Long short-term memory (LSTM) neural network and adaptive neuro-fuzzy inference system (ANFIS) approach in modeling renewable electricity generation forecasting-Reference-Cited by-同舟云学术

Long short-term memory (LSTM) neural network and adaptive neuro-fuzzy inference system (ANFIS) approach in modeling renewable electricity generation forecasting

Published:2020-12-29 Issue:6 Volume:18 Page:578-594
ISSN:1543-5075
Container-title:International Journal of Green Energy
language:en
Short-container-title:International Journal of Green Energy

Author:

Bilgili Mehmet¹^ORCID,Yildirim Alper²^ORCID,Ozbek Arif¹^ORCID,Celebi Kerimcan¹^ORCID,Ekinci Firat³^ORCID

Affiliation:

1. Department of Mechanical Engineering, Ceyhan Engineering Faculty, Cukurova University, Adana, Turkey

2. Department of Machinery and Metal Technology, Osmaniye Korkut Ata University, Osmaniye, Turkey

3. Department of Energy System Engineering, Faculty of Engineering, Adana Alparslan Turkes Science and Technology University, Adana, Turkey

Funder

the Office of Scientific Research of Cukurova University

Publisher

Informa UK Limited

Subject

Renewable Energy, Sustainability and the Environment

Link

https://www.tandfonline.com/doi/pdf/10.1080/15435075.2020.1865375

Reference63 articles.

1. Performance Evaluation of ANN and ANFIS Models for Estimating Garlic Crop Evapotranspiration

2. Daily electrical energy consumption: Periodicity, harmonic regression method and forecasting

3. Long-term electrical energy consumption forecasting for developing and developed economies based on different optimized models and historical data types

4. Electricity consumption forecasting for Turkey with nonhomogeneous discrete grey model

5. Deep learning framework to forecast electricity demand

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