Near real-time load forecasting of power system using fuzzy time series, artificial neural networks, and wavelet transform models

Yükleniyor...
Küçük Resim

Tarih

2024

Dergi Başlığı

Dergi ISSN

Cilt Başlığı

Yayıncı

Taylor & Francis Inc.

Erişim Hakkı

info:eu-repo/semantics/closedAccess

Özet

Due to the increasing usage of electrical power, the size of electrical power system has increased manifold over the years. There is no inventory or buffer from generation to customer; therefore, to provide a reliable and quality electrical energy whenever demanded, power utility engineers require an adequate, efficient, and precise load forecast to meet continuously varying load demands. This article presents the design and analysis of demand forecasting over shorter interval for power system. The fuzzy time series (FTS), artificial neural network (ANN), and wavelet transform (WT) based forecasting is presented and analyzed in this article. The real-time data from Indian utility is collected for forecasting the demand and to check the effectiveness of FTS, ANN, and WT. The various error definitions are used to calculate the accuracy of the proposed techniques, and the application results verify the superiority of WT and ANN over FTS by showing reduced error value with greater accuracy. Additionally, it is watched that wavelet db3, level 3 is discovered to be the most accurate Daubechies wavelet-oriented technique for predicting the demand in comparison to other dbs, and it highly aligns in reducing the error between actual and predicted demand.

Açıklama

Anahtar Kelimeler

Artificial Neural Network (ANN), Automatic Generation Control (AGC), Fuzzy Time Series (FTS), Load Forecast, Power System Operation, Wavelet Transform

Kaynak

Electric Power Components and Systems

WoS Q Değeri

Q3

Scopus Q Değeri

Q3

Cilt

52

Sayı

5

Künye

Khatoon, S., Ibraheem, Shahid, M., Sharma, G., Çelik, E., Bekiroğlu, E., ... & Priti. (2024). Near Real-Time Load Forecasting of Power System Using Fuzzy Time Series, Artificial Neural Networks, and Wavelet Transform Models. Electric Power Components and Systems, 52(5), 796-810.