Short term forecasting based on hourly wind speed data using deep learning algorithms

Vikash Kumar Saini; Rajesh Kumar; Akhilesh Mathur; Akash Saxena

doi:10.1109/ICETCE48199.2020.9091757

Short term forecasting based on hourly wind speed data using deep learning algorithms

Vikash Kumar Saini
, Rajesh Kumar
, Akhilesh Mathur
, Akash Saxena

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

40 Citations (Scopus)

Abstract

Application of Internet of Things (IoT) in smart grid is evident in current trends. Smart grid management have greater impact on market economics, security, and distribution of energy. Smart grid is an integration of several components like, wind, solar, cyber-security etc. One of major concern in smart grid is optimal control of wind generation and accurate prediction of wind speed. This paper aims to predict the wind speed with meteorological time series data as input variable using deep learning topology for one-year wind speed data. The dynamic recurrent type network (RNN) integrates and processed with the Extreme Learning-Machine (ELM), nonlinear autoregressive network with exogenous inputs (NARX), and Long short-term memory (LSTM) model. Three models having the same Network's architecture, intermediate layer in architecture have 19 neurons and an activation function. Feature selection method is used for feature extraction from wind data (have four features as wind speed, pressure, humidity, air temperature) and applied to models. Comparative analysis of different models are assessed by performance matrices such as MAPE, MAE, and RMSE.

Original language	English
Title of host publication	Proceedings of 3rd International Conference on Emerging Technologies in Computer Engineering
Subtitle of host publication	Machine Learning and Internet of Things, ICETCE 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	30-35
Number of pages	6
ISBN (Electronic)	9781728116839
DOIs	https://doi.org/10.1109/ICETCE48199.2020.9091757
Publication status	Published - Feb 2020
Externally published	Yes
Event	3rd International Conference on Emerging Technologies in Computer Engineering: Machine Learning and Internet of Things, ICETCE 2020 - Jaipur, India Duration: 7 Feb 2020 → 8 Feb 2020

Publication series

Name	Proceedings of 3rd International Conference on Emerging Technologies in Computer Engineering: Machine Learning and Internet of Things, ICETCE 2020

Conference

Conference	3rd International Conference on Emerging Technologies in Computer Engineering: Machine Learning and Internet of Things, ICETCE 2020
Country/Territory	India
City	Jaipur
Period	7/02/20 → 8/02/20

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Deep Learning
LSTM
RNN
Wind Forecasting

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications
Computer Science Applications
Computer Vision and Pattern Recognition
Hardware and Architecture

Access to Document

10.1109/ICETCE48199.2020.9091757

Cite this

Saini, V. K., Kumar, R., Mathur, A., & Saxena, A. (2020). Short term forecasting based on hourly wind speed data using deep learning algorithms. In Proceedings of 3rd International Conference on Emerging Technologies in Computer Engineering: Machine Learning and Internet of Things, ICETCE 2020 (pp. 30-35). Article 9091757 (Proceedings of 3rd International Conference on Emerging Technologies in Computer Engineering: Machine Learning and Internet of Things, ICETCE 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICETCE48199.2020.9091757

Saini, Vikash Kumar ; Kumar, Rajesh ; Mathur, Akhilesh et al. / Short term forecasting based on hourly wind speed data using deep learning algorithms. Proceedings of 3rd International Conference on Emerging Technologies in Computer Engineering: Machine Learning and Internet of Things, ICETCE 2020. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 30-35 (Proceedings of 3rd International Conference on Emerging Technologies in Computer Engineering: Machine Learning and Internet of Things, ICETCE 2020).

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title = "Short term forecasting based on hourly wind speed data using deep learning algorithms",

abstract = "Application of Internet of Things (IoT) in smart grid is evident in current trends. Smart grid management have greater impact on market economics, security, and distribution of energy. Smart grid is an integration of several components like, wind, solar, cyber-security etc. One of major concern in smart grid is optimal control of wind generation and accurate prediction of wind speed. This paper aims to predict the wind speed with meteorological time series data as input variable using deep learning topology for one-year wind speed data. The dynamic recurrent type network (RNN) integrates and processed with the Extreme Learning-Machine (ELM), nonlinear autoregressive network with exogenous inputs (NARX), and Long short-term memory (LSTM) model. Three models having the same Network's architecture, intermediate layer in architecture have 19 neurons and an activation function. Feature selection method is used for feature extraction from wind data (have four features as wind speed, pressure, humidity, air temperature) and applied to models. Comparative analysis of different models are assessed by performance matrices such as MAPE, MAE, and RMSE.",

keywords = "Deep Learning, LSTM, RNN, Wind Forecasting",

author = "Saini, \{Vikash Kumar\} and Rajesh Kumar and Akhilesh Mathur and Akash Saxena",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 3rd International Conference on Emerging Technologies in Computer Engineering: Machine Learning and Internet of Things, ICETCE 2020 ; Conference date: 07-02-2020 Through 08-02-2020",

year = "2020",

month = feb,

doi = "10.1109/ICETCE48199.2020.9091757",

language = "English",

series = "Proceedings of 3rd International Conference on Emerging Technologies in Computer Engineering: Machine Learning and Internet of Things, ICETCE 2020",

