This paper is published in Volume-4, Issue-2, 2018
Area
Machine Learning
Author
Pooja Varshini R
Co-authors
Farzana Begum S, Saranya M, Dr. B. Vanathi
Org/Univ
Valliammai Engineering College, Kanchipuram, Tamil Nadu, India
Pub. Date
12 April, 2018
Paper ID
V4I2-1843
Publisher
Keywords
Tree Ensemble, XGBoost, RMSE, Apache Spark.

Citationsacebook

IEEE
Pooja Varshini R, Farzana Begum S, Saranya M, Dr. B. Vanathi. Wind speed prediction using tree ensemble, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Pooja Varshini R, Farzana Begum S, Saranya M, Dr. B. Vanathi (2018). Wind speed prediction using tree ensemble. International Journal of Advance Research, Ideas and Innovations in Technology, 4(2) www.IJARIIT.com.

MLA
Pooja Varshini R, Farzana Begum S, Saranya M, Dr. B. Vanathi. "Wind speed prediction using tree ensemble." International Journal of Advance Research, Ideas and Innovations in Technology 4.2 (2018). www.IJARIIT.com.

Abstract

The wind energy has emerged as one of the safest growing renewable energy to address the crisis witnessed in power generation. Wind speed is an important factor in wind power production and integration. However, the complex nature of wind speed limits the dependability and induces high fluctuation in power generation. The accurate prediction of wind speed energy with minimum accepted errors will increase to harness the energy content in a wind efficiently. In recent years, machine learning algorithms are used to analyze and predict data to make better decisions. Ensemble model is one of the supervised machine learning approaches to predict numerical data. In this project, the speed of wind is predicted through XGBoost 4j package in a distributed programming environment of Apache Spark. XGBOOST is a short form of Extreme Gradient Boosting tool of supervised machine learning, where the training data set xi is used to predict a target variable Yi. The results are compared with different iterations in order to minimize the uncertainties and to evaluate the efficiency and accuracy. The main findings were that Ensemble model was the most accurate method.
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