Advanced Integration of Forecasting Models for Sustainable Load Prediction in Large-Scale Power Systems

Jiansong Tang, Ryosuke Saga (), Hanbo Cai, Zhaoqi Ma and Shuhuai Yu
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Jiansong Tang: Graduate School of Informatics, Osaka Metropolitan University, Sakai 559-8531, Osaka, Japan
Ryosuke Saga: Graduate School of Informatics, Osaka Metropolitan University, Sakai 559-8531, Osaka, Japan
Hanbo Cai: College of Computer and Information, Hohai University, Nanjing 211100, China
Zhaoqi Ma: Graduate School of Informatics, Osaka Metropolitan University, Sakai 559-8531, Osaka, Japan
Shuhuai Yu: Graduate School of Informatics, Osaka Metropolitan University, Sakai 559-8531, Osaka, Japan

Sustainability, 2024, vol. 16, issue 4, 1-23

Abstract: In the burgeoning field of sustainable energy, this research introduces a novel approach to accurate medium- and long-term load forecasting in large-scale power systems, a critical component for optimizing energy distribution and reducing environmental impacts. This study breaks new ground by integrating Causal Convolutional Neural Networks (Causal CNN) and Variational Autoencoders (VAE), among other advanced forecasting models, surpassing conventional methodologies in this domain. Methodologically, the power of these cutting-edge models is harnessed to assimilate and analyze a wide array of influential factors, including economic trends, demographic shifts, and natural phenomena. This approach enables a more nuanced and comprehensive understanding of power load dynamics, essential for accurate forecasting. The results demonstrate a remarkable improvement in forecasting accuracy, with a 15% increase in precision over traditional models. Additionally, the robustness of the forecasting under varying conditions showcases a significant advancement in predicting power loads more reliably. In conclusion, the findings not only contribute substantially to the field of load forecasting but also highlight the pivotal role of innovative methodologies in promoting sustainable energy practices. This work establishes a foundational framework for future research in sustainable energy systems, addressing the immediate challenges and exploring potential future avenues in large-scale power system management.

Keywords: large-scale power systems forecasting; sustainable energy management; integrated forecasting models; energy demand prediction (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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