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}

Saini, VK, Kumar, R, Mathur, A & Saxena, A 2020, Short term forecasting based on hourly wind speed data using deep learning algorithms. in Proceedings of 3rd International Conference on Emerging Technologies in Computer Engineering: Machine Learning and Internet of Things, ICETCE 2020., 9091757, Proceedings of 3rd International Conference on Emerging Technologies in Computer Engineering: Machine Learning and Internet of Things, ICETCE 2020, Institute of Electrical and Electronics Engineers Inc., pp. 30-35, 3rd International Conference on Emerging Technologies in Computer Engineering: Machine Learning and Internet of Things, ICETCE 2020, Jaipur, India, 7/02/20. https://doi.org/10.1109/ICETCE48199.2020.9091757

Short term forecasting based on hourly wind speed data using deep learning algorithms. / Saini, Vikash Kumar; Kumar, Rajesh; Mathur, Akhilesh et al.
Proceedings of 3rd International Conference on Emerging Technologies in Computer Engineering: Machine Learning and Internet of Things, ICETCE 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 30-35 9091757 (Proceedings of 3rd International Conference on Emerging Technologies in Computer Engineering: Machine Learning and Internet of Things, ICETCE 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Short term forecasting based on hourly wind speed data using deep learning algorithms

AU - Saini, Vikash Kumar

AU - Kumar, Rajesh

AU - Mathur, Akhilesh

AU - Saxena, Akash

PY - 2020/2

Y1 - 2020/2

N2 - Application of Internet of Things (IoT) in smart grid is evident in current trends. Smart grid management have greater impact on market economics, security, and distribution of energy. Smart grid is an integration of several components like, wind, solar, cyber-security etc. One of major concern in smart grid is optimal control of wind generation and accurate prediction of wind speed. This paper aims to predict the wind speed with meteorological time series data as input variable using deep learning topology for one-year wind speed data. The dynamic recurrent type network (RNN) integrates and processed with the Extreme Learning-Machine (ELM), nonlinear autoregressive network with exogenous inputs (NARX), and Long short-term memory (LSTM) model. Three models having the same Network's architecture, intermediate layer in architecture have 19 neurons and an activation function. Feature selection method is used for feature extraction from wind data (have four features as wind speed, pressure, humidity, air temperature) and applied to models. Comparative analysis of different models are assessed by performance matrices such as MAPE, MAE, and RMSE.

AB - Application of Internet of Things (IoT) in smart grid is evident in current trends. Smart grid management have greater impact on market economics, security, and distribution of energy. Smart grid is an integration of several components like, wind, solar, cyber-security etc. One of major concern in smart grid is optimal control of wind generation and accurate prediction of wind speed. This paper aims to predict the wind speed with meteorological time series data as input variable using deep learning topology for one-year wind speed data. The dynamic recurrent type network (RNN) integrates and processed with the Extreme Learning-Machine (ELM), nonlinear autoregressive network with exogenous inputs (NARX), and Long short-term memory (LSTM) model. Three models having the same Network's architecture, intermediate layer in architecture have 19 neurons and an activation function. Feature selection method is used for feature extraction from wind data (have four features as wind speed, pressure, humidity, air temperature) and applied to models. Comparative analysis of different models are assessed by performance matrices such as MAPE, MAE, and RMSE.

KW - Deep Learning

KW - LSTM

KW - RNN

KW - Wind Forecasting

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DO - 10.1109/ICETCE48199.2020.9091757

M3 - Conference contribution

AN - SCOPUS:85085547441

T3 - Proceedings of 3rd International Conference on Emerging Technologies in Computer Engineering: Machine Learning and Internet of Things, ICETCE 2020

SP - 30

EP - 35

BT - Proceedings of 3rd International Conference on Emerging Technologies in Computer Engineering

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 3rd International Conference on Emerging Technologies in Computer Engineering: Machine Learning and Internet of Things, ICETCE 2020

Y2 - 7 February 2020 through 8 February 2020

ER -

Saini VK, Kumar R, Mathur A, Saxena A. Short term forecasting based on hourly wind speed data using deep learning algorithms. In Proceedings of 3rd International Conference on Emerging Technologies in Computer Engineering: Machine Learning and Internet of Things, ICETCE 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 30-35. 9091757. (Proceedings of 3rd International Conference on Emerging Technologies in Computer Engineering: Machine Learning and Internet of Things, ICETCE 2020). doi: 10.1109/ICETCE48199.2020.9091757

Short term forecasting based on hourly wind speed data using deep learning algorithms

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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