Wind Power Short-Term Forecasting Model Based on the Hierarchical Output Power and Poisson Re-Sampling Random Forest Algorithm

Hao, Jie; Zhu, Changsheng; Guo, Xiuting

doi:10.1109/access.2020.3048382

Cited by 19 publications

(4 citation statements)

References 12 publications

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“…Hao et al put forward the quasiadaptive classification RF algorithm. First, it was found that the Adaboost algorithm has great advantages in adaptive selfhelp sampling weight and adaptive voting weight setting [10]. Fornaser et al mixed the C4.5DT algorithm, and Fornaser et al mixed the C4.5DTalgorithm and CART (Classification and Regression Tree) algorithm into one algorithm and used the mixed algorithm to generate the RF algorithm, which improved the accuracy of RF [11].…”

Section: Introductionmentioning

confidence: 99%

Optimization and Application of Random Forest Algorithm for Applied Mathematics Specialty

2022

Security and Communication Networks

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For unbalanced data classification, RF (Random forest) algorithm will cause problems such as poor classification performance and a large DT scale. With the advent of the era of big data, RF algorithms should have the ability to process large-scale data. Aiming at the problem that RF cannot handle unbalanced data well, this paper improves the feature selection method built in RF and proposes a new feature selection algorithm. On the basis of feature importance ranking, randomness is introduced to ensure the strength of each tree and reduce the correlation between trees. In the extended transform data set, the sensitivity of the RF model has exceeded 0.8, and that of other models has increased to about 0.65. The prediction accuracy of the centralized RF model for the company’s credit rating reached 100%, while the CART model misjudged companies C6 and C7, while the Logit model misjudged companies C3, C5, and C8. Experiments prove the extrapolation of the RF model and its excellent prediction ability. In the practical application of applied mathematics specialty, the RF optimization algorithm proposed in this study can well handle continuous variables and improve the classification accuracy of RF. This paper holds that the advantages of the RF algorithm in data processing and model performance will make it more widely used in the field of enterprise credit risk evaluation.

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Section: Introductionmentioning

confidence: 99%

Optimization and Application of Random Forest Algorithm for Applied Mathematics Specialty

2022

Security and Communication Networks

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“…A convolution operation to capture the spatial-temporal correlation between neighboring wind farms was based on the novel spatial-temporal wind power predictor (CSTWPP) [15] and a spatiotemporal convolutional network (STCN), each developed separately [16]. New ANN model predictive control-based models [14,[17][18][19][20][21][22] have been developed and offered for wind power prediction in microgrid applications and use air density and wind speed as input parameters.…”

Section: Introductionmentioning

confidence: 99%

A Review of Modern Wind Power Generation Forecasting Technologies

Tsai

Hong

et al. 2023

Sustainability

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The prediction of wind power output is part of the basic work of power grid dispatching and energy distribution. At present, the output power prediction is mainly obtained by fitting and regressing the historical data. The medium- and long-term power prediction results exhibit large deviations due to the uncertainty of wind power generation. In order to meet the demand for accessing large-scale wind power into the electricity grid and to further improve the accuracy of short-term wind power prediction, it is necessary to develop models for accurate and precise short-term wind power prediction based on advanced algorithms for studying the output power of a wind power generation system. This paper summarizes the contribution of the current advanced wind power forecasting technology and delineates the key advantages and disadvantages of various wind power forecasting models. These models have different forecasting capabilities, update the weights of each model in real time, improve the comprehensive forecasting capability of the model, and have good application prospects in wind power generation forecasting. Furthermore, the case studies and examples in the literature for accurately predicting ultra-short-term and short-term wind power generation with uncertainty and randomness are reviewed and analyzed. Finally, we present prospects for future studies that can serve as useful directions for other researchers planning to conduct similar experiments and investigations.

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“…The short-term prediction results of wind power are highly significant for determining the power generation plan, improving the wind power consumption capacity of a power system, and formulating a maintenance plan for wind turbines [3,4]. At present, extensive research has been carried out on wind power prediction methods at home and abroad, and machine learning has been widely used in the field of wind power research, and the machine learning class of prediction methods include extreme learning machine and deep learning algorithms belong to neural network, so the three are not in a parallel relationship [5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Short‐term prediction of wind power based on temporal convolutional network and the informer model

Wang,

Chang,

Liu

et al. 2023

IET Generation Trans & Dist

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In this study, a new short‐term wind power prediction model based on a temporal convolutional network (TCN) and the Informer model is proposed to solve the problem of low prediction accuracy caused by large wind speed fluctuations in short‐term prediction. First, an input feature selection method based on the maximum information coefficient is proposed after considering the problem of information interference caused by excessively large input features. A dynamic time planning method is used to select the optimal input step of historical power. Then, the combined forecasting model composed of TCN and the Informer is constructed in accordance with the numerical weather forecast and historical power data. Lastly, the pinball loss function is used to expand the prediction model into a quantile regression model, measure the effect of volatility, quantify the volatility range of prediction, and finally, obtain a deterministic prediction result. The actual measured data of wind farms in the Bohai Sea area are selected for analysis and calculation. The results show that the prediction model proposed in this study achieves better accuracy in deterministic prediction and interval prediction than the traditional model.

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Wind Power Short-Term Forecasting Model Based on the Hierarchical Output Power and Poisson Re-Sampling Random Forest Algorithm

Cited by 19 publications

References 12 publications

Optimization and Application of Random Forest Algorithm for Applied Mathematics Specialty

Optimization and Application of Random Forest Algorithm for Applied Mathematics Specialty

A Review of Modern Wind Power Generation Forecasting Technologies

Short‐term prediction of wind power based on temporal convolutional network and the informer model

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