Machine learning model for wind direction and speed prediction

被引：0

作者：

Gowrishankar J. ^{[1
]}

Tamilselvan K. ^{[2
]}

Saravanan N.S. ^{[3
]}

Murali B. ^{[4
]}

机构：

[1] Department of Electrical and Electronics Engineering, Siddharth Institute of Engineering and Technology (Autonomous), Andhra Pradesh, Puttur

[2] Department of Electrical and Electronics Engineering, Government College of Engineering, Tamil Nadu, Erode

[3] Department of Electrical and Electronics Engineering, S.A. Engineering College, Chennai

[4] Department of Mechanical Engineering, Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology, Tamil Nadu, Avadi

来源：

International Journal of Power and Energy Conversion | 2024年 / 15卷 / 03期

关键词：

input/output variables; linear vs. nonlinear model; machine learning model;

D O I：

10.1504/IJPEC.2024.140021

中图分类号：

学科分类号：

摘要：

The capacity to estimate wind direction and speed is essential for both the generation of renewable energy and the forecasting of weather. Near the ground, the performance of the mechanistic models that are the foundation of conventional forecasting is quite low. We will explore a different data-driven strategy that is based on supervised learning. We train supervised learning algorithms utilising the previous history of wind data. We use data from individual locations and horizons to conduct a systematic comparison of a number of algorithms, during which we change the input/output variables, the amount of memory, and whether or not the model is linear or nonlinear. According to our findings, the ideal design as well as the performance of the system varies depending on the region. Our technique achieves an improvement in performance of 0.3 m/s on average when it is applied to datasets that are accessible to the public. © 2024 Inderscience Enterprises Ltd.

引用

页码：208 / 219

页数：11

共 24 条

[1]

Alkesaiberi A., Harrou F., Sun Y., Efficient wind power prediction using machine learning methods: a comparative study, Energies, 15, (2022)

[2]

De Giorgi M.G., Campilongo S., Ficarella A., Congedo P., Comparison between wind power prediction models based on wavelet decomposition with least-squares support vector machine (LS-SVM) and artificial neural network (ANN), Energies, 7, 3, pp. 5251-5272, (2014)

[3]

Engel H., Hensley R., Knupfer S., Sahdev S., The Potential Impact of Electric Vehicles on Global Energy Systems, (2018)

[4]

Renewable Energy Directive: Cooperation Mechanisms, (2013)

[5]

Climate Strategies and Targets: 2030 Climate & Energy Framework, (2020)

[6]

Directive (EU) 2018/2001 of the European Parliament and of the Council of 11 December 2018 on the Promotion of the Use of Energy from Renewable Sources, (2018)

[7]

Directive (EU) 2018/844 of the European Parliament and of the Council of 30 May 2018 Amending Directive 2010/31/EU on the Energy Performance of Buildings and Directive 2012/27/EU on Energy Efficiency, (2018)

[8]

Evans R., Gao J., DeepMind AI Reduces Google Data Centre Cooling Bill by 40%, (2016)

[9]

Flores P., Tapia A., Tapia G., Application of a control algorithm for wind speed prediction and active power generation, Renewable Energy, 30, pp. 523-536, (2005)

[10]

He D., Liu R., Ultra-short-term wind power prediction using ANN ensemble based on PCA, Proceedings of the 7th International Power Electronics and Motion Control Conference, pp. 2108-2112, (2012)

← 1 2 3 →