Repeated wavelet transform based ARIMA model for very short-term wind speed forecasting

Aasim,; Singh, S. N.; Mohapatra, Abheejeet

doi:10.1016/j.renene.2019.01.031

Cited by 462 publications

(107 citation statements)

References 24 publications

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“…erefore, the study of the prediction problem of underwater acoustic signal is of great significance in the processing of underwater acoustic signal. In traditional forecasting models [7,8], autoregressive and moving average model (ARMA) and autoregressive integrated moving average model (ARIMA) models have been widely used in time series prediction. However, ARMA and ARIMA can only capture the linear relationship of the signal in nature, but not the nonlinear relationship of the signal, which will have certain limitations on the time series prediction.…”

Section: Introductionmentioning

confidence: 99%

Underwater Acoustic Signal Prediction Based on MVMD and Optimized Kernel Extreme Learning Machine

Yang

Gao

2020

Complexity

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Aiming at the chaotic characteristics of underwater acoustic signal, a prediction model of grey wolf-optimized kernel extreme learning machine (OKELM) based on MVMD is proposed in this paper for short-term prediction of underwater acoustic signals. To solve the problem of K value selection in variational mode decomposition, a new K value selection method MVMD is proposed from the perspective of mutual information, which avoids the blindness of variational mode decomposition (VMD) in the preset modal number. Based on the prediction model of kernel extreme learning machine (KELM), this paper uses grey wolf optimization (GWO) algorithm to optimize and select its regularization parameters and kernel parameters and proposes an optimized kernel extreme learning machine OKELM. To further improve the prediction performance of the model, combined with MVMD, an underwater acoustic signal prediction model based on MVMD-OKELM is established. MVMD-OKELM prediction model is applied to Mackey–Glass chaotic time series prediction and underwater acoustic signal prediction and is compared with ARIMA, EMD-OKELM, and other prediction models. The experimental results show that the proposed MVMD-OKELM prediction model has a higher prediction accuracy and can be effectively applied to the prediction of underwater acoustic signal series.

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Section: Introductionmentioning

confidence: 99%

Underwater Acoustic Signal Prediction Based on MVMD and Optimized Kernel Extreme Learning Machine

Yang

Gao

2020

Complexity

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“…This results in accumulation of data and this method need large computational time to correlate these data in order to forecast the wind speed. Due to this limitation, these methods are not suitable for short-term forecasting horizons; therefore， these approaches are best suited for medium-term and long-term wind speed forecasting [2,7,8].…”

Section: Introductionmentioning

confidence: 99%

“…These models can be linear as well as non-linear in nature. These techniques are, generally, useful if one is interested in very short-term and short-term forecasts [1,7,8]. However, most of the existing statistical approaches for renewable energy forecasts are formulated as linear models that limit their ability to deal with more challenging prediction problems with longer forecasting time horizons [7].…”

Section: Introductionmentioning

confidence: 99%

“…For the hybrid methods, they may combine physical with statistical techniques or use a combination of different intelligent approaches, etc. [8,9]. In addition, and as far as the intelligent and hybrid methods are concerned, several techniques have been proposed recently.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, and as far as the intelligent and hybrid methods are concerned, several techniques have been proposed recently. these techniques may include K-Nearest Neighbor prediction method (KNN), Autoregressive Moving Average (ARMA) and Support vector machine (SVM) models, genetic algorithm (GA), fuzzy logic based methods, artificial neural network (ANN), and wavelet transform (WT) based techniques [1,5,8,10,11]. Moreover, combinations of two or more machine learning techniques may be employed to enhance the forecast and prediction accuracy [1,9,10,[12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Efficient Wind Speed Forecasting Using Discrete Wavelet Transform and Artificial Neural Networks

Berrezzek¹,

Khelil²,

Bouadjila³

2019

RIA

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Electricity demand is globally increasing throughout the world, giving rise to many other renewable energy sources such as solar and wind energy. Therefore, to efficiently manage grid-connected wind power farms, precise wind forecast is very useful and necessary. This paper seeks to find out the most appropriate discrete wavelet transform (DWT), combined with artificial neural networks (ANN), for wind speed forecasting. Wavelet decomposition is applied to obtain a smoothed wind speed signal for more accurate prediction through neural networks. Wind speed data of three cities from the northern Africa region are employed, namely Annaba, Sidi Bouzid and Tetouan located in Algeria, Tunisia and Morocco respectively. The obtained simulation results show that the Daubechies wavelet db4 with 5level decomposition outperforms all other standard wavelets in terms of wind speed forecasting accuracy.

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A day‐ahead sleeved power purchase agreement model for estimating the profit of wind farms in the Indian energy market

Das

Malakar

2021

Int Trans Electr Energ Syst

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SUMMARY The ever‐increasing demand for green energy sources encourages wind farms to participate in the energy market through long‐term contracts. This paper discusses the existing frameworks of the wind energy market and presents the strategies to integrate wind energy into the grid through the valuable power purchase agreement (PPA). Critical observations reveal that the opportunities of the short‐term energy market remain unexplored for wind farms till date. To promote more investments and to enhance the operating economy, the wind energy sector must adapt to the sporadic nature of generation in the day‐ahead energy market. The selection of accurate PPA for wind farms is a potent area of research and has encouraged the authors to explore this area vividly. In view of this, the main objective of the article is to develop a day‐ahead sleeved PPA model and to evaluate its effectiveness in the Indian energy market. To correlate the proposed model with practicality aspects, seasonal wind speed scenarios are forecasted using the sARIMA model for a practical wind farm. The performance of the employed sARIMA model is evaluated through proper comparative assessment with other models. Thereafter to validate the importance of the developed PPA, a detailed comparative analysis is carried out with the existing long‐term PPA. Further, the potentiality of the proposed sleeved PPA is compared with its other counterparts. Result analysis confirms the usefulness of the proposed PPA model in maximizing the economy of the renewable energy market entities.

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Repeated wavelet transform based ARIMA model for very short-term wind speed forecasting

Cited by 462 publications

References 24 publications

Underwater Acoustic Signal Prediction Based on MVMD and Optimized Kernel Extreme Learning Machine

Underwater Acoustic Signal Prediction Based on MVMD and Optimized Kernel Extreme Learning Machine

Efficient Wind Speed Forecasting Using Discrete Wavelet Transform and Artificial Neural Networks

A day‐ahead sleeved power purchase agreement model for estimating the profit of wind farms in the Indian energy market

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