Prediction of Hydropower Generation Using Grey Wolf Optimization Adaptive Neuro-Fuzzy Inference System

Energies

Hossein Riahi-Madvar¹  Shahaboddin Shamshirband²  Kwok-wing Chau³  Amir Mosavi⁴  Edmundas Kazimieras Zavadskas⁵  Majid Dehghani⁶  Farhad Hooshyaripor⁷ 

[1] College of Agriculture, Vali-e-Asr University of Rafsanjan, P.O. Box 518, 7718897111 Rafsanjan, Iran;Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Viet Nam;Department of Civil and Environmental Engineering, Hong Kong Polytechnic University, Hong Kong, China;Institute of Automation, Kando Kalman Faculty of Electrical Engineering, Obuda University, 1034 Budapest, Hungary;Institute of Sustainable Construction, Vilnius Gediminas Technical University, LT-10223 Vilnius, Lithuania;Technical and Engineering Department, Faculty of Civil Engineering, Vali-e-Asr University of Rafsanjan, P.O. Box 518, 7718897111 Rafsanjan, Iran;Technical and Engineering Department, Science and Research, Branch, Islamic Azad University, 1477893855, Tehran, Iran;

关键词: hydropower generation;    hydropower prediction;    dam inflow;    machine learning;    hybrid models;    artificial intelligence;    prediction;    grey wolf optimization (GWO), deep learning;    adaptive neuro-fuzzy inference system (ANFIS), hydrological modelling;    hydroinformatics;    energy system;    drought;    forecasting;    precipitation;   

DOI  :  10.3390/en12020289

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown