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Artificial neural network based short term electrical load forecasting

International Journal of Power Electronics and Drive Systems · Vol. 13 No. 1 (2022) · hlm. 586–593

DOI
10.11591/ijpeds.v13.i1.pp586-593
ISSN
2722-256X, 2088-8694
Terbit
2022-03-01
Penerbit
Institute of Advanced Engineering and Science
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

In power generation, a 24-hour load profile can vary significantly throughout the day. Therefore, power generation must be adjusted to reduce money loss due to excess generation. This paper presents a short-term load forecasting (STLF) system design using artificial neural network (ANN). As ANN come in many different configurations, this paper analyzes the best ANN configuration via trial-and-error method. To train the ANN, historical load data from 2016 to 2018 of power south energy cooperative (AEC) is used. A simple feedforward ANN type with one hidden layer is implemented, where 48 neurons are used at the input layer. For hidden layer, an arbitrary 50 neurons are chosen and 24 neurons at output layer are used to generate a day ahead 24-hour load profile. To measure the best activation function for SLTF application, four non-linear activation functions will be tested and the best activation function is used to create two and three hidden layer ANN architecture. Finally, the performance of the two new networks will be compared against one hidden layer model. From the obtained result, the best performing model is found as two hidden layers ANN with Tanh as its hidden layer activation function with 8.9% of testing mean absolute percentage error (MAPE).

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  • University of Technology Malaysia
  • Universitas Ahmad Dahlan
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Rekan penulisOrganisasiArtikel bersama
Chee Wei TanAfiliasi tidak diketahui6
Norjulia Mohamad NordinAfiliasi tidak diketahui3
Jasrul Jamani JamianUniversity of Technology Malaysia2
Shahrin Md AyobAfiliasi tidak diketahui2
Nik Din MuhamadAfiliasi tidak diketahui2
Abba Lawan BukarAfiliasi tidak diketahui2
Syed Norazizul Syed NasirAfiliasi tidak diketahui2
Mohd Zaki DaudAfiliasi tidak diketahui2
Tole SutiknoUniversitas Ahmad Dahlan2
Muhammad Khairie Idham Abd RahmanUniversity of Technology Malaysia2
Musa Mohd MokjiUniversity of Technology Malaysia2
Kwan Yiew LauAfiliasi tidak diketahui1
Zulkarnain Ahmad NoordenUniversity of Technology Malaysia1
Awang JusohUniversity of Technology Malaysia1
Chee Wei TanAfiliasi tidak diketahui1
Cheng Siong LimAfiliasi tidak diketahui1
Lau Kwan YiewAfiliasi tidak diketahui1
Nik Rumzi Nik IdrisUniversity of Technology Malaysia1
Azizah Abdul RazakAfiliasi tidak diketahui1
Hazlina SelamatAfiliasi tidak diketahui1
Abobaker Kikki AbobakerAfiliasi tidak diketahui1
Mohd Junaidi Abdul AzizAfiliasi tidak diketahui1
Shahrin Md. AyobAfiliasi tidak diketahui1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) http://ijpeds.iaescore.com/index.php/IJPEDS/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">Artificial neural network based short term electrical load forecasting</dc:title>
	<dc:creator>Her, Oon Yi</dc:creator>
	<dc:creator>Mahmud, Mohd Saiful Azimi</dc:creator>
	<dc:creator>Abidin, Mohamad Shukri Zainal</dc:creator>
	<dc:creator>Ayop, Razman</dc:creator>
	<dc:creator>Buyamin, Salinda</dc:creator>
	<dc:subject xml:lang="en-US">artificial neural network; electrical energy; load forecasting; power distribution; short term</dc:subject>
	<dc:description xml:lang="en-US">In power generation, a 24-hour load profile can vary significantly throughout the day. Therefore, power generation must be adjusted to reduce money loss due to excess generation. This paper presents a short-term load forecasting (STLF) system design using artificial neural network (ANN). As ANN come in many different configurations, this paper analyzes the best ANN configuration via trial-and-error method. To train the ANN, historical load data from 2016 to 2018 of power south energy cooperative (AEC) is used. A simple feedforward ANN type with one hidden layer is implemented, where 48 neurons are used at the input layer. For hidden layer, an arbitrary 50 neurons are chosen and 24 neurons at output layer are used to generate a day ahead 24-hour load profile. To measure the best activation function for SLTF application, four non-linear activation functions will be tested and the best activation function is used to create two and three hidden layer ANN architecture. Finally, the performance of the two new networks will be compared against one hidden layer model. From the obtained result, the best performing model is found as two hidden layers ANN with Tanh as its hidden layer activation function with 8.9% of testing mean absolute percentage error (MAPE).</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US">Universiti Teknologi Malaysia</dc:contributor>
	<dc:date>2022-03-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijpeds.iaescore.com/index.php/IJPEDS/article/view/21286</dc:identifier>
	<dc:identifier>10.11591/ijpeds.v13.i1.pp586-593</dc:identifier>
	<dc:source xml:lang="en-US">International Journal of Power Electronics and Drive Systems (IJPEDS); Vol 13, No 1: March 2022; 586-593</dc:source>
	<dc:source>2722-256X</dc:source>
	<dc:source>2088-8694</dc:source>
	<dc:source>10.11591/ijpeds.v13.i1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijpeds.iaescore.com/index.php/IJPEDS/article/view/21286/13757</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2022 Oon Yi Her, Mohd Saiful Azimi Mahmud, Mohamad Shukri Zainal Abidin, Razman Ayop, Salinda Buyamin</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>