relation: https://khub.utp.edu.my/scholars/16683/
title: Deterioration of Electrical Load Forecasting Models in a Smart Grid Environment
creator: Azeem, A.
creator: Ismail, I.
creator: Jameel, S.M.
creator: Romlie, F.
creator: Danyaro, K.U.
creator: Shukla, S.
description: Smart Grid (S.G.) is a digitally enabled power grid with an automatic capability to control electricity and information between utility and consumer. S.G. data streams are heterogenous and possess a dynamic environment, whereas the existing machine learning methods are static and stand obsolete in such environments. Since these models cannot handle variations posed by S.G. and utilities with different generation modalities (D.G.M.), a model with adaptive features must comply with the requirements and fulfill the demand for new data, features, and modality. In this study, we considered two open sources and one real-world dataset and observed the behavior of ARIMA, ANN, and LSTM concerning changes in input parameters. It was found that no model observed the change in input parameters until it was manually introduced. It was observed that considered models experienced performance degradation and deterioration from 5 to 15 in terms of accuracy relating to parameter change. Therefore, to improve the model accuracy and adapt the parametric variations, which are dynamic in nature and evident in S.G. and D.G.M. environments. The study has proposed a novel adaptive framework to overcome the existing limitations in electrical load forecasting models. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
publisher: MDPI
date: 2022
type: Article
type: PeerReviewed
identifier:   Azeem, A. and Ismail, I. and Jameel, S.M. and Romlie, F. and Danyaro, K.U. and Shukla, S.  (2022) Deterioration of Electrical Load Forecasting Models in a Smart Grid Environment.  Sensors, 22 (12).   ISSN 14248220     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131566537&doi=10.3390%2fs22124363&partnerID=40&md5=1643aa153112ca23eaa092a0d2a2e4f4
relation: 10.3390/s22124363
identifier: 10.3390/s22124363