Year: 2025
Type: article
Abstract: Electricity load forecasting plays a critical role in strategic decision-making processes within the energy sector. Accurate and reliable forecasts are essential for ensuring the efficient operation of energy companies, distribution networks, and energy markets. Due to the complexity and dynamic nature of traditional forecasting methods, electricity load forecasting has become an increasingly challenging topic. In recent years, alongside traditional methods, there has been a growing use of novel and advanced techniques such as Empirical Mode Decomposition and Support Vector Regression in this field. These techniques offer effective alternatives for addressing complex data structures and nonlinear relationships. However, a comprehensive bibliometric analysis of studies and the application of these techniques in literature is yet to be conducted. This study aims to identify trends and future research directions in electricity load forecasting. To this end, a bibliometric analysis was performed using the Visualization of Similarities Viewer software. The findings of this study will serve as a valuable resource for researchers and industry professionals in the field of electricity load forecasting by guiding future research and shedding light on upcoming studies. This research encompasses articles containing the keywords "electrical," "load," and "estimation" published in scientific journals between 2015 and 2024.
Authors: Oya KILCI, Abdulkadir OZTURK, Muslume Beyza YILDIZ, Sude Nur ATIK, Elham Tahsin YASIN, Zeki Berk TONGUÇ, Murat Koklu
Institution: Selçuk University
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7115069035
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