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IETE Journal of Research Volume 69, 2023 - Issue 3
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Articles

Comparative Assessment of Regression Techniques for Wind Power Forecasting

Rachna Pathak1 Department of Instrumentation and control Engineering, Bharati Vidyapeeth’s College of Engineering (Affiliated to GGSIPU, Delhi), Delhi, IndiaView further author information
,
Arnav Wadhwa2 Department of Information Technology, Bharati Vidyapeeth’s College of Engineering (Affiliated to GGSIPU, Delhi), Delhi, IndiaView further author information
,
Poras Khetarpal1 Department of Instrumentation and control Engineering, Bharati Vidyapeeth’s College of Engineering (Affiliated to GGSIPU, Delhi), Delhi, IndiaView further author information
&
Neeraj Kumar3 Department of Electrical and Electronics Engineering, Bharati Vidyapeeth’s College of Engineering (Affiliated to GGSIPU, Delhi), New Delhi110 063, IndiaCorrespondence[email protected]
View further author information
Pages 1393-1402 | Published online: 13 Jan 2021

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Rahul Mahaseth, Neeraj Kumar, Aayush Aggarwal, Anshul Tayal, Amit Kumar & Rajat Gupta. (2022) Short term wind power forecasting using k-nearest neighbour (KNN). Journal of Information and Optimization Sciences 43:1, pages 251-259.
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Articles from other publishers (7)

Upma Singh & M. Rizwan. (2022) Analysis of wind turbine dataset and machine learning based forecasting in SCADA-system. Journal of Ambient Intelligence and Humanized Computing 14:6, pages 8035-8044.
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Anfeng Zhu, Qiancheng Zhao, Lin Gui, Tianlong Yang & Xuebing Yang. (2023) Operation State of the Wind Turbine Pitch System Based on Fuzzy Comprehensive Evaluation. Energy Engineering 120:2, pages 425-444.
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Adam Krechowicz, Maria Krechowicz & Katarzyna Poczeta. (2022) Machine Learning Approaches to Predict Electricity Production from Renewable Energy Sources. Energies 15:23, pages 9146.
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Saeed Salah, Husain R. Alsamamra & Jawad H. Shoqeir. (2022) Exploring Wind Speed for Energy Considerations in Eastern Jerusalem-Palestine Using Machine-Learning Algorithms. Energies 15:7, pages 2602.
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David A. Wood. (2022) Trend decomposition aids short-term countrywide wind capacity factor forecasting with machine and deep learning methods. Energy Conversion and Management 253, pages 115189.
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Fatih Demir & Burak Tasci. (2021) Predicting The Power of a Wind Turbine with Machine Learning-Based Approaches from Wind Direction and Speed Data. Predicting The Power of a Wind Turbine with Machine Learning-Based Approaches from Wind Direction and Speed Data.
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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