Reservoir Inflow Prediction System Based on Interval Type-2 Fuzzy Logic †

Hao Han Tsao; Meng Wei Chen; Yi Hsiang Tseng; Yih Guang Leu

doi:10.3390/engproc2025120072

Reservoir Inflow Prediction System Based on Interval Type-2 Fuzzy Logic †

Hao Han Tsao
, Meng Wei Chen
, Yi Hsiang Tseng
, Yih Guang Leu^*

^*此作品的通信作者

電機工程學系

研究成果: 雜誌貢獻 › 期刊論文 › 同行評審

摘要

Due to its fast start and stop, purity, and reliability, hydropower is becoming more important in the overall power dispatch strategy in grids with a high proportion of wind and solar power generation. Therefore, we propose an interval type-2 fuzzy logic-based rainfall classification and fuzzy neural network model to build a 48 h reservoir inflow forecasting system, addressing the challenges of renewable energy instability and extreme weather in hydropower operations.

原文	英語
文章編號	72
期刊	Engineering Proceedings
卷	120
發行號	1
DOIs	https://doi.org/10.3390/engproc2025120072
出版狀態	已發佈 - 2026

UN SDG

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ASJC Scopus subject areas

生物醫學工程
機械工業
工業與製造工程
電氣與電子工程

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10.3390/engproc2025120072

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abstract = "Due to its fast start and stop, purity, and reliability, hydropower is becoming more important in the overall power dispatch strategy in grids with a high proportion of wind and solar power generation. Therefore, we propose an interval type-2 fuzzy logic-based rainfall classification and fuzzy neural network model to build a 48 h reservoir inflow forecasting system, addressing the challenges of renewable energy instability and extreme weather in hydropower operations.",

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AU - Tsao, Hao Han

AU - Chen, Meng Wei

AU - Tseng, Yi Hsiang

AU - Leu, Yih Guang

PY - 2026

Y1 - 2026

N2 - Due to its fast start and stop, purity, and reliability, hydropower is becoming more important in the overall power dispatch strategy in grids with a high proportion of wind and solar power generation. Therefore, we propose an interval type-2 fuzzy logic-based rainfall classification and fuzzy neural network model to build a 48 h reservoir inflow forecasting system, addressing the challenges of renewable energy instability and extreme weather in hydropower operations.

AB - Due to its fast start and stop, purity, and reliability, hydropower is becoming more important in the overall power dispatch strategy in grids with a high proportion of wind and solar power generation. Therefore, we propose an interval type-2 fuzzy logic-based rainfall classification and fuzzy neural network model to build a 48 h reservoir inflow forecasting system, addressing the challenges of renewable energy instability and extreme weather in hydropower operations.

KW - fuzzy neural network

KW - interval type-2 fuzzy logic

KW - prediction

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Reservoir Inflow Prediction System Based on Interval Type-2 Fuzzy Logic †

摘要

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ASJC Scopus subject areas

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