A novel combination forecasting model for wind power integrating least square support vector machine, deep belief network, singular spectrum analysis and locality-sensitive hashing

Energy ◽  
2019 ◽  
Vol 168 ◽  
pp. 558-572 ◽  
Author(s):  
Yachao Zhang ◽  
Jian Le ◽  
Xiaobing Liao ◽  
Feng Zheng ◽  
Yinghai Li
Keyword(s):  
Support Vector Machine ◽  
Wind Power ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Least Square ◽  
Locality Sensitive Hashing ◽  
Support Vector ◽  
Forecasting Model ◽  
Combination Forecasting ◽  
Combination Forecasting Model

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.

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.

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 Short Term Wind Power Prediction Based on Data Regression and Enhanced Support Vector Machine

Energies ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 6319
Author(s):  
Chia-Sheng Tu ◽  
Chih-Ming Hong ◽  
Hsi-Shan Huang ◽  
Chiung-Hsing Chen
Keyword(s):  
Support Vector Machine ◽  
Wind Speed ◽  
Regression Model ◽  
Wind Power ◽  
Support Vector ◽  
Forecasting Model ◽  
Short Term ◽  
Wind Power Forecasting ◽  
Data Regression ◽  
Power Forecasting

This paper presents a short-term wind power forecasting model for the next day based on historical marine weather and corresponding wind power output data. Due the large amount of historical marine weather and wind power data, we divided the data into clusters using the data regression (DR) algorithm to get meaningful training data, so as to reduce the number of modeling data and improve the efficiency of computing. The regression model was constructed based on the principle of the least squares support vector machine (LSSVM). We carried out wind speed forecasting for one hour and one day and used the correlation between marine wind speed and the corresponding wind power regression model to realize an indirect wind power forecasting model. Proper parameter settings for LSSVM are important to ensure its efficiency and accuracy. In this paper, we used an enhanced bee swarm optimization (EBSO) to perform the parameter optimization for LSSVM, which not only improved the forecast model availability, but also improved the forecasting accuracy.

Sign in / Sign up

Export Citation Format

Share Document