Short-term prediction of the output power of PV system based on improved grey prediction model

2014 ◽  
Author(s):  
Yanbin Li ◽  
Jiuju Zhang ◽  
Junming Xiao ◽  
Yang Tan
Keyword(s):  
Prediction Model ◽  
Output Power ◽  
Grey Prediction ◽  
Short Term ◽  
Pv System ◽  
Grey Prediction Model ◽  
Term Prediction ◽  
Short Term Prediction

scholarly journals Forecasting the Number of New Coronavirus Infections Us-ing an Improved Grey Prediction Model

2021 ◽  
Author(s):  
Hui Li ◽  
Bo Zeng ◽  
Jianzhou Wang ◽  
Hua’an Wu
Keyword(s):  
Prediction Model ◽  
Particle Swarm Optimization Algorithm ◽  
Percentage Error ◽  
Small Data ◽  
Grey Prediction ◽  
Matlab Simulation ◽  
Short Term ◽  
New Model ◽  
Grey Prediction Model ◽  
Coronavirus Infections

Background: Recently, a new coronavirus has been rapidly spreading from Wuhan, China. Forecasting the number of infections scientifically and effectively is of great significance to the allocation of medical resources and the improvement of rescue efficiency. Methods: The number of new coronavirus infections was characterized by “small data, poor information” in the short term. The grey prediction model provides an effective method to study the prediction problem of “small data, poor information”. Based on the order optimization of NHGM(1,1,k), this paper uses particle swarm optimization algorithm to optimize the background value, and obtains a new improved grey prediction model called GM(1,1|r,c,u). Results: Through MATLAB simulation, the comprehensive percentage error of GM(1,1|r,c,u), NHGM(1,1,k), UGM(1,1), DGM(1,1) are 2.4440%, 11.7372%, 11.6882% and 59.9265% respectively, so the new model has the best prediction performance. The new coronavirus infections was predicted by the new model. Conclusion: The number of new coronavirus infections in China increased continuously in the next two weeks, and the final infections was nearly 100 thousand. Based on the prediction results, this paper puts for-ward specific suggestions.

Application of grey prediction model to the prediction of medical consumables consumption

2019 ◽  
Vol 9 (2) ◽  
pp. 213-223
Author(s):  
Xuejun Shen ◽  
Minghui Yue ◽  
Pengfei Duan ◽  
Guihai Wu ◽  
Xuerui Tan
Keyword(s):  
Grey Prediction ◽  
Material Consumption ◽  
Short Term ◽  
Content Type ◽  
Abc Classification ◽  
Term Prediction ◽  
Annual Consumption ◽  
Medical Materials ◽  
Short Term Prediction ◽  
Medical Material

Purpose Based on the prediction of the consumption of medical materials, the purpose of this paper is to study the applicability of the grey model method to the field and its predicted accuracy. Design/methodology/approach The ABC classification method is used to classify medical consumables and select the analysis objects. The GM (1,1) model predicts the annual consumption of medical materials. The GM (1,1) modeling of the consumption of the selected medical materials in 2006~2017 was carried out by using the metabolite sequence and the sequence topology subsequence, respectively. The average rolling error and the average rolling accuracy are calculated to evaluate the prediction accuracy of the model. Findings The ABC classification results show that Class A projects, which account for only 9.79 percent of the total inventory items, occupy most of the inventory funds. Eight varieties with varying purchases and usages and complete historical data were selected for further analysis. The subsequence GM(1,1) model group constructed by two different methods predicts and scans the annual consumption of eight kinds of medical materials, and the rolling precision can reach more than 90 percent. Originality/value The metabolic GM (1,1) model is an ideal predictive model that can meet the requirements for a short-term prediction of medical material consumption (Zhang et al., 2014). The GM (1,1) model is more suitable for a short-term prediction of medical material consumption with less data modeling.

A Short-Term Prediction Model of Topic Popularity on Microblogs

2013 ◽  
pp. 759-769 ◽  
Author(s):  
Juanjuan Zhao ◽  
Weili Wu ◽  
Xiaolong Zhang ◽  
Yan Qiang ◽  
Tao Liu ◽  
...  
Keyword(s):  
Prediction Model ◽  
Short Term ◽  
Term Prediction ◽  
Short Term Prediction

scholarly journals Improved Random Forest Method for Ultra-short-term Prediction of the Output Power of a Photovoltaic Cluster

2021 ◽  
Vol 9 ◽  
Author(s):  
Mao Yang ◽  
Meng Zhao ◽  
Dingze Liu ◽  
Miaomiao Ma ◽  
Xin Su
Keyword(s):  
Random Forest ◽  
Output Power ◽  
Training Dataset ◽  
Total Output ◽  
Short Term ◽  
Forest Model ◽  
Term Prediction ◽  
Two Phases ◽  
Short Term Prediction ◽  
Daily Time

Current models for the prediction of the output power of photovoltaic (PV) clusters suffer from low prediction accuracy and are prone to overfitting. To address these problems, we propose an improved random forest (RF)-based method for ultra-short-term prediction of PV cluster output power. The total output power data for the PV clusters are used as the training dataset and fed into the RF model to obtain preliminary predictions. The error and accuracy of the preliminary predictions for individual sampling points concerning the actual values of the PV cluster output power are assessed. Each of the daily time series of preliminary predictions is divided into two phases according to whether the output power is increasing (morning) or decreasing (afternoon). The final ultra-short-term predictions of the PV cluster output power are obtained by correcting the two phases of preliminary predictions through trend correction and peak correction, respectively. The results show that, compared with the unimproved model, the accuracy of the stochastic forest model is 1.48% higher than that of the modified random forest model., which proves the effectiveness and practicability of the proposed method.

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