Prediction of hydropower generation using Grey wolf optimization adaptive neuro-fuzzy inference system

Majid Dehghani; Hossein Riahi-Madvar; Farhad Hooshyaripor; Amir Mosavi; Shahaboddin Shamshirband; Edmundas Kazimieras Zavadskas; Kwok wing Chau

doi:10.3390/en12020289

Prediction of hydropower generation using Grey wolf optimization adaptive neuro-fuzzy inference system

Majid Dehghani, Hossein Riahi-Madvar, Farhad Hooshyaripor, Amir Mosavi, Shahaboddin Shamshirband, Edmundas Kazimieras Zavadskas, Kwok wing Chau

Research output: Journal article publication › Journal article › Academic research › peer-review

169 Citations (Scopus)

Abstract

Hydropower is among the cleanest sources of energy. However, the rate of hydropower generation is profoundly affected by the inflow to the dam reservoirs. In this study, the Grey wolf optimization (GWO) method coupled with an adaptive neuro-fuzzy inference system (ANFIS) to forecast the hydropower generation. For this purpose, the Dez basin average of rainfall was calculated using Thiessen polygons. Twenty input combinations, including the inflow to the dam, the rainfall and the hydropower in the previous months were used, while the output in all the scenarios was one month of hydropower generation. Then, the coupled model was used to forecast the hydropower generation. Results indicated that the method was promising. GWO-ANFIS was capable of predicting the hydropower generation satisfactorily, while the ANFIS failed in nine input-output combinations.

Original language	English
Article number	289
Journal	Energies
Volume	12
Issue number	2
DOIs	https://doi.org/10.3390/en12020289
Publication status	Published - 17 Jan 2019

Keywords

Adaptive neuro-fuzzy inference system (ANFIS)
Artificial intelligence
Dam inflow
Deep learning
Drought
Energy system
Forecasting
Grey Wolf optimization (GWO)
Hybrid models
Hydroinformatics
Hydrological modelling
Hydropower generation
Hydropower prediction
Machine learning
Precipitation
Prediction

ASJC Scopus subject areas

Renewable Energy, Sustainability and the Environment
Energy Engineering and Power Technology
Energy (miscellaneous)
Control and Optimization
Electrical and Electronic Engineering

UN SDGs

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

More information

10.3390/en12020289

http://hdl.handle.net/10397/80674

Cite this

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abstract = "Hydropower is among the cleanest sources of energy. However, the rate of hydropower generation is profoundly affected by the inflow to the dam reservoirs. In this study, the Grey wolf optimization (GWO) method coupled with an adaptive neuro-fuzzy inference system (ANFIS) to forecast the hydropower generation. For this purpose, the Dez basin average of rainfall was calculated using Thiessen polygons. Twenty input combinations, including the inflow to the dam, the rainfall and the hydropower in the previous months were used, while the output in all the scenarios was one month of hydropower generation. Then, the coupled model was used to forecast the hydropower generation. Results indicated that the method was promising. GWO-ANFIS was capable of predicting the hydropower generation satisfactorily, while the ANFIS failed in nine input-output combinations.",

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author = "Majid Dehghani and Hossein Riahi-Madvar and Farhad Hooshyaripor and Amir Mosavi and Shahaboddin Shamshirband and Zavadskas, {Edmundas Kazimieras} and Chau, {Kwok wing}",

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AU - Dehghani, Majid

AU - Riahi-Madvar, Hossein

AU - Hooshyaripor, Farhad

AU - Mosavi, Amir

AU - Shamshirband, Shahaboddin

AU - Zavadskas, Edmundas Kazimieras

AU - Chau, Kwok wing

PY - 2019/1/17

Y1 - 2019/1/17

N2 - Hydropower is among the cleanest sources of energy. However, the rate of hydropower generation is profoundly affected by the inflow to the dam reservoirs. In this study, the Grey wolf optimization (GWO) method coupled with an adaptive neuro-fuzzy inference system (ANFIS) to forecast the hydropower generation. For this purpose, the Dez basin average of rainfall was calculated using Thiessen polygons. Twenty input combinations, including the inflow to the dam, the rainfall and the hydropower in the previous months were used, while the output in all the scenarios was one month of hydropower generation. Then, the coupled model was used to forecast the hydropower generation. Results indicated that the method was promising. GWO-ANFIS was capable of predicting the hydropower generation satisfactorily, while the ANFIS failed in nine input-output combinations.

AB - Hydropower is among the cleanest sources of energy. However, the rate of hydropower generation is profoundly affected by the inflow to the dam reservoirs. In this study, the Grey wolf optimization (GWO) method coupled with an adaptive neuro-fuzzy inference system (ANFIS) to forecast the hydropower generation. For this purpose, the Dez basin average of rainfall was calculated using Thiessen polygons. Twenty input combinations, including the inflow to the dam, the rainfall and the hydropower in the previous months were used, while the output in all the scenarios was one month of hydropower generation. Then, the coupled model was used to forecast the hydropower generation. Results indicated that the method was promising. GWO-ANFIS was capable of predicting the hydropower generation satisfactorily, while the ANFIS failed in nine input-output combinations.

KW - Adaptive neuro-fuzzy inference system (ANFIS)

KW - Artificial intelligence

KW - Dam inflow

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KW - Drought

KW - Energy system

KW - Forecasting

KW - Grey Wolf optimization (GWO)

KW - Hybrid models

KW - Hydroinformatics

KW - Hydrological modelling

KW - Hydropower generation

KW - Hydropower prediction

KW - Machine learning

KW - Precipitation

KW - Prediction

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Prediction of hydropower generation using Grey wolf optimization adaptive neuro-fuzzy inference system

Abstract

Keywords

ASJC Scopus subject areas

UN SDGs

More information

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