scholarly journals Short-Term Photovoltaic Power Interval Prediction Based on the Improved Generalized Error Mixture Distribution and Wavelet Packet -LSSVM

2021 ◽  
Vol 9 ◽  
Author(s):  
Mao Yang ◽  
Tian Peng ◽  
Xin Su ◽  
Miaomiao Ma
Keyword(s):  
Prediction Interval ◽  
Wavelet Packet ◽  
Mixture Distribution ◽  
Prediction Method ◽  
Support Vector ◽  
Average Width ◽  
Efficient Operation ◽  
Confidence Levels ◽  
Interval Prediction ◽  
Probability Prediction

The periodicity and non-stationary nature of photovoltaic (PV) output power make the point prediction result contain very little information, increase the difficulty of describing the prediction uncertainty, and it is difficult to ensure the most efficient operation of the power system. Effectively predicting the PV power range will greatly improve the economics and stability of the grid. Therefore, this paper proposes an improved generalized based on the combination of wavelet packet (WP) and least squares support vector machine (LSSVM) to obtain higher accuracy point prediction results. The error mixed distribution function is used to fit the probability distribution of the prediction error, and the probability prediction is performed to obtain the prediction interval. The coverage rate and average width of the prediction interval are used as indicators to evaluate the prediction results of the interval. By comparing with the results of conventional methods based on normal distribution, at 95 and 90% confidence levels, the method proposed in this paper achieves higher coverage while reducing the average bandwidth by 5.238 and 3.756%, which verifies the effectiveness of the proposed probability interval prediction method.

scholarly journals Multi-Objective Interval Prediction of Load Based on the Conditional Copula Function

2019 ◽  
Vol 9 (5) ◽  
pp. 955 ◽  
Author(s):  
Gang Zhang ◽  
Zhixuan Li ◽  
Jinwang Hou ◽  
Kaoshe Zhang ◽  
Fuchao Liu ◽  
...  
Keyword(s):  
Prediction Accuracy ◽  
Prediction Interval ◽  
Prediction Method ◽  
Copula Function ◽  
Solution Set ◽  
Shaanxi Province ◽  
Model Parameters ◽  
Multi Objective ◽  
Interval Prediction ◽  
Conditional Copula

Compared with the point prediction, the interval prediction of the load could more effectively guarantee the safe operation of the power system. In view of the problem that the correlation between adjacent load data is not fully utilized so that the prediction accuracy is reduced, this paper proposes the conditional copula function interval prediction method, which could make full use of the correlation relationship between adjacent load data so as to obtain the interval prediction result. At the same time, there are the different prediction results of the method under different parameters, and the evaluation results of the two accuracy evaluation indicators containing PICP (prediction interval coverage probability) and the PIAW (prediction interval average width) are inconsistent, the above result that the optimal parameters and prediction results cannot be obtained, therefore, the NSGA-II (Non-dominated Sorting Genetic Algorithm-II) multi-objective optimization algorithm is proposed to seek out the optimal solution set, and by evaluating the solution set, obtain the optimal prediction model parameters and the corresponding prediction results. Finally, the proposed method is applied to the three regions of Shaanxi Province, China to conduct ultra-short-term load prediction, and compare it with the commonly used load interval prediction method such as Gaussian process regression (GPR) algorithm, artificial neural network (ANN), extreme learning machine (ELM) and others, and the results show that the proposed method always has better prediction accuracy when applying it to different regions.

scholarly journals Research on Prediction Method of Reasonable Cost Level of Transmission Line Project Based on PCA-LSSVM-KDE

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Zhao Xue-hua ◽  
Miao Xu-juan ◽  
Zhang Zhen-gang ◽  
Hao Zheng
Keyword(s):  
Prediction Model ◽  
Transmission Line ◽  
Prediction Interval ◽  
High Reliability ◽  
Prediction Method ◽  
Principal Component ◽  
Support Vector ◽  
Investment Planning ◽  
Project Investment ◽  
Project Cost

