A Hybrid Model for Short-term PV Output Forecasting Based on PCA-GWO-GRNN

Ge, Leijiao; Xian, Yiming; Yan, Jun; Wang, Bo; Wang, Zhongguan

doi:10.35833/mpce.2020.000004

Cited by 61 publications

(25 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Indicator of quantile W ITH the increasing capacity of renewable energy, the randomness and dynamic fluctuations of electrical magnitudes set new requirements for power system security, efficiency, and flexibility [1]- [3]. More specifically, as wind power production usually suffers from lower controllability and higher variability, compared with conventional power generation, considerable uncertainties in power system operation are introduced [4], [5].…”

Section: Nomenclature ηmentioning

confidence: 99%

Performance Improvement of Very Short-term Prediction Intervals for Regional Wind Power Based on Composite Conditional Nonlinear Quantile Regression

Zhou

Sun

Wang

et al. 2022

Journal of Modern Power Systems and Clean Energy

View full text Add to dashboard Cite

Accurate regional wind power prediction plays an important role in the security and reliability of power systems. For the performance improvement of very short-term prediction intervals (PIs), a novel probabilistic prediction method based on composite conditional nonlinear quantile regression (CCNQR) is proposed. First, the hierarchical clustering method based on weighted multivariate time series motifs (WMTSM) is studied to consider the static difference, dynamic difference, and meteorological difference of wind power time series. Then, the correlations are used as sample weights for the conditional linear programming (CLP) of CCNQR. To optimize the performance of PIs, a composite evaluation including the accuracy of PI coverage probability (PICP), the average width (AW), and the offsets of points outside PIs (OPOPI) is used to quantify the appropriate upper and lower bounds. Moreover, the adaptive boundary quantiles (ABQs) are quantified for the optimal performance of PIs. Finally, based on the real wind farm data, the superiority of the proposed method is verified by adequate comparisons with the conventional methods.

show abstract

Section: Nomenclature ηmentioning

confidence: 99%

Performance Improvement of Very Short-term Prediction Intervals for Regional Wind Power Based on Composite Conditional Nonlinear Quantile Regression

Zhou

Sun

Wang

et al. 2022

Journal of Modern Power Systems and Clean Energy

View full text Add to dashboard Cite

show abstract

“…In the case where the information is not lost, with fewer parameters instead of the original multiple parameters, the calculation and convergence speed improves. As a classic data dimensionality reduction method, the main purpose of PCA (Ge et al, 2020) is "dimensionality reduction," and its idea is to convert multiple indicator features into a small number of comprehensive indicators. Each principal component can reflect most of the information of the original variable, discarding redundant information.…”

Section: Pca Principal Component Analysismentioning

confidence: 99%

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

Cao

Zhou

et al. 2022

Front. Energy Res.

View full text Add to dashboard Cite

Aiming to solve the problem that photovoltaic power generation is always accompanied by uncertainty and the short-term prediction accuracy of photovoltaic power (PV) is not high, this paper proposes a method for short-term photovoltaic power forecasting (PPF) and uncertainty analysis using the fuzzy-c-means (FCM), whale optimization algorithm (WOA), bi-directional long short-term memory (BILSTM), and no-parametric kernel density estimation (NPKDE). First, the principal component analysis (PCA) is used to reduce the dimensionality of the daily feature vector, and then the FCM is used to divide the weather into four categories: sunny, cloudy, rainy, and extreme weather. Second, the WOA algorithm is used to train the hyperparameters of BILSTM, and finally, the optimized hyperparameters were used to construct a WOA-BILSTM prediction model to train the four types of weather samples after FCM clustering. The NPKDE method was used to calculate the probability density distribution of PV prediction errors and confidence intervals for PPF. The RMSEs of the FCM-WOA-BILSTM model are 2.46%, 4.89%, and 1.14% for sunny, cloudy, and rainy weather types, respectively. The simulation results of the calculation example show that compared with the BP, LSTM, GRU, PSO-BILSTM, and FCM-PSO-BP models, the proposed FCM-WOA-BILSTM model has higher prediction accuracy under various weather types, which verifies the effectiveness of the method. Moreover, the NPKDE method can accurately describe the probability density distribution of forecast errors.

show abstract

“…The artificial fish swarm algorithm and particle swarm optimization AFSA-PSO is used to develop aircraft path planning [11]. L.Ge et al [12] made a hybrid forecast of short-term photovoltaic power generation based on the PCA-GWO-GRNN algorithm.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Electrical Fault Detection and Recognition Based on Gray Wolf Optimization and Support Vector Machine

Zou¹,

Lv²,

Li³

et al. 2022

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

With the increase of electrical equipment, the hidden danger of its failure also rises. Aiming at the problem that the classification accuracy of the basic traditional classification algorithm is not high when judging whether the electrical failure occurs, this paper proposes a Gray Wolf Optimization Support Vector Machine model to improve the recognition rate of electrical diagnosis, referred to as GWO-SVM. We first collected the most common linear (incandescent lamps) and non-linear (microwave ovens) household electrical appliances in normal working and arc fault waveform signals in real life, and then carried out frequency domain feature extraction. Finally, compared with no-optimized SVM and BP neural network, we employed the gray wolf optimization algorithm to optimize support vector machine, and the accuracy of GWO-SVM reaches 90%.

show abstract

A Hybrid Model for Short-term PV Output Forecasting Based on PCA-GWO-GRNN

Cited by 61 publications

References 28 publications

Performance Improvement of Very Short-term Prediction Intervals for Regional Wind Power Based on Composite Conditional Nonlinear Quantile Regression

Performance Improvement of Very Short-term Prediction Intervals for Regional Wind Power Based on Composite Conditional Nonlinear Quantile Regression

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

Intelligent Electrical Fault Detection and Recognition Based on Gray Wolf Optimization and Support Vector Machine

Contact Info

Product

Resources

About