Deterministic and probabilistic wind speed forecasting with de-noising-reconstruction strategy and quantile regression based algorithm

Hu, Jianming; Heng, Jiani; Wen, Jiemei; Zhao, Weigang

doi:10.1016/j.renene.2020.08.077

Cited by 66 publications

(12 citation statements)

References 53 publications

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“…Hence, the time series dataset, which needs either a long term or a term, could use LSTM to process the model conduction. Hu et al [65] revealed a nonlinear algorithm to combine the LSTM with an extreme learning machine and a a differential evolution algorithm (DE) to improve the number of hidden layers to b LSTM has excellent performance in dealing with long-term datasets due to the gate and memory cell of its architecture, which is stable and better able to overcome the overfitting problem. Hence, the time series dataset, which needs either a long term or a short term, could use LSTM to process the model conduction.…”

Section: Evaluation and Comparison Methodsmentioning

confidence: 99%

“…Hence, the time series dataset, which needs either a long term or a short term, could use LSTM to process the model conduction. Hu et al [65] revealed a novel nonlinear algorithm to combine the LSTM with an extreme learning machine and applied a differential evolution algorithm (DE) to improve the number of hidden layers to balance the structure complexity and performance. Altan et al [26] also chose LSTM combined with the decomposition method and optimizer to conduct wind speed forecasting.…”

Section: Evaluation and Comparison Methodsmentioning

confidence: 99%

“…The wavelet-based method was used to decompose the raw data into different series to process rather than keep them at the same level [32]. Ensemble EMD [34,52], EMD [65] and mode decomposition (VMD) [77] are decomposition-based methods. Xiang [78] applied a secondary decomposition (SD) preprocessing method, which is a signal processing method, to propose a deep learning-based model with higher efficiency, to conduct wind speed and wind power forecasting.…”

Section: Applicationsmentioning

confidence: 99%

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Review on Deep Learning Research and Applications in Wind and Wave Energy

2022

Energies

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Wind energy and wave energy are considered to have enormous potential as renewable energy sources in the energy system to make great contributions in transitioning from fossil fuel to renewable energy. However, the uncertain, erratic, and complicated scenarios, as well as the tremendous amount of information and corresponding parameters, associated with wind and wave energy harvesting are difficult to handle. In the field of big data handing and mining, artificial intelligence plays a critical and efficient role in energy system transition, harvesting and related applications. The derivative method of deep learning and its surrounding prolongation structures are expanding more maturely in many fields of applications in the last decade. Even though both wind and wave energy have the characteristics of instability, more and more applications have implemented using these two renewable energy sources with the support of deep learning methods. This paper systematically reviews and summarizes the different models, methods and applications where the deep learning method has been applied in wind and wave energy. The accuracy and effectiveness of different methods on a similar application were compared. This paper concludes that applications supported by deep learning have enormous potential in terms of energy optimization, harvesting, management, forecasting, behavior exploration and identification.

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Section: Evaluation and Comparison Methodsmentioning

confidence: 99%

Section: Evaluation and Comparison Methodsmentioning

confidence: 99%

Section: Applicationsmentioning

confidence: 99%

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Review on Deep Learning Research and Applications in Wind and Wave Energy

2022

Energies

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“…As a forecasting method, we will choose extrapolation since the internal transportation of sugar is characterized by the time and volume of the transported cargo. Many models allow forecasting with varying degrees of accuracy: correlation-regression analysis [4][5][6][7], neural network models [8,9], research-based on multiple regression [10][11][12], models based on classification-regression trees [13,14], maximum likelihood sampling models [15][16][17] and many others [18][19][20][21]. For forecasting economic time series, models of the ARIMA class are used [22].…”

Section: Justification Of the Choice Of Methods For Solving The Problemmentioning

confidence: 99%

Analysis of the Forecast Price as a Factor of Sustainable Development of Agriculture

et al. 2021

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Analysis of the rise in prices for consumer goods is a state’s priority task. The state assumes the obligation to regulate pricing in all spheres of consumption. First of all, the prices for essential commodities to which agricultural products belong are analyzed. The article shows the changes in prices for consumer goods of agricultural products (sugar) during a pandemic. The analysis of forecasting prices for sugar and its impact on the development of its production is carried out. The construction of the forecast model was based on extrapolation. The structure of a forecast model for price changes was based on the analysis of the time series of the Autoregressive Integrated Moving Average (ARIMA) class. This model consists of an autoregressive model and a moving average model. A forecast of the volume of domestic sugar transportation by rail has been completed. The algorithms implemented this model for searching for initial approximations and optimal parameters for the predictive model. The Hirotsugu Akaike Information Criterion (AIC) was used to select the best model. The algorithms were implemented in the Python programming language. The quality check of the description was performed with a predictive model of actual data. An economic interpretation of the rise in sugar prices and proof of the forecast’s truth obtained from a financial point of view were carried out.

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“…Four types of modeling theory are grouped: physical method, traditional statistical method, artificial intelligence (AI) method, and hybrid method. The physical method requires detailed wind farm background data and numerical weather prediction (NWP), which shows better performance in medium-and long-term prediction with high-quality NWP (Hu et al, 2020). The traditional statistical method is represented by autoregressive integrated moving average (ARIMA) (Singh et al, 2021), seasonal autoregressive integrated moving average (SARIMA) , multilayer perceptron (MLP) (Deo et al, 2018;, and extreme learning machine (ELM) (Li et al, 2016), showing great accuracy in very-short-term prediction.…”

Section: Introductionmentioning

confidence: 99%

Numerical Weather Prediction Correction Strategy for Short-Term Wind Power Forecasting Based on Bidirectional Gated Recurrent Unit and XGBoost

et al. 2022

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Accurate short-term wind power forecasting (WPF) plays a crucial role in grid scheduling and wind power accommodation. Numerical weather prediction (NWP) wind speed is the fundamental data for short-term WPF. At present, reducing NWP wind speed forecast errors contributes to improving the accuracy of WPF from the perspective of data quality. In this article, a variational mode decomposition combined with bidirectional gated recurrent unit (VMD-BGRU) method for NWP wind speed correction and XGBoost forecasting model are proposed. First, several NWP wind speed sub-series are divided by VMD to obtain more abundant multidimensional timing features. BGRU is applied to establish the potential relation between decomposed NWP wind speed sub-series and measured wind speed and get the proposed wind speed correction model. Then, a more clear regression forecasting model is trained based on XGBoost using historical measured wind speed and power. The corrected NWP wind speed is used to forecast wind power by XGBoost. Finally, the superiority of the proposed method is validated on a wind farm located in China. The results show that the proposed correction model and forecasting model outperform other compared models.

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Deterministic and probabilistic wind speed forecasting with de-noising-reconstruction strategy and quantile regression based algorithm

Cited by 66 publications

References 53 publications

Review on Deep Learning Research and Applications in Wind and Wave Energy

Review on Deep Learning Research and Applications in Wind and Wave Energy

Analysis of the Forecast Price as a Factor of Sustainable Development of Agriculture

Numerical Weather Prediction Correction Strategy for Short-Term Wind Power Forecasting Based on Bidirectional Gated Recurrent Unit and XGBoost

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