eprintid: 10196897
rev_number: 11
eprint_status: archive
userid: 699
dir: disk0/10/19/68/97
datestamp: 2024-09-13 07:29:36
lastmod: 2024-09-13 07:31:14
status_changed: 2024-09-13 07:29:36
type: article
metadata_visibility: show
sword_depositor: 699
creators_name: Ashraf, Waqar Muhammad
creators_name: Dua, Vivek
title: Storage of weights and retrieval method (SWARM) approach for neural networks hybridized with conformal prediction to construct the prediction intervals for energy system applications
ispublished: inpress
divisions: UCL
divisions: B04
divisions: F43
keywords: Prediction interval; Uncertainty quantification; Conformal prediction; SWARM
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abstract: The prediction intervals represent the uncertainty associated with the model-predicted responses that impacts the sequential decision-making analytics. Here in this work, we present a novel model-based data-driven approach to construct the prediction intervals around the model-simulated responses using artificial neural network (ANN) model. The loss function is modified with least mean square error and standard deviation between the model-simulated and actual responses for the online-training mode of ANN model development. The parameters (weights and biases) stored during the model development are extracted and are deployed to construct the prediction intervals with 95% confidence level for the test datasets of the three energy systems-based case studies including: crease recovery angle, energy efficiency cooling & energy efficiency heating and gas turbine power plant & coal power plant which are taken from literature, benchmark datasets and industrial-scale applications, respectively. The developed ANN models present root-mean-squared error of 1.20% and 0.52% on test dataset for energy efficiency cooling and energy efficiency heating, respectively. The width of prediction intervals made by the proposed approach, called as Storage of Weights And Retrieval Method (SWARM), incorporates the information available for each test observation during the model training and the SWARM-based prediction intervals are compared to those of inductive conformal prediction (ICP) technique. It is noted that SWARM technique offers better locally adaptive prediction intervals than those of ICP, highlighting the effectiveness of the SWARM technique for the estimation of prediction intervals for the case studies. This research presents a novel data-driven approach to construct the prediction intervals using the model-based information that can be applied on different real-life applications.
date: 2024-07-05
date_type: published
publisher: SPRINGERNATURE
official_url: http://dx.doi.org/10.1007/s41060-024-00595-w
oa_status: green
full_text_type: pub
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 2296982
doi: 10.1007/s41060-024-00595-w
lyricists_name: Dua, Vivek
lyricists_name: Ashraf, Waqar
lyricists_id: VDUAX49
lyricists_id: WMAAS21
actors_name: Ashraf, Waqar
actors_id: WMAAS21
actors_role: owner
funding_acknowledgements: CMMS-PHD-2021-006 [Punjab Educational Endowment Fund]
full_text_status: public
publication: International Journal of Data Science and Analytics
pages: 15
issn: 2364-415X
citation:        Ashraf, Waqar Muhammad;    Dua, Vivek;      (2024)    Storage of weights and retrieval method (SWARM) approach for neural networks hybridized with conformal prediction to construct the prediction intervals for energy system applications.                   International Journal of Data Science and Analytics        10.1007/s41060-024-00595-w <https://doi.org/10.1007/s41060-024-00595-w>.    (In press).    Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10196897/1/Ashraf_s41060-024-00595-w.pdf