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Ping Jiang

Bio: Ping Jiang is an academic researcher from Dongbei University of Finance and Economics. The author has contributed to research in topics: Wind speed & Wind power. The author has an hindex of 7, co-authored 8 publications receiving 364 citations.

Papers
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Journal ArticleDOI
TL;DR: A forecasting system is developed based on a data pretreatment strategy, a modified multi-objective optimization algorithm, and several forecasting models that positively exceeds all contrastive models in respect to forecasting precision and stability.

189 citations

Journal ArticleDOI
TL;DR: In this paper, a hybrid wind speed forecasting model is proposed with the hope of achieving better forecasting performance, where a Least Square Support Vector Machine (LSSVM) was used to decompose the wind speed series into several series with different frequencies.

138 citations

Journal ArticleDOI
TL;DR: A novel hybrid forecasting system consisting of three modules (a data preprocessing module, optimization module, and forecasting module) is developed to improve the forecasting accuracy and stability and demonstrates the great performance of proposed system.

124 citations

Journal ArticleDOI
15 Feb 2021-Energy
TL;DR: The experimental results reveal that the proposed combined forecasting system can provide effective wind speed point and interval forecasts and is deemed more useful for the scheduling and management of electric power systems than other benchmark models.

112 citations

Journal ArticleDOI
TL;DR: Wang et al. as discussed by the authors proposed an ensemble forecasting system that integrates data decomposition technology, sub-model selection, a novel multi-objective version of the Mayfly algorithm, and different predictors to better demonstrate the stochasticity and fluctuation of wind speed data.
Abstract: Wind energy has attracted considerable attention in the past decades as a low-carbon, environmentally friendly, and efficient renewable energy. However, the irregularity of wind speed makes it difficult to integrate wind energy into smart grids. Thus, achieving credible and effective wind speed forecasting results is crucial for the operation and management of wind energy. In this study, we propose an ensemble forecasting system that integrates data decomposition technology, sub-model selection, a novel multi-objective version of the Mayfly algorithm, and different predictors to better demonstrate the stochasticity and fluctuation of wind speed data. After decomposition using the data decomposition technology, each decomposed wind speed series is considered as the input to multiple predictors, from which the optimal forecasting model for each sub-series is determined based on sub-model selection. To obtain reliable forecasting results, a novel multi-objective version of the Mayfly algorithm is proposed to estimate the optimal weight coefficients for integrating the forecasting values of the sub-series. Based on three experiments and four analyses, the proposed ensemble system is verified as effective for obtaining accurate and stable point forecasting and interval forecasting performances, thus aiding in the planning and dispatching of power grids.

94 citations


Cited by
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Journal ArticleDOI
TL;DR: A comprehensive and extensive review of renewable energy forecasting methods based on deep learning to explore its effectiveness, efficiency and application potential and the current research activities, challenges, and potential future research directions are explored.

537 citations

Journal ArticleDOI
TL;DR: An overview of existing research on wind speed and power forecasting can be found in this article, where state-of-the-art approaches for wind power and wind speed forecasting are discussed.
Abstract: This paper presents an overview of existing research on wind speed and power forecasting. It first discusses state-of-the-art wind speed and power forecasting approaches. Then, forecasting accuracy is presented based on variable factors. Finally, potential techniques to improve the accuracy of forecasting models are reviewed. A full survey on all existing models is not presented, but attempts to highlight the most promising body of knowledge concerning wind speed and power forecasting.

535 citations

Journal ArticleDOI
Huaizhi Wang1, Guibin Wang1, Gangqiang Li1, Jianchun Peng1, Yitao Liu1 
TL;DR: The comparative results demonstrate that the high-level nonlinear and non-stationary feature in the wind speed series can be learned better, and competitive performance can be obtained, and it is convinced that the proposed method has a high potential for practical applications in electric power and energy systems.

385 citations

Journal ArticleDOI
TL;DR: A novel hybrid deep-learning wind speed prediction model, which combines the empirical wavelet transformation and two kinds of recurrent neural network, is proposed, which indicates that the proposed model has satisfactory performance in the high-precision wind speed Prediction.

340 citations

Journal ArticleDOI
TL;DR: The proposed model has the best multistep prediction performance; compared to the other involved models, the proposed model is more effective and robust in extracting the trend information.

324 citations