Short-Term Forecasting and Uncertainty Analysis of Photovoltaic Power Based on the FCM-WOA-BILSTM Model

Cao, Wensi; Zhou, Jianjiang; Xu, Qian; Zhen, Juan; Huang, Xiaobo

doi:10.3389/fenrg.2022.926774

Cited by 7 publications

(3 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the traditional LSTM is capable to extract data time-related information only in the forward direction. In comparison, the LSTM based on a bidirectional prediction strategy (i.e., BiLSTM) relies on double hidden layers which are opposite in the directions of transmission for connection with the same output layer, which allows the output layer to obtain the information about the past and future states (Cao et al, 2022). Therefore, BiLSTM can extract time-characteristic information in two different directions, thus improving data integrity utilization.…”

Section: Bidirectional Long Short-term Memory (Bilstm)mentioning

confidence: 99%

A novel few-sample wind power prediction model based on generative adversarial network and quadratic mode decomposition

Yuan

2023

Front. Energy Res.

View full text Add to dashboard Cite

With the emergence of various new power systems, accurate wind power prediction plays a critical role in their safety and stability. However, due to the historical wind power data with few samples, it is difficult to ensure the accuracy of power system prediction for new wind farms. At the same time, wind power data show significant uncertainty and fluctuation. To address this issue, it is proposed in this research to build a novel few-sample wind power prediction model based on the least-square generative adversarial network (LSGAN) and quadratic mode decomposition (QMD). Firstly, a small amount of original wind power data are generated to improve the data by least-square generative adversarial network, which solves the error in prediction with limited sample data. Secondly, a quadratic mode decomposition method based on ensemble empirical mode decomposition (EEMD) and variational mode decomposition (VMD) is developed to address the instability of wind power data and extract hidden temporal characteristics. Specifically, ensemble empirical mode decomposition decomposes the data once to obtain a set of intrinsic mode functions (IMFs), and then variational mode decomposition is used to decompose the fuzzy irregular IMF1 function twice. Finally, a bidirectional long short-term memory network (BiLSTM) based on particle swarm optimization (PSO) is applied to predict wind power data. The LSGAN-QMD-PSO-BiLSTM model proposed in this research is verified on a wind farm located in Spain, which indicates that the proposed model achieves the lowest root mean square error (RMSE) and mean absolute error (MAE) errors of 100.6577 and 66.5175 kW, along with the highest R2 of 0.9639.

show abstract

Section: Bidirectional Long Short-term Memory (Bilstm)mentioning

confidence: 99%

A novel few-sample wind power prediction model based on generative adversarial network and quadratic mode decomposition

Yuan

2023

Front. Energy Res.

View full text Add to dashboard Cite

show abstract

“…Researchers increasingly integrate neural network models with intelligent algorithms to determine optimal parameters [ 53 ]. This integration has led to substantial improvements in the accuracy of integrated predictive models compared with single predictive models [ 54 ].…”

Section: Introductionmentioning

confidence: 99%

Ultra-early prediction of the process parameters of coal chemical production

Li,

Yao,

Luo

et al. 2024

Heliyon

View full text Add to dashboard Cite

“…Among them, LSTM and its improvement methods have received extensive attention. Cao et al 16 proposed a fuzzy‐c‐means (FCM)‐whale optimization algorithm (WOA)‐bi‐directional LSTM model that increased the accuracy of short‐term PV power prediction under different weather types. Agga et al 17 combined LSTM and convolutional neural network (CNN) to predict the power output of PV power stations in different scenarios.…”

Section: Introductionmentioning

confidence: 99%

Interval prediction of short‐term photovoltaic power based on an improved GRU model

Zhang,

Liao,

Shu

et al. 2024

Energy Science & Engineering

View full text Add to dashboard Cite

The accurate prediction of photovoltaic (PV) power is crucial for planning, constructing, and scheduling high‐penetration distributed PV power systems. Traditional point prediction methods suffer from instability and lack reliability, which can be effectively addressed through interval prediction. This study proposes a short‐term PV power interval prediction method based on the framework of sparrow search algorithm (SSA)‐variational mode decomposition (VMD)‐convolutional neural network (CNN)‐gate recurrent unit (GRU). First, PV data undergo similar day clustering based on permutation entropy and VMD is applied to solar radiation signals with high correlation. Then, the hyperparameters of GRU are optimized by SSA according to the comprehensive evaluation indicator of interval prediction proposed in this study. Subsequently, quantile prediction results are obtained based on CNN‐GRU using the optimal parameters from SSA optimization. Finally, the prediction interval is composed of multiple quantile prediction results. A MATLAB R2022b program is developed to compare different prediction methods. The results demonstrate that compared to single neural network methods, the proposed method effectively improves the coverage width‐based criterion. In the interval prediction of sunny and rainy similar days, the comprehensive evaluation indicators of the proposed method are only 54.3% and 37.4% of the single GRU, respectively, indicating significantly improved interval prediction accuracy.

show abstract

Short-Term Forecasting and Uncertainty Analysis of Photovoltaic Power Based on the FCM-WOA-BILSTM Model

Cited by 7 publications

References 33 publications

A novel few-sample wind power prediction model based on generative adversarial network and quadratic mode decomposition

A novel few-sample wind power prediction model based on generative adversarial network and quadratic mode decomposition

Ultra-early prediction of the process parameters of coal chemical production

Interval prediction of short‐term photovoltaic power based on an improved GRU model

Contact Info

Product

Resources

About