Short‐term wind speed forecasting using S‐transform with compactly supported kernel

Kamath, Priya R.; Senapati, Kedarnath

doi:10.1002/we.2571

Cited by 4 publications

(2 citation statements)

References 41 publications

(69 reference statements)

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“…Wind direction of AMeDAS is divided into 16 as shown in Figure 2. Equations (1) and (2) show x and y components of wind vector,…”

Section: Explanation Of Wind Speed and Directionmentioning

confidence: 99%

“…Wind power generation contributed around 28% of new energy additions in the world in 2018. The advantage of wind power is being clean, reliable, most abundant, and affordable as part of the renewable energy resources, with having the potential for supply energy and being able to increase producing energy, significantly in the future [1,2]. However, the principal problem of wind power is the wide fluctuation of output caused by a change in wind speed and direction.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Deep convolutional long short-term memory for forecasting wind speed and direction

Sari

Suzuki

Komamizu

et al. 2021

SICE Journal of Control, Measurement, and System Integration

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This paper proposed deep learning to create an accurate forecasting system that uses a deep convolutional long short-term memory (DCLSTM) for forecasting wind speed and direction. In order to use the DCLSTM system, wind speed and direction are represented as an image in 2D coordinates and make it to time sequence data. The wind speed and direction data were obtained from AMeDAS (Automated Meteorological Data Acquisition System), Japan. The target of the proposed forecasting system was to improve forecasting accuracy compared to the system in SICE 2020 (The Society of Instrument and Control Engineers Annual Conference 2020) in all seasons. For verifying the efficiency of the forecasting system by comparison with persistent system, deep fully connected-LSTM (DFC-LSTM) and encoding-forecasting network with convolutional long short-term memory (CLSTM) systems were investigated. Forecasting performance of the system was evaluated by RMSE (root mean square error) between forecasted and measured data.

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“…Wind direction of AMeDAS is divided into 16 as shown in Figure 2. Equations (1) and (2) show x and y components of wind vector,…”

Section: Explanation Of Wind Speed and Directionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep convolutional long short-term memory for forecasting wind speed and direction

Sari

Suzuki

Komamizu

et al. 2021

SICE Journal of Control, Measurement, and System Integration

View full text Add to dashboard Cite

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A novel hybrid framework for wind speed forecasting using autoencoder‐based convolutional long short‐term memory network

Kosana

Madasthu

Teeparthi

2021

Int Trans Electr Energ Syst

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Summary A precise forecast of wind speed is a fundamental requirement of wind power integration. The nonlinear and intermittent nature of the wind makes wind speed forecasting (WSF) complicated for linear approaches. Addressing the complications faced by the linear approaches, this paper proposed a novel and robust approach using long short‐term memory (LSTM) autoencoder, convolutional neural network (CNN), and LSTM model for enhanced WSF. The proposed hybrid approach is divided into two main components: feature encoding, dimensionality reduction using LSTM autoencoder and forecasting using convolutional LSTM. In the first stage, the LSTM autoencoder eliminates the uncertainties present in raw wind speed data and also reduces the computational load on the forecasting convolutional LSTM approach. Then, in the second stage, CNN is used to extract the optimum features, and the LSTM network is used to forecast the wind speed. Five different benchmark forecasting models are used to evaluate and study the proposed hybrid approach's performance. The experiment is performed with real‐time wind speed data from the Garden city wind farm, USA. The proposed hybrid approach performance is verified using various performance metrics. The experimental results demonstrate that the proposed approach improved by 40% over the second best benchmark forecasting approach.

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Machine learning models coupled with empirical mode decomposition for simulating monthly and yearly streamflows: a case study of three watersheds in Ontario, Canada

Parisouj,

Jun,

Bateni

et al. 2023

Engineering Applications of Computational Fluid Mechanics

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Short‐term wind speed forecasting using S‐transform with compactly supported kernel

Cited by 4 publications

References 41 publications

Deep convolutional long short-term memory for forecasting wind speed and direction

Deep convolutional long short-term memory for forecasting wind speed and direction

A novel hybrid framework for wind speed forecasting using autoencoder‐based convolutional long short‐term memory network

Machine learning models coupled with empirical mode decomposition for simulating monthly and yearly streamflows: a case study of three watersheds in Ontario, Canada

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