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^*

^*Corresponding author for this work

Department of Electrical Engineering

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Article number	72
Journal	Engineering Proceedings
Volume	120
Issue number	1
DOIs	https://doi.org/10.3390/engproc2025120072
Publication status	Published - 2026

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

fuzzy neural network
interval type-2 fuzzy logic
prediction

ASJC Scopus subject areas

Biomedical Engineering
Mechanical Engineering
Industrial and Manufacturing Engineering
Electrical and Electronic Engineering

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10.3390/engproc2025120072

Cite this

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title = "Reservoir Inflow Prediction System Based on Interval Type-2 Fuzzy Logic †",

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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author = "Tsao, \{Hao Han\} and Chen, \{Meng Wei\} and Tseng, \{Yi Hsiang\} and Leu, \{Yih Guang\}",

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doi = "10.3390/engproc2025120072",

language = "English",

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journal = "Engineering Proceedings",

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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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UR - https://www.scopus.com/pages/publications/105043101428#tab=citedBy

U2 - 10.3390/engproc2025120072

DO - 10.3390/engproc2025120072

M3 - Article

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JO - Engineering Proceedings

JF - Engineering Proceedings

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M1 - 72

ER -

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

Abstract

UN SDGs

Keywords

ASJC Scopus subject areas

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