A Wind Speed Combination Forecasting Method Based on Multifaceted Feature Fusion and Transfer Learning for Centralized Control Center

Tao, Liang; Chen, Chun-Yu; Mei, Chunxiao; Jing, Yanwei; Sun, Hong

doi:10.1016/j.epsr.2022.108765

Cited by 8 publications

(3 citation statements)

References 30 publications

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“…Wind speed forecasting plays a pivotal role in determining the economic viability of wind farms [5]. Firstly, precise forecasts empower power plants to strategically plan power production in advance, mitigating the impact of erratic power generation resulting from fluctuating wind speeds.…”

Section: Background Of the Researchmentioning

confidence: 99%

A Comprehensive Multivariate Wind Speed Forecasting Model Utilizing Deep Learning Neural Networks

Wei,

Tian

2024

Preprint

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Wind speed is influenced by various factors, necessitating the consideration of interactions between multiple meteorological elements in multivariate wind speed prediction. This paper proposes a multivariate combinatorial model based on a deep learning neural network, which incorporates not only historical wind speed data but also relevant meteorological features. Initially, the feature extraction of meteorological features. related to wind speed is first performed using an autoencoder (AE) and singular value decomposition (SVD). Subsequently, the complementary ensemble empirical mode decomposition and wavelet transform (CEWT) method is utilized to mitigate noise in the wind speed series. Finally, this paper utilize a gated recurrent unit (GRU) deep learning neural network for predicting the wind speed series. The optimization of the GRU’s 4 hyperparameters is accomplished through the implementation of the improved gray wolf algorithm (IGWO). This paper evaluates and validates the predictive performance of the model using two datasets. The experimental results demonstrate that the proposed model outperforms other models across 4 key performance indicators.

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Section: Background Of the Researchmentioning

confidence: 99%

A Comprehensive Multivariate Wind Speed Forecasting Model Utilizing Deep Learning Neural Networks

Wei,

Tian

2024

Preprint

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“…With the development of wind power forecasting techniques [23], hybrid forecasting methods have been used to achieve better forecasts [24]. Hybrid forecasting is the use of combined models to obtain the best performance [25].…”

Section: Introductionmentioning

confidence: 99%

Short-Term Power Prediction of Wind Power Generation System Based on Logistic Chaos Atom Search Optimization BP Neural Network

Zhang¹,

Li²,

Li³

et al. 2023

International Transactions on Electrical Energy Systems

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Wind power generation is the major approach to wind energy utilization. However, due to the volatility, intermittent, and controllability of wind power, it is difficult to control and scheduling of wind power, which brings challenges to the grid-connected operation and dispatch of wind power. Therefore, accurate power prediction of the wind power generation system is worthy of in-depth study. And this paper proposes a wind power prediction model based on logistic chaos atom search optimization (LCASO) optimized back-propagation (BP) neural network, aiming to achieve accurate and efficient power prediction. Moreover, this work utilizes data preprocessing to obtain more precise prediction results and related prediction evaluation indexes to quantificationally compare the effect of the proposed one with other prediction models based on GA-BP neural network and PSO-BP neural network. In contrast with the BP neural network, GA-BP neural network, and PSO-BP neural network, the simulation tests verify the comprehensive prediction performance and wider applicability of LCASO-BP neural network-based power prediction model.

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“…Liang et al constructed a model based on multilayer feature fusion and transfer learning for multilocation wind speed forecasting. [ 31 ] The results revealed that the proposed model outperformed other basic models in adaptability and accuracy. Jaseena and Kovoor built a hybrid wind speed forecasting model using bidirectional long short‐term memory (LSTM) model that exhibited better prediction accuracy than other models.…”

Section: Introductionmentioning

confidence: 99%

A Novel Prediction‐Integration Forecasting System for Short Wind Speed Based on Combined Data Preprocessing Technique and Weight Determination Strategy

Wang,

An,

Wang

et al. 2023

Energy Tech

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Based on the advanced theory research of artificial intelligence and data analysis strategy, a multimodel‐integrated wind speed prediction system is designed in this study, which contains a combined data preprocessing technique, weight determination strategy, and uncertainty prediction. The proposed system not only eliminates the impact of noise but also integrates the results of individual prediction models based on a weight determination strategy. In addition, uncertainty prediction is used to quantify the uncertainty caused by point prediction. The experimental results show that: 1) the mean absolute percentage error values of the proposed model at Site 1 are about 1% and 2%, respectively, which outperform some common basic models such as back propagation neural network (about 7% and 9%). 2) At the significant level α = 0.05, the prediction interval coverage probability values of the proposed model at Site 1 are about 98% and 94%, respectively, for the uncertainty forecasting, which is significantly better than most traditional methods such as extreme learning machine (about 86% and 41%). It is reasonable to conclude that the proposed system is superior to the traditional model in accuracy and stability, which can be a powerful tool for power grid planning.

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A Wind Speed Combination Forecasting Method Based on Multifaceted Feature Fusion and Transfer Learning for Centralized Control Center

Cited by 8 publications

References 30 publications

A Comprehensive Multivariate Wind Speed Forecasting Model Utilizing Deep Learning Neural Networks

A Comprehensive Multivariate Wind Speed Forecasting Model Utilizing Deep Learning Neural Networks

Short-Term Power Prediction of Wind Power Generation System Based on Logistic Chaos Atom Search Optimization BP Neural Network

A Novel Prediction‐Integration Forecasting System for Short Wind Speed Based on Combined Data Preprocessing Technique and Weight Determination Strategy

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