Forecast Solar Energy Using Artificial Neural Networks

2019 ◽  
pp. 500-507
Author(s):  
Sukhampreet Kaur Dhillon ◽  
Charu Madhu ◽  
Daljeet Kaur ◽  
Sarvjit Singh
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Solar Energy ◽  
Artificial Neural

A New Approach for Meteorological Variables Prediction at Kuala Lumpur, Malaysia, Using Artificial Neural Networks: Application for Sizing and Maintaining Photovoltaic Systems

2012 ◽  
Vol 134 (2) ◽  
Author(s):  
Tamer Khatib ◽  
Azah Mohamed ◽  
M. Mahmoud ◽  
K. Sopian
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Wind Speed ◽  
Relative Humidity ◽  
Solar Energy ◽  
Ambient Temperature ◽  
Meteorological Variables ◽  
Mean Bias Error ◽  
Artificial Neural ◽  
Prediction Approach

This research presents a new meteorological variables prediction approach for Malaysia using artificial neural networks. The developed model predicts four meteorological variables using sun shine ratio, day number, and location coordinates. These meteorological variables are solar energy, ambient temperature, wind speed, and relative humidity. However, three statistical values are used to evaluate the proposed model. These statistical values are mean absolute percentage error (MAPE), mean bias error (MBE), and root mean square error (RMSE). Based on results, the developed model predicts accurately the four meteorological variables. The MAPE, RMSE, and MBE in predicting solar radiation are 1.3%, 5.8 (1.8%), and 0.9 (0.3%), respectively, while the MAPE, RMSE, and MBE values for ambient temperature prediction are 1.3%, 0.4 (1.7%), and 0.1 (0.4%), respectively. In addition, the MAPE, RMSE, and MBE values in relative humidity prediction are 3.2%, 3.2, and 0.2. As for wind speed prediction, it is the worst in accuracy among the predicted variables because the MAPE, RMSE, and MBE values are 28.9%, 0.5 (31.3%), and 0.02 (1.25%). Such a developed model helps in sizing photovoltaic (PV) systems using solar energy and ambient temperature records. Moreover, wind speed and relative humidity records could be used in estimating dust concentration group which leads to dust deposition on a PV system.

Improving solar energy prediction in complex topography using artificial neural networks: Case study Peninsular Malaysia

2015 ◽  
Vol 34 (5) ◽  
pp. 1528-1535
Author(s):  
Ali Wadi Abbas Al-Fatlawi ◽  
Nasrudin Abdul Rahim ◽  
Rahman Saidur ◽  
Thomas Arthur Ward
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Solar Energy ◽  
Peninsular Malaysia ◽  
Complex Topography ◽  
Energy Prediction ◽  
Artificial Neural

Optimal performance assessment for a photo-Fenton degradation pilot plant driven by solar energy using artificial neural networks

2011 ◽  
Vol 36 (14) ◽  
pp. 1314-1324 ◽  
Author(s):  
A. Bassam ◽  
I. Salgado-Tránsito ◽  
I. Oller ◽  
E. Santoyo ◽  
A.E. Jiménez ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Solar Energy ◽  
Performance Assessment ◽  
Pilot Plant ◽  
Optimal Performance ◽  
Artificial Neural
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