Renewable energy prediction: A novel short-term prediction model of photovoltaic output power

2019 ◽  
Vol 228 ◽  
pp. 359-375 ◽  
Author(s):  
Ling-Ling Li ◽  
Shi-Yu Wen ◽  
Ming-Lang Tseng ◽  
Cheng-Shan Wang
Keyword(s):  
Renewable Energy ◽  
Prediction Model ◽  
Output Power ◽  
Short Term ◽  
Energy Prediction ◽  
Term Prediction ◽  
Short Term Prediction

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.

Short-term prediction model for daily COVID-19 reported positive cases in Senegal

2021 ◽  
Vol 8 (1) ◽  
pp. 1111-1126
Author(s):  
Aba Diop ◽  
Abdourahmane Ndao ◽  
Cheikh Tidiane Seck ◽  
Ibrahima Faye
Keyword(s):  
Prediction Model ◽  
Moving Average ◽  
Arima Model ◽  
Short Term ◽  
Ministry Of Health ◽  
Term Prediction ◽  
Daily Data ◽  
Auto Regressive ◽  
Short Term Prediction ◽  
Second Wave

In this work, we use an Auto-Regressive Integrated Moving Average (ARIMA) model to study the evolution of COVID-19 disease in Senegal and then make short-term predictions about the number of people likely to be infected by the coronavirus. We are dealing with daily data provided by the Senegalese Ministry of Health during the period from March 2, 2020 to March 2, 2021.Our results show that the peak of the disease appearsduring the second wave seems to be reached on February 12 2021. But they also show that the number of COVID-19 infections will be around 200 cases per day during the next 30 days if the trend of the total number of tests performed is maintained.

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