Electrical load forecasting through long short term memory
For a power supplier, meeting demand-supply equilibrium is of utmost importance. Electrical energy must be generated according to demand, as a large amount of electrical energy cannot be stored. For the proper functioning of a power supply system, an adequate model for predicting load is a necessity...
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| Main Authors: | , , , , |
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| Format: | EJournal Article |
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Institute of Advanced Engineering and Science,
2022-01-01.
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| Online Access: | Get fulltext |
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| LEADER | 02549 am a22003373u 4500 | ||
|---|---|---|---|
| 001 | ijeecs25250_15850 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Mishra, Debani Prasad |e author |
| 100 | 1 | 0 | |a Woosong University's Academic Research Funding - 2021 |e contributor |
| 700 | 1 | 0 | |a Mishra, Sanhita |e author |
| 700 | 1 | 0 | |a Yadav, Rakesh Kumar |e author |
| 700 | 1 | 0 | |a Vishnoi, Rishabh |e author |
| 700 | 1 | 0 | |a Salkuti, Surender Reddy |e author |
| 245 | 0 | 0 | |a Electrical load forecasting through long short term memory |
| 260 | |b Institute of Advanced Engineering and Science, |c 2022-01-01. | ||
| 500 | |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/25250 | ||
| 520 | |a For a power supplier, meeting demand-supply equilibrium is of utmost importance. Electrical energy must be generated according to demand, as a large amount of electrical energy cannot be stored. For the proper functioning of a power supply system, an adequate model for predicting load is a necessity. In the present world, in almost every industry, whether it be healthcare, agriculture, and consulting, growing digitization and automation is a prominent feature. As a result, large sets of data related to these industries are being generated, which when subjected to rigorous analysis, yield out-of-the-box methods to optimize the business and services offered. This paper aims to ascertain the viability of long short term memory (LSTM) neural networks, a recurrent neural network capable of handling both long-term and short-term dependencies of data sets, for predicting load that is to be met by a Dispatch Center located in a major city. The result shows appreciable accuracy in forecasting future demand. | ||
| 540 | |a Copyright (c) 2021 Institute of Advanced Engineering and Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc/4.0 | ||
| 546 | |a eng | ||
| 690 | |a Power Engineering; Electrical Engineering | ||
| 690 | |a Daily load curve; Factors affecting load; Long short-term memory; Monthly load curve; Recurrent neural network; Root mean square deviation; | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 25, No 1: January 2022; 42-50 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v25.i1 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/25250/15850 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/25250/15850 |z Get fulltext |