Wind Speed Forecasting Using a Two-Stage Forecasting System With an Error Correcting and Nonlinear Ensemble Strategy

Wang, Jianzhou; Dong, Yao; Wang, Jianzhou

doi:10.1109/access.2019.2957174

Cited by 35 publications

(19 citation statements)

References 52 publications

(51 reference statements)

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“…To assess the forecasting ability of our developed combined strategy, three performance evaluation indices must be introduced to comprehensively measure the proposed model. The three indices adopted in this study are the mean absolute error (MAE), mean square error (MSE) [48] and mean absolute percent error (MAPE) [49]. The detailed definitions of the indicators are given in Table. 2.…”

Section: B Assessment Indicators For Prediction Effectivenessmentioning

confidence: 99%

A Combined Strategy for Wind Speed Forecasting Using Data Preprocessing and Weight Coefficients Optimization Calculation

2020

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Wind speed forecasting is an essential procedure in electric grid dispatching. Short-term wind speed forecasting can be a great challenge and an intractable issue in increasing wind energy output and guaranteeing power safety. The current wind speed forecasting models are based on a single model, which is generally an artificial intelligence model or a statistical forecasting method. However, these models cannot perform well in all cases. An effective combined model is proposed in this paper, and this model includes four parts: weight coefficient optimization calculation based on the nonpositive constraint combination theory, singular-spectrum analysis, combined forecasting and discussion of results. The developed model can decrease the negative influences of the component models and maximize the advantages of each component model. To evaluate the forecasting accuracy of our proposed model, ten minutes of wind speed data from Shandong Peninsula, China, were used as test cases. It is clearly demonstrated that the developed combined strategy outperforms the individual forecasting methods in terms of forecast performance and stability. INDEX TERMS Combined model, data preprocessing approach, nonpositive constraint combination theory, weight coefficient optimization.

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Section: B Assessment Indicators For Prediction Effectivenessmentioning

confidence: 99%

A Combined Strategy for Wind Speed Forecasting Using Data Preprocessing and Weight Coefficients Optimization Calculation

2020

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“…Real estimates corresponding to the beginning of the process can be made only after a set of statistical data (roughly 4  n ) regarding the deviation of the real trajectory from that calculated on the basis of the selected model, the corresponding mathematical expectations are naturally selected as estimates [33][34][35][36][37]. The random component z is observed in the form of forecast errors, then it can be assumed that at the initial moment of estimation…”

Section: It Can Be Assumed That the Initial Values Of The Random Compmentioning

confidence: 99%

Mathematical Model of the Development of Manufacturing Defects in the Surface Layer of Substrates of Moems’ Functional Components

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Omarov

Chala

2020

Eskişehir Technical University Journal of Science and Technology a - Applied Sciences and Engineering

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A mathematical model of the development of manufacturing defects, with the prediction of the random component of the model in the substrates of functional components of MOEMS, which are made of semiconductors, in particular, silicon, are developed in the article. The main manufacturing defects that arise in the surface layer of the substrates of the MOEMS functional components taking into account the technological processes of their production and dynamic processes were used when developing the model. The developed mathematical model takes into account the occurrence of a random component of the model with its predictive ability. The possibility of such control is the basis for the development of the scientific direction of technology and equipment for the production of semiconductors, materials and electronic devices-defect engineering, based on the management and forecasting of defect formation processes.

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“…Different prediction horizons have different application values. For short-term and ultrashort-term wind speed prediction, the most critical impact is its role in power system operation [5]. For example, the output power of a wind farm in the United States can fluctuate by several hundred megawatts within an hour, which has a significant impact on the safe and stable operation of the system.…”

Section: Introductionmentioning

confidence: 99%

Research on Hybrid Wind Speed Prediction System Based on Artificial Intelligence and Double Prediction Scheme

Nie

Zhang

et al. 2020

Complexity

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Wind energy analysis and wind speed modeling have a significant impact on wind power generation systems and have attracted significant attention from many researchers in recent decades. Based on the inherent characteristics of wind speed, such as nonlinearity and randomness, the prediction of wind speed is considered to be a challenging task. Previous studies have only considered point prediction or interval measurement of wind speed separately and have not combined these two methods for prediction and analysis. In this study, we developed a novel hybrid wind speed double prediction system comprising a point prediction module and interval prediction module to compensate for the shortcomings of existing research. Regarding point prediction in the developed double prediction system, a novel nonlinear integration method based on a backpropagation network optimized using the multiobjective evolutionary algorithm based on decomposition was successfully implemented to derive the final prediction results, which enable further improvement of the accuracy of point prediction. Based on point prediction results, we propose an interval prediction method that constructs different intervals according to the classification of different data features via fuzzy clustering, which provides reliable interval prediction results. e experimental results demonstrate that the proposed system outperforms existing methods in engineering applications and can be used as an effective technology for power system planning.

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Wind Speed Forecasting Using a Two-Stage Forecasting System With an Error Correcting and Nonlinear Ensemble Strategy

Cited by 35 publications

References 52 publications

A Combined Strategy for Wind Speed Forecasting Using Data Preprocessing and Weight Coefficients Optimization Calculation

A Combined Strategy for Wind Speed Forecasting Using Data Preprocessing and Weight Coefficients Optimization Calculation

Mathematical Model of the Development of Manufacturing Defects in the Surface Layer of Substrates of Moems’ Functional Components

Research on Hybrid Wind Speed Prediction System Based on Artificial Intelligence and Double Prediction Scheme

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