANN in district heat demand prediction and other applications.

Keywords:Building heat demand prediction, Statistical modelling, Artificial neural network, Sensitivity analysis, Feature selection
Subjects:H Engineering > H100 General Engineering
G Mathematical and Computer Sciences > G150 Mathematical Modelling
Divisions:College of Science > School of Engineering
ID Code:50860
Deposited On:20 Sep 2022 10:04

Repository Staff Only: item control page