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Multi-horizon short-term load forecasting using hybrid of LSTM and modified split convolution

Precise short-term load forecasting (STLF) plays a crucial role in the smooth operation of power systems, future capacity planning, unit commitment, and demand response. However, due to its non-stationary and its dependency on multiple cyclic and non-cyclic calendric features and non-linear highly c...

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Autores principales: Ullah, Irshad, Muhammad Hasanat, Syed, Aurangzeb, Khursheed, Alhussein, Musaed, Rizwan, Muhammad, Anwar, Muhammad Shahid
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10557505/
https://www.ncbi.nlm.nih.gov/pubmed/37810340
http://dx.doi.org/10.7717/peerj-cs.1487
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author Ullah, Irshad
Muhammad Hasanat, Syed
Aurangzeb, Khursheed
Alhussein, Musaed
Rizwan, Muhammad
Anwar, Muhammad Shahid
author_facet Ullah, Irshad
Muhammad Hasanat, Syed
Aurangzeb, Khursheed
Alhussein, Musaed
Rizwan, Muhammad
Anwar, Muhammad Shahid
author_sort Ullah, Irshad
collection PubMed
description Precise short-term load forecasting (STLF) plays a crucial role in the smooth operation of power systems, future capacity planning, unit commitment, and demand response. However, due to its non-stationary and its dependency on multiple cyclic and non-cyclic calendric features and non-linear highly correlated metrological features, an accurate load forecasting with already existing techniques is challenging. To overcome this challenge, a novel hybrid technique based on long short-term memory (LSTM) and a modified split-convolution (SC) neural network (LSTM-SC) is proposed for single-step and multi-step STLF. The concatenating order of LSTM and SC in the proposed hybrid network provides an excellent capability of extraction of sequence-dependent features and other hierarchical spatial features. The model is evaluated by the Pakistan National Grid load dataset recorded by the National Transmission and Dispatch Company (NTDC). The load data is pre-processed and multiple other correlated features are incorporated into the data for performance enhancement. For generalization capability, the performance of LSTM-SC is evaluated on publicly available datasets of American Electric Power (AEP) and Independent System Operator New England (ISO-NE). The effect of temperature, a highly correlated input feature, on load forecasting is investigated either by removing the temperature or adding a Gaussian random noise into it. The performance evaluation in terms of RMSE, MAE, and MAPE of the proposed model on the NTDC dataset are 500.98, 372.62, and 3.72% for multi-step while 322.90, 244.22, and 2.38% for single-step load forecasting. The result shows that the proposed method has less forecasting error, strong generalization capability, and satisfactory performance on multi-horizon.
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spelling pubmed-105575052023-10-07 Multi-horizon short-term load forecasting using hybrid of LSTM and modified split convolution Ullah, Irshad Muhammad Hasanat, Syed Aurangzeb, Khursheed Alhussein, Musaed Rizwan, Muhammad Anwar, Muhammad Shahid PeerJ Comput Sci Data Mining and Machine Learning Precise short-term load forecasting (STLF) plays a crucial role in the smooth operation of power systems, future capacity planning, unit commitment, and demand response. However, due to its non-stationary and its dependency on multiple cyclic and non-cyclic calendric features and non-linear highly correlated metrological features, an accurate load forecasting with already existing techniques is challenging. To overcome this challenge, a novel hybrid technique based on long short-term memory (LSTM) and a modified split-convolution (SC) neural network (LSTM-SC) is proposed for single-step and multi-step STLF. The concatenating order of LSTM and SC in the proposed hybrid network provides an excellent capability of extraction of sequence-dependent features and other hierarchical spatial features. The model is evaluated by the Pakistan National Grid load dataset recorded by the National Transmission and Dispatch Company (NTDC). The load data is pre-processed and multiple other correlated features are incorporated into the data for performance enhancement. For generalization capability, the performance of LSTM-SC is evaluated on publicly available datasets of American Electric Power (AEP) and Independent System Operator New England (ISO-NE). The effect of temperature, a highly correlated input feature, on load forecasting is investigated either by removing the temperature or adding a Gaussian random noise into it. The performance evaluation in terms of RMSE, MAE, and MAPE of the proposed model on the NTDC dataset are 500.98, 372.62, and 3.72% for multi-step while 322.90, 244.22, and 2.38% for single-step load forecasting. The result shows that the proposed method has less forecasting error, strong generalization capability, and satisfactory performance on multi-horizon. PeerJ Inc. 2023-09-15 /pmc/articles/PMC10557505/ /pubmed/37810340 http://dx.doi.org/10.7717/peerj-cs.1487 Text en © 2023 Ullah et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
spellingShingle Data Mining and Machine Learning
Ullah, Irshad
Muhammad Hasanat, Syed
Aurangzeb, Khursheed
Alhussein, Musaed
Rizwan, Muhammad
Anwar, Muhammad Shahid
Multi-horizon short-term load forecasting using hybrid of LSTM and modified split convolution
title Multi-horizon short-term load forecasting using hybrid of LSTM and modified split convolution
title_full Multi-horizon short-term load forecasting using hybrid of LSTM and modified split convolution
title_fullStr Multi-horizon short-term load forecasting using hybrid of LSTM and modified split convolution
title_full_unstemmed Multi-horizon short-term load forecasting using hybrid of LSTM and modified split convolution
title_short Multi-horizon short-term load forecasting using hybrid of LSTM and modified split convolution
title_sort multi-horizon short-term load forecasting using hybrid of lstm and modified split convolution
topic Data Mining and Machine Learning
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10557505/
https://www.ncbi.nlm.nih.gov/pubmed/37810340
http://dx.doi.org/10.7717/peerj-cs.1487
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