Ashraf, Waqar Muhammad;
Dua, Vivek;
(2024)
Data Information integrated Neural Network (DINN) algorithm for modelling and interpretation performance analysis for energy systems.
Energy and AI
, 16
, Article 100363. 10.1016/j.egyai.2024.100363.
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Abstract
Developing a well-predictive machine learning model that also offers improved interpretability is a key challenge to widen the application of artificial intelligence in various application domains. In this work, we present a Data Information integrated Neural Network (DINN) algorithm that incorporates the correlation information present in the dataset for the model development. The predictive performance of DINN is also compared with a standard artificial neural network (ANN) model. The DINN algorithm is applied on two case studies of energy systems namely energy efficiency cooling (ENC) & energy efficiency heating (ENH) of the buildings, and power generation from a 365 MW capacity industrial gas turbine. For ENC, DINN presents lower mean RMSE for testing datasets (RMSE_test = 1.23 %) in comparison with the ANN model (RMSE_test = 1.41 %). Similarly, DINN models have presented better predictive performance to model the output variables of the two case studies. The input perturbation analysis following the Gaussian distribution for noise generation reveals the order of significance of the variables, as made by DINN, can be better explained by the domain knowledge of the power generation operation of the gas turbine. This research work demonstrates the potential advantage to integrate the information present in the data for the well-predictive model development complemented with improved interpretation performance thereby opening avenues for industry-wide inclusion and other potential applications of machine learning.
| Type: | Article |
|---|---|
| Title: | Data Information integrated Neural Network (DINN) algorithm for modelling and interpretation performance analysis for energy systems |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.egyai.2024.100363 |
| Publisher version: | http://dx.doi.org/10.1016/j.egyai.2024.100363 |
| Language: | English |
| Additional information: | Copyright © 2024 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/bync/4.0/). |
| Keywords: | Explainable AI; Model interpretation; Scientific machine learning; Artificial neural network; Loss function |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Chemical Engineering |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10191466 |
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