Paper Title
Review of Wavelet Transform-Based Short-Term Load Forecasting: Enhancing Accuracy and Efficiency
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Registration ID: IJNRD_226282
Published ID: IJNRD2408377
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Keywords
Decomposition Levels, Short-term load forecasting (STLF), Wavelet Transform, Mean Absolute Percentage Error (MAPE), Non-Stationary Load.
Abstract
Short-term load forecasting (STLF) plays a crucial role in the efficient operation of electric power systems by providing accurate predictions of electricity demand over a limited future period. Wavelet transformation has emerged as a powerful tool in time series analysis due to its ability to capture both time and frequency domain information simultaneously. This paper explores the application of wavelet transformation in the context of STLF. The study begins with an overview of traditional methods used in load forecasting and their limitations, particularly in handling non-stationary and multi-scale characteristics of load data. Wavelet transformation is introduced as a method to address these challenges by decomposing the load time series into different frequency components. The decomposition enables the extraction of relevant features at various scales, which can then be used to improve forecasting accuracy. Several case studies and experiments are presented to demonstrate the effectiveness of wavelet transformation in STLF. These experiments involve real-world load data from different geographical regions and diverse load patterns. Comparative analyses with traditional forecasting techniques illustrate the advantages of wavelet-based approaches in terms of accuracy and robustness. Furthermore, the paper discusses practical considerations and implementation aspects when applying wavelet transformation for STLF, including the selection of wavelet functions, decomposition levels, and forecasting models. It also addresses potential challenges such as computational complexity and parameter tuning. It is suggested that wavelet transformation offers a promising avenue for enhancing the accuracy and reliability of short-term load forecasting in electric power systems. Future research directions are proposed to further explore and refine the application of wavelet-based techniques in this domain.
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How To Cite (APA)
Dr. Shilpa G N & Dr. Nataraja C (August-2024). Review of Wavelet Transform-Based Short-Term Load Forecasting: Enhancing Accuracy and Efficiency. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(8), d377-d383. https://ijnrd.org/papers/IJNRD2408377.pdf
Issue
Volume 9 Issue 8, August-2024
Pages : d377-d383
Other Publication Details
Paper Reg. ID: IJNRD_226282
Published Paper Id: IJNRD2408377
Research Area: Electrical EngineeringÂ
Author Type: Indian Author
Country: TUMAKURU, KARNATAKA, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2408377.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2408377
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