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Journal of Statistics and Management Systems Volume 23, 2020 - Issue 1: Developments and Advances in Renewable Energy systems
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Articles

Short term wind power forecasting using machine learning techniques

Aditya Chaudhary1 Department of Electrical & Electronics Engineering, Bharati Vidyapeeth’s College of Engineering, Paschim Vihar, New Delhi110063, IndiaCorrespondence[email protected]
,
Akash Sharma2 Department of Electrical & Electronics Engineering, Bharati Vidyapeeth’s College of Engineering, Paschim Vihar, New Delhi110063, India, E-mail: [email protected]
,
Ayush Kumar3 Department of Electrical & Electronics Engineering, Bharati Vidyapeeth’s College of Engineering, Paschim Vihar, New Delhi110063, India, E-mail: [email protected]
,
Karan Dikshit4 Department of Electrical & Electronics Engineering, Bharati Vidyapeeth’s College of Engineering, Paschim Vihar, New Delhi110063, India, E-mail: [email protected]
&
Neeraj Kumar5 Department of Electrical & Electronics Engineering, Bharati Vidyapeeth’s College of Engineering, Paschim Vihar, New Delhi110063, India, E-mail: [email protected]
Pages 145-156 | Published online: 06 Feb 2020

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Pulavarthi Satya Venkata Kishore, Jami Rajesh, Nakka Jayaram & Sukanta Halder. (2022) A Survey of Machine Learning Applications in Renewable Energy Sources. IETE Journal of Research 0:0, pages 1-18.
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Abhinav Saxena, Amit Kumar Sharma & Mohd Majid. (2022) A robust controlling and management of load with minimum frequency and voltage deviation in network employing genetic algorithm. Journal of Statistics and Management Systems 25:7, pages 1531-1540.
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Gyana Ranjan Patra & Mihir Narayan Mohanty. (2022) An LSTM-GRU based hybrid framework for secured stock price prediction. Journal of Statistics and Management Systems 25:6, pages 1491-1499.
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Aliva Routray, Khyati D Mistry, Sabha Raj Arya & B. Chittibabu. (2020) Applied machine learning in wind speed prediction and loss minimization in unbalanced radial distribution system. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 0:0, pages 1-21.
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Articles from other publishers (11)

Tacjana Niksa-Rynkiewicz, Piotr Stomma, Anna Witkowska, Danuta Rutkowska, Adam Słowik, Krzysztof Cpałka, Joanna Jaworek-Korjakowska & Piotr Kolendo. (2023) An Intelligent Approach to Short-Term Wind Power Prediction Using Deep Neural Networks. Journal of Artificial Intelligence and Soft Computing Research 13:3, pages 197-210.
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Quanhui Li, Ji Lv, Min Ding, Danyun Li & Zhijian Fang. (2023) A Short-term Wind Power Forecasting Method Based on NWP Wind Speed Fluctuation Division and Clustering. A Short-term Wind Power Forecasting Method Based on NWP Wind Speed Fluctuation Division and Clustering.
Seyed Matin Malakouti. (2022) Use machine learning algorithms to predict turbine power generation to replace renewable energy with fossil fuels. Energy Exploration & Exploitation 41:2, pages 836-857.
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Amaris Dalton & Bernard Bekker. (2022) Exogenous atmospheric variables as wind speed predictors in machine learning. Applied Energy 319, pages 119257.
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Antonio Lorenzo-Espejo, Alejandro Escudero-Santana, María-Luisa Muñoz-Díaz & Alicia Robles-Velasco. (2022) Machine Learning-Based Analysis of a Wind Turbine Manufacturing Operation: A Case Study. Sustainability 14:13, pages 7779.
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Arun Kumar Nayak, Kailash Chand Sharma, Rohit Bhakar & Harpal Tiwari. (2022) Effect of High-Resolution Data Input on Wind Speed Prediction using Machine Learning Algorithms. Effect of High-Resolution Data Input on Wind Speed Prediction using Machine Learning Algorithms.
David A. Wood. (2022) Feature averaging of historical meteorological data with machine and deep learning assist wind farm power performance analysis and forecasts. Energy Systems.
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P.S.V. Kishore, Jami Rajesh, Sukanta Halder & Nakka Jayaram. 2022. Smart Electrical and Mechanical Systems. Smart Electrical and Mechanical Systems 123 149 .
Upma Singh, Mohammad Rizwan, Muhannad Alaraj & Ibrahim Alsaidan. (2021) A Machine Learning-Based Gradient Boosting Regression Approach for Wind Power Production Forecasting: A Step towards Smart Grid Environments. Energies 14:16, pages 5196.
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Muhammad Ahsan Zamee & Dongjun Won. (2020) Novel Mode Adaptive Artificial Neural Network for Dynamic Learning: Application in Renewable Energy Sources Power Generation Prediction. Energies 13:23, pages 6405.
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Lin Ye, Jinlong Zhao, Peng Lu, Ming Pei, Mei Chen & Bo Wang. (2020) Combined Approach for Short-Term Wind Power Forecasting Considering Meteorological Fluctuation and Feature Extraction. Combined Approach for Short-Term Wind Power Forecasting Considering Meteorological Fluctuation and Feature Extraction.

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