sis 20(25): e1

Research Article

Comparative Analysis of Wind Speed Forecasting Using LSTM and SVM

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  • @ARTICLE{10.4108/eai.13-7-2018.159407,
        author={Satyam Gangwar and Vikram Bali and Ajay Kumar},
        title={Comparative Analysis of Wind Speed Forecasting Using LSTM and SVM},
        journal={EAI Endorsed Transactions on Scalable Information Systems},
        volume={7},
        number={25},
        publisher={EAI},
        journal_a={SIS},
        year={2019},
        month={7},
        keywords={long short term memory, support-vector machine, root mean square error, wind forecasting},
        doi={10.4108/eai.13-7-2018.159407}
    }
    
  • Satyam Gangwar
    Vikram Bali
    Ajay Kumar
    Year: 2019
    Comparative Analysis of Wind Speed Forecasting Using LSTM and SVM
    SIS
    EAI
    DOI: 10.4108/eai.13-7-2018.159407
Satyam Gangwar1, Vikram Bali1, Ajay Kumar1
  • 1: Department of Computer Science & Engineering, JSS Academy of Technical Education, Noida, India

Abstract

The objective of this work is to present a comprehensive exploration of deep learning based wind forecasting model. The forecasting of speed of wind is called as the wind speed forecasting/prediction. It is basically done to achieve the better sustainability for power generation and production. The availability of wind energy in ample amount makes it quite comfortable to be utilized for various functionalities. In this research work the main aim is to forecast speed using LSTM including certain parameters and then comparative analysis is done using SVM. Both are machine learning approaches but have different functionalities in comparison to each other. This comparison is done to obtain the better technique which can be further applied on larger datasets to design a better, accurate, efficient forecasting model for speed of wind. The survey and implementation of both the techniques gave a clear idea about the utilisation of long short term memory for the better and enhanced wind speed forecasting. The forecasting is based on various atmospheric variables, and the data set is taken from the kaggle datsets which have numerous attributes but we have considered few of them only for the prediction purpose.