scholarly journals Short-Term Photovoltaic Power Generation Combination Forecasting Method Based on Similar Day and Cross Entropy Theory

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Qi Wang ◽  
Shunxiang Ji ◽  
Minqiang Hu ◽  
Wei Li ◽  
Fusuo Liu ◽  
...  
Keyword(s):  
Power Generation ◽  
Least Square ◽  
Cross Entropy ◽  
Support Vector ◽  
Forecasting Model ◽  
Entropy Theory ◽  
Short Term ◽  
Combination Forecasting ◽  
Combination Forecasting Model ◽  
Similar Day

The forecast for photovoltaic (PV) power generation is of great significance for the operation and control of power system. In this paper, a short-term combination forecasting model for PV power based on similar day and cross entropy theory is proposed. The main influencing factors of PV power are analyzed. From the perspective of entropy theory, considering distance entropy and grey relation entropy, a comprehensive index is proposed to select similar days. Then, the least square support vector machine (LSSVM), autoregressive and moving average (ARMA), and back propagation (BP) neural network are used to forecast PV power, respectively. The weights of three single forecasting methods are dynamically set by the cross entropy algorithm and the short-term combination forecasting model for PV power is established. The results show that this method can effectively improve the prediction accuracy of PV power and is of great significance to real-time economical dispatch.

Short-Term Load Combination Forecast Based on Rough Set and Support Vector Machine

2011 ◽  
Vol 201-203 ◽  
pp. 2481-2487
Author(s):  
Yuan Sheng Huang ◽  
Li Ming Yuan
Keyword(s):  
Support Vector Machine ◽  
Rough Set ◽  
Attribute Reduction ◽  
Support Vector ◽  
Forecasting Model ◽  
Short Term ◽  
Combination Forecast ◽  
Load Combination ◽  
Combination Forecasting ◽  
Combination Forecasting Model

A short-term load combination forecasting model based on rough set and support vector machine was proposed in this paper, firstly build decision table based on historical data, and data mining the data through attribute reduction algorithms, and then use the results of prediction methods to be the input of the SVM, practical load value to be the output, training according to the algorithm of the SVM. the result shows that the SVM combination forecasting model has a better balance fitting and extrapolation,and its prediction accuracy is better than single prediction model.

scholarly journals Combination Forecasting Model for Predicting the Shelf Life of Two-state Materials Based on Support Vector Machine

2015 ◽  
Vol 9 (1) ◽  
pp. 124-129
Author(s):  
Li Zhiwei ◽  
Gao Qi ◽  
Liu Shenyang ◽  
Li Zhen
Keyword(s):  
Support Vector Machine ◽  
Prediction Method ◽  
Weighted Average ◽  
Support Vector ◽  
Forecasting Model ◽  
Forecasting Models ◽  
Optimal Weights ◽  
Combination Forecasting ◽  
Neural Network Prediction ◽  
Combination Forecasting Model

A combination forecasting model based on Support Vector Machine (SVM) whose objective is to minimize the structure risk, is proposed. The storage failure of two-state materials tends to fail immediately without any recognizable defeats prior to the failure, which increases the difficulty of forecasting, so the combination forecasting model is often used to optimize the prediction effect. The core ideas of previous combination forecasting models such as those based on forecasting error and those based on nonlinear weighted average are finding the optimal weights, but the structure of forecasting model is fixed. In this paper, three single forecasting models, Weibull distribution statistic method, BP neural network prediction method and SPFM (Sliding Polynomial Fitting Method) are chosen in which their forecast mechanisms are completely different. The results of single forecasting methods are used as training set of SVM. By using libsvm toolbox, we can get the nonlinear mapping functions that have the minimum structure risk. At last, a simulation is conducted to verify this model by using the data from Petroleum Center.

scholarly journals Photovoltaic Power Forecasting Based on EEMD and a Variable-Weight Combination Forecasting Model

2018 ◽  
Vol 10 (8) ◽  
pp. 2627 ◽  
Author(s):  
Hui Wang ◽  
Jianbo Sun ◽  
Weijun Wang
Keyword(s):  
Power Generation ◽  
Solar Energy ◽  
Low Frequency ◽  
Intermediate Frequency ◽  
Developed Countries ◽  
Forecasting Model ◽  
Combination Forecasting ◽  
Variable Weight ◽  
Combination Forecasting Model ◽  
Pv Power Generation

It is widely considered that solar energy will be one of the most competitive energy sources in the future, and solar energy currently accounts for high percentages of power generation in developed countries. However, its power generation capacity is significantly affected by several factors; therefore, accurate prediction of solar power generation is necessary. This paper proposes a photovoltaic (PV) power generation forecasting method based on ensemble empirical mode decomposition (EEMD) and variable-weight combination forecasting. First, EEMD is applied to decompose PV power data into components that are then combined into three groups: low-frequency, intermediate-frequency, and high-frequency. These three groups of sequences are individually predicted by the variable-weight combination forecasting model and added to obtain the final forecasting result. In addition, the design of the weights for combination forecasting was studied during the forecasting process. The comparison in the case study indicates that in PV power generation forecasting, the prediction results obtained by the individual forecasting and summing of the sequences after the EEMD are better than those from direct prediction. In addition, when the single prediction model is converted to a variable-weight combination forecasting model, the prediction accuracy is further improved by using the optimal weights.

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