In order to reduce the investment risk, the evaluation standard of transmission line project investment planning becomes higher, which puts forward higher requirements for the reasonable level prediction of transmission line project cost. This paper combines principal component analysis (PCA) with the least squares support vector machine (LSSVM) model and establishes a point prediction model for transmission line project cost. Based on the analysis of the error of the point prediction model, the kernel density estimation (KDE) method is innovatively introduced to estimate the prediction error, and the probability density function of the error is obtained. Then, according to different confidence levels, the corresponding cost intervals are obtained, which means that the reasonable level of transmission line project cost is obtained. The results show that the coverage rate of the cost prediction interval under 85% confidence level is 88.57%. This conclusion shows that the model has high reliability and can provide a reliable basis for the evaluation of transmission line project investment planning.

scholarly journals Improved Probability Prediction Method Research for Photovoltaic Power Output

2019 ◽  
Vol 9 (10) ◽  
pp. 2043
Author(s):  
Ze Cheng ◽  
Qi Liu ◽  
Wen Zhang
Keyword(s):  
Prediction Models ◽  
Prediction Method ◽  
Gaussian Process Regression ◽  
Combination Method ◽  
Support Vector ◽  
Probabilistic Prediction ◽  
Power Prediction ◽  
Machine Error ◽  
Photovoltaic Power ◽  
Probability Prediction

Due to solar radiation and other meteorological factors, photovoltaic (PV) output is intermittent and random. Accurate and reliable photovoltaic power prediction can improve the stability and safety of grid operation. Compared to solar power point prediction, probabilistic prediction methods can provide more information about potential uncertainty. Therefore, this paper first proposes two kinds of photovoltaic output probability prediction models, which are improved sparse Gaussian process regression model (IMSPGP), and improved least squares support vector machine error prediction model (IMLSSVM). In order to make full use of the advantages of the different models, this paper proposes a combined forecasting method with divided-interval and variable weights, which divides one day into four intervals. The models are combined by the optimal combination method in each interval. The simulation results show that IMSPGP and IMLSSVM have better prediction accuracy than the original models, and the combination model obtained by the combination method proposed in this paper further improves the prediction performance.

Detection of Dendritic Spines Using Wavelet Packet Entropy and Fuzzy Support Vector Machine

2017 ◽  
Vol 16 (2) ◽  
pp. 116-121 ◽  
Author(s):  
Shuihua Wang ◽  
Yang Li ◽  
Ying Shao ◽  
Carlo Cattani ◽  
Yudong Zhang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Dendritic Spines ◽  
Wavelet Packet ◽  
Support Vector ◽  
Fuzzy Support Vector Machine

scholarly journals Interval prediction method based on Long-Short Term Memory networks for system integrated of hydro, wind and solar power

2019 ◽  
Vol 158 ◽  
pp. 6176-6182 ◽  
Author(s):  
Zhendong Zhang ◽  
Hui Qin ◽  
Liqiang Yao ◽  
Jiantao Lu ◽  
Liangge Cheng
Keyword(s):  
Solar Power ◽  
Short Term Memory ◽  
Prediction Method ◽  
Short Term ◽  
Term Memory ◽  
Interval Prediction ◽  
Long Short Term Memory

Hot Strip Tail Flick Recognition Based on Morphological Pattern Spectrum

2012 ◽  
Vol 572 ◽  
pp. 25-30
Author(s):  
Li Jing Han ◽  
Jian Hong Yang ◽  
Min Lin ◽  
Jin Wu Xu
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Steel Strip ◽  
Wavelet Packet ◽  
Support Vector ◽  
Tail Flick ◽  
Morphological Pattern ◽  
Feature Extraction Method ◽  
Pattern Spectrum ◽  
Hot Strip

Hot strip tail flick is an abnormal production phenomenon, which brings many damages. To recognize the tail flick signals from all throwing steel strip signals, a feature extraction method based on morphological pattern spectrum is proposed in this paper. The area between signal curves after multiscale opening operation and the horizontal axis is computed as the pattern spectrum value and it reflects the geometric information differences. Then, support vector machine is used as the classifier. Experimental results show that the total correct rate based on pattern spectrum feature reached 96.5%. Compared with wavelet packet energy feature, the total correct rate is 92.1%. So, the feasibility and availability of this new feature extraction method are verified.

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