Short-Term Prediction of Carbon Monoxide Concentration Using Artificial Neural Network (NARX) Without Traffic Data: Case Study: Shiraz City

2018 ◽  
Vol 43 (3) ◽  
pp. 533-540 ◽  
Author(s):  
Mohammad Reza Mohebbi ◽  
Ayub Karimi Jashni ◽  
Maryam Dehghani ◽  
Kamal Hadad
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Carbon Monoxide ◽  
Traffic Data ◽  
Short Term ◽  
Carbon Monoxide Concentration ◽  
Term Prediction ◽  
Artificial Neural ◽  
Short Term Prediction

scholarly journals ARTIFICIAL NEURAL NETWORK BASE SHORT-TERM ELECTRICITY LOAD FORECASTING: A CASE STUDY OF A 132/33KV TRANSMISSION SUB-STATION

2020 ◽  
Vol 10 (2) ◽  
pp. 200-205
Author(s):  
Isaac Adekunle Samuel ◽  
Segun Ekundayo ◽  
Ayokunle Awelewa ◽  
Tobiloba Emmanuel Somefun ◽  
Adeyinka Adewale
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Load Forecasting ◽  
Short Term ◽  
Electricity Load ◽  
Artificial Neural ◽  
Electricity Load Forecasting

scholarly journals Very Short-Term Power Forecasting of High Concentrator Photovoltaic Power Facility by Implementing Artificial Neural Network

Energies ◽  
2020 ◽  
Vol 13 (13) ◽  
pp. 3493
Author(s):  
Yaser I. Alamin ◽  
Mensah K. Anaty ◽  
José Domingo Álvarez Hervás ◽  
Khalid Bouziane ◽  
Manuel Pérez García ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Real Data ◽  
Short Term ◽  
Prediction Horizon ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Short Term Prediction ◽  
The University

Concentrator photovoltaic (CPV) is used to obtain cheaper and more stable renewable energy. Methods which predict the energy production of a power system under specific circumstances are highly important to reach the goal of using this system as a part of a bigger one or of making it integrated with the grid. In this paper, the development of a model to predict the energy of a High CPV (HCPV) system using an Artificial Neural Network (ANN) is described. This system is located at the University of Rabat. The performed experiments show a quick prediction with encouraging results for a very short-term prediction horizon, considering the small amount of data available. These conclusions are based on the processes of obtaining the ANN models and detailed discussion of the results, which have been validated using real data.

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