USING ARTIFICIAL NEURAL NETWORKS FOR ELECTRIC LOAD FORECASTING

In today’s evolving energy landscape, accurate electric load forecasting has become critical for ensuring the stability and efficiency of power systems. Traditional methods often fall short due to increasing system complexity and variability. Therefore, this article explores the use of Artificial Neural Networks (ANNs) as a modern, adaptive solution. It highlights the strengths of ANNs in handling nonlinear data, integrating multiple input variables, and learning from past patterns. Moreover, it discusses challenges such as data requirements and interpretability. Ultimately, the article concludes that ANNs offer substantial benefits and will play a key role in the future of sustainable energy management.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-04-22
DOI
https://doi.org/10.5281/zenodo.15258976
Primary Topic
Prenatal Screening and Diagnostics
Type
article
Field-Weighted Citation Impact
0.00
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article

USING ARTIFICIAL NEURAL NETWORKS FOR ELECTRIC LOAD FORECASTING

Zenodo (CERN European Organization for Nuclear Research)
Prenatal Screening and Diagnostics
article

USING ARTIFICIAL NEURAL NETWORKS FOR ELECTRIC LOAD FORECASTING

article en

Abstract

In today’s evolving energy landscape, accurate electric load forecasting has become critical for ensuring the stability and efficiency of power systems. Traditional methods often fall short due to increasing system complexity and variability. Therefore, this article explores the use of Artificial Neural Networks (ANNs) as a modern, adaptive solution. It highlights the strengths of ANNs in handling nonlinear data, integrating multiple input variables, and learning from past patterns. Moreover, it discusses challenges such as data requirements and interpretability. Ultimately, the article concludes that ANNs offer substantial benefits and will play a key role in the future of sustainable energy management.

Zenodo (CERN European Organization for Nuclear Research)
Affordable and clean energy
Openalex Percentile: Top 43%
Prenatal Screening and Diagnostics
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USING ARTIFICIAL NEURAL NETWORKS FOR ELECTRIC LOAD FORECASTING · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS