Study of ionospheric TEC short-term forecast model based on combination method

2014 ◽  
Author(s):  
Ruizhao Niu ◽  
Chengjun Guo ◽  
Yiran Zhang ◽  
Liang He ◽  
Yanling Mao
Keyword(s):  
Forecast Model ◽  
Combination Method ◽  
Short Term ◽  
Short Term Forecast ◽  
Model Based ◽  
Term Forecast

scholarly journals Short-term Forecast Model of Vehicles Volume Based on ARIMA Seasonal Model and Holt-Winters

2017 ◽  
Vol 12 ◽  
pp. 04028 ◽  
Author(s):  
Zhi-Hui Wang ◽  
Chen-Yang Lu ◽  
Bin Pu ◽  
Gui-Wen Li ◽  
Zhen-Jiang Guo
Keyword(s):  
Forecast Model ◽  
Short Term ◽  
Short Term Forecast ◽  
Term Forecast ◽  
Seasonal Model

A short-term forecast model for hail

2007 ◽  
Vol 83 (2-4) ◽  
pp. 176-184 ◽  
Author(s):  
Laura López ◽  
Eduardo García-Ortega ◽  
José Luis Sánchez
Keyword(s):  
Forecast Model ◽  
Short Term ◽  
Short Term Forecast ◽  
Term Forecast

scholarly journals Deep RNN-Based Photovoltaic Power Short-Term Forecast Using Power IoT Sensors

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 436
Author(s):  
Hyung Keun Ahn ◽  
Neungsoo Park
Keyword(s):  
Power Generation ◽  
Forecast Model ◽  
Experimental Results ◽  
Short Term ◽  
Time Step ◽  
Short Term Forecast ◽  
Term Forecast ◽  
Weather Changes ◽  
The Impact ◽  
Pv Power Generation

Photovoltaic (PV) power fluctuations caused by weather changes can lead to short-term mismatches in power demand and supply. Therefore, to operate the power grid efficiently and reliably, short-term PV power forecasts are required against these fluctuations. In this paper, we propose a deep RNN-based PV power short-term forecast. To reflect the impact of weather changes, the proposed model utilizes the on-site weather IoT dataset and power data, collected in real-time. We investigated various parameters of the proposed deep RNN-based forecast model and the combination of weather parameters to find an accurate prediction model. Experimental results showed that accuracies of 5 and 15 min ahead PV power generation forecast, using 3 RNN layers with 12 time-step, were 98.0% and 96.6% based on the normalized RMSE, respectively. Their R2-scores were 0.988 and 0.949. In experiments for 1 and 3 h ahead of PV power generation forecasts, their accuracies were 94.8% and 92.9%, respectively. Also, their R2-scores were 0.963 and 0.927. These experimental results showed that the proposed deep RNN-based short-term forecast algorithm achieved higher prediction accuracy.

scholarly journals COVID-19 Pandemic Development in Jordan—Short-Term and Long-Term Forecasting

Vaccines ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 728
Author(s):  
Tareq Hussein ◽  
Mahmoud H. Hammad ◽  
Pak Lun Fung ◽  
Marwan Al-Kloub ◽  
Issam Odeh ◽  
...  
Keyword(s):  
Early Stage ◽  
Middle Eastern ◽  
Forecast Model ◽  
Third Wave ◽  
Short Term ◽  
Model Based ◽  
Model Merging ◽  
Term Forecast ◽  
Long Term Forecasting

In this study, we proposed three simple approaches to forecast COVID-19 reported cases in a Middle Eastern society (Jordan). The first approach was a short-term forecast (STF) model based on a linear forecast model using the previous days as a learning data-base for forecasting. The second approach was a long-term forecast (LTF) model based on a mathematical formula that best described the current pandemic situation in Jordan. Both approaches can be seen as complementary: the STF can cope with sudden daily changes in the pandemic whereas the LTF can be utilized to predict the upcoming waves’ occurrence and strength. As such, the third approach was a hybrid forecast (HF) model merging both the STF and the LTF models. The HF was shown to be an efficient forecast model with excellent accuracy. It is evident that the decision to enforce the curfew at an early stage followed by the planned lockdown has been effective in eliminating a serious wave in April 2020. Vaccination has been effective in combating COVID-19 by reducing infection rates. Based on the forecasting results, there is some possibility that Jordan may face a third wave of the pandemic during the Summer of 2021.

scholarly journals Short-term Forecast of Hourly Electricity Demand in Iran Using a Forecast Combination Method

2020 ◽  
Vol 24 (4) ◽  
pp. 57-73
Author(s):  
Seyed Farshad Fatemi Ardestani ◽  
Seyed Mahdi Barakchian ◽  
Hamideh Shokoohian ◽  
◽  
◽  
...  
Keyword(s):  
Electricity Demand ◽  
Combination Method ◽  
Forecast Combination ◽  
Short Term ◽  
Short Term Forecast ◽  
Term Forecast
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