Short-Term Wind Power Prediction Based on a Hybrid Markov-Based PSO-BP Neural Network

Wang, Chia‐Hung; Zhao, Qigen; Tian, Rong

doi:10.3390/en16114282

Cited by 17 publications

(6 citation statements)

References 38 publications

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“…However, it can be concluded that the results obtained in our work, and results of other authors [2,13,17,22], are similar in terms of accu-racy: the deviation of the predicted power from the actual power is not greater than a few percent.…”

Section: Comparison Of the Resultssupporting

confidence: 84%

An Intelligent Approach to Short-Term Wind Power Prediction Using Deep Neural Networks

Niksa-Rynkiewicz,

Stomma,

Witkowska

et al. 2023

Journal of Artificial Intelligence and Soft Computing Research

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In this paper, an intelligent approach to the Short-Term Wind Power Prediction (STWPP) problem is considered, with the use of various types of Deep Neural Networks (DNNs). The impact of the prediction time horizon length on accuracy, and the influence of temperature on prediction effectiveness have been analyzed. Three types of DNNs have been implemented and tested, including: CNN (Convolutional Neural Networks), GRU (Gated Recurrent Unit), and H-MLP (Hierarchical Multilayer Perceptron). The DNN architectures are part of the Deep Learning Prediction (DLP) framework that is applied in the Deep Learning Power Prediction System (DLPPS). The system is trained based on data that comes from a real wind farm. This is significant because the prediction results strongly depend on weather conditions in specific locations. The results obtained from the proposed system, for the real data, are presented and compared. The best result has been achieved for the GRU network. The key advantage of the system is a high effectiveness prediction using a minimal subset of parameters. The prediction of wind power in wind farms is very important as wind power capacity has shown a rapid increase, and has become a promising source of renewable energies.

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Section: Comparison Of the Resultssupporting

confidence: 84%

An Intelligent Approach to Short-Term Wind Power Prediction Using Deep Neural Networks

Niksa-Rynkiewicz,

Stomma,

Witkowska

et al. 2023

Journal of Artificial Intelligence and Soft Computing Research

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show abstract

“…They have been developed rapidly because of their good adaptability to various non-stationary data. Wang C. H. et al (2023a) used the backpropagation (BP) neural network to predict wind power, and the experimental results confirmed the outstanding advantages of complex neurons in adaptive ability and nonlinear expression. Liu et al (2020) used a support vector machine (SVM) to predict wind speed data, and the experimental results verified the feasibility of the SVM for regression prediction.…”

Section: Introductionmentioning

confidence: 85%

Forecasting model for short-term wind speed using robust local mean decomposition, deep neural networks, intelligent algorithm, and error correction

Li,

Liu,

Wen

2024

Front. Energy Res.

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Wind power generation has aroused widespread concern worldwide. Accurate prediction of wind speed is very important for the safe and economic operation of the power grid. This paper presents a short-term wind speed prediction model which includes data decomposition, deep learning, intelligent algorithm optimization, and error correction modules. First, the robust local mean decomposition (RLMD) is applied to the original wind speed data to reduce the non-stationarity of the data. Then, the salp swarm algorithm (SSA) is used to determine the optimal parameter combination of the bidirectional gated recurrent unit (BiGRU) to ensure prediction quality. In order to eliminate the predictable components of the error further, a correction module based on the improved salp swarm algorithm (ISSA) and deep extreme learning machine (DELM) is constructed. The exploration and exploitation capability of the original SSA is enhanced by introducing a crazy operator and dynamic learning strategy, and the input weights and thresholds in the DELM are optimized by the ISSA to improve the generalization ability of the model. The actual data of wind farms are used to verify the advancement of the proposed model. Compared with other models, the results show that the proposed model has the best prediction performance. As a powerful tool, the developed forecasting system is expected to be further used in the energy system.

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“…The UMAP algorithm aims to gather connected data clustered in the projection space, while keeping unconnected data as far away as possible. (13) This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and i is the weight of data.…”

Section: B Weather Category Identificationmentioning

confidence: 99%

An Integrated Modeling Strategy for Wind Power Forecasting Based on Dynamic Meteorological Visualization

Zhang,

2024

IEEE Access

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The escalating incorporation of wind energy into power grids imposes constraints on the effective operation and control of power systems. Effective short-term wind power forecasting technology is essential for safe power supply and stable operation of the power grid. Thus, a UMAP-IVMD-ILSTM model for wind power forecasting is proposed based on the uniform manifold approximation and projection (UMAP) integrated improved variational mode decomposition (IVMD) and multi-level improved long and short-term memory (LSTM) networks. First, the UMAP approach was utilized to reduce the dimension of related meteorological indicators, and k-means clustering was applied to classify the weather categories after dimension reduction. Subsequently, through the improved variational mode decomposition, the wind power of the adjacent day is decomposed. Moreover, long and short-term memory networks were optimized using the sparrow search algorithm (SSA) as an improved LSTM (ILSTM), and a wind power forecasting model with multi-level LSTM was established for each mode, and the forecasting results of each mode were integrated. Through actual wind farm operation data, it was proven that the method proposed in this study is effective in processing high-dimensional numerical weather prediction (NWP) data. The model can better preserve the information of the original data and reduce the complexity of the original data sequence based on visualization. Compared with the comparative models, the proposed model reduces the RMSE and MAE by up to 18.2% and 21.6% respectively, and has the least running time, which can effectively improve the accuracy and efficiency of wind power prediction.

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Short-Term Wind Power Prediction Based on a Hybrid Markov-Based PSO-BP Neural Network

Cited by 17 publications

References 38 publications

An Intelligent Approach to Short-Term Wind Power Prediction Using Deep Neural Networks

An Intelligent Approach to Short-Term Wind Power Prediction Using Deep Neural Networks

Forecasting model for short-term wind speed using robust local mean decomposition, deep neural networks, intelligent algorithm, and error correction

An Integrated Modeling Strategy for Wind Power Forecasting Based on Dynamic Meteorological Visualization

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