Real-Time Load Forecasting and Adaptive Control in Smart Grids Using a Hybrid Neuro-Fuzzy Approach

Author:

Wang Fangzong1,Nishtar Zuhaib1

Affiliation:

1. College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China

Abstract

The transition to smart grids is revolutionizing the management and distribution of electrical energy. Nowadays, power systems must precisely estimate real-time loads and use adaptive regulation to operate in the era of sustainable energy. To address these issues, this paper presents a new approach—a hybrid neuro-fuzzy system—that combines neural networks with fuzzy logic. We use neural networks’ adaptability to describe complex load patterns and fuzzy logic’s interpretability to fine-tune control techniques in our approach. Our improved load forecasting system can now respond to changes in real-time due to the combination of these two powerful methodologies. Developing, training, and implementing the forecasting and control system are detailed in this article, which also explores the theoretical underpinnings of our hybrid neuro-fuzzy approach. We demonstrate how the technology improves grid stability and the accuracy of load forecasts by using adaptive control methods. Furthermore, comprehensive simulations confirm the proposed technology, showcasing its smooth integration with smart grid infrastructure. Better energy management is just the beginning of what our method can accomplish; it also paves the way for a more sustainable energy future that is easier on the planet and its inhabitants. In conclusion, this study’s innovative approach to adaptive control and real-time load forecasting advances smart grid technology, which, in turn, improves sustainability and energy efficiency.

Publisher

MDPI AG

Reference30 articles.

1. Load Forecasting Techniques for Power System: Research Challenges and Survey;Ahmad;IEEE Access,2022

2. A Short-Term Load Forecasting Model Based on Self-Adaptive Momentum Factor and Wavelet Neural Network in Smart Grid;Zulfiqar;IEEE Access,2022

3. Review of Short-Term Load Forecasting for Smart Grids Using Deep Neural Networks and Metaheuristic Methods;Islam;Math. Probl. Eng.,2022

4. Short-term load forecasting in smart grids using artificial intelligence methods: A survey;Salehimehr;J. Eng.,2022

5. Dewangan, F., Abdelaziz, A.Y., and Biswal, M. (2023). Load Forecasting Models in Smart Grid Using Smart Meter Information: A Review. Energies, 16.

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3