eprintid: 10191466
rev_number: 10
eprint_status: archive
userid: 699
dir: disk0/10/19/14/66
datestamp: 2024-04-30 09:27:03
lastmod: 2024-04-30 09:27:03
status_changed: 2024-04-30 09:27:03
type: article
metadata_visibility: show
sword_depositor: 699
creators_name: Ashraf, Waqar Muhammad
creators_name: Dua, Vivek
title: Data Information integrated Neural Network (DINN) algorithm for modelling and interpretation performance analysis for energy systems
ispublished: pub
divisions: UCL
divisions: B04
divisions: C05
divisions: F43
keywords: Explainable AI; Model interpretation; 
Scientific machine learning; Artificial neural network; 
Loss function
note: 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/).
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.
date: 2024-05
date_type: published
publisher: Elsevier BV
official_url: http://dx.doi.org/10.1016/j.egyai.2024.100363
oa_status: green
full_text_type: pub
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 2266048
doi: 10.1016/j.egyai.2024.100363
lyricists_name: Dua, Vivek
lyricists_name: Ashraf, Waqar
lyricists_id: VDUAX49
lyricists_id: WMAAS21
actors_name: Ashraf, Waqar
actors_id: WMAAS21
actors_role: owner
full_text_status: public
publication: Energy and AI
volume: 16
article_number: 100363
issn: 2666-5468
citation:        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 <https://doi.org/10.1016/j.egyai.2024.100363>.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10191466/1/Ashraf_1-s2.0-S2666546824000296-main.pdf