E3S Web Conf.
Volume 57, 20182018 3rd International Conference on Sustainable and Renewable Energy Engineering (ICSREE 2018)
|Number of page(s)||4|
|Section||Clean Energy Development and Utilization|
|Published online||05 October 2018|
Kalman filter model, as a tool for short-term forecasting of solar potential: case of the Dakar site
Electrical Engineering Department of the Polytechnic High School of Dakar Senegal, Laboratory International Center for Solar Energy, BP 5085 Dakar-Fann, Senegal.
2 Department of Mathematics and Computer Science, University Cheikh Anta Diop of Dakar Senegal, Laboratory LID.
3 Department of physical, University Cheikh Anta Diop of Dakar Senegal, Laboratory of Hydraulic and fluid Mechanics.
The prediction of solar potential is an important step toward the evaluation of PV plant production for the best energy planning. In this study, the discrete Kalman filter model was implemented for short-term solar resource forecasting one the Dakar site in Senegal. The model input parameters are constituted at a time t of the air temperature, the relative humidity and the global solar radiation. The expected output at time t+T is the global solar radiation. The model performance is evaluated with the square root of the normalized mean squared error (NRMSE), the absolute mean of the normalized error (NMAE), the average bias error (NMBE). The model Validation is carried out by means of the data measured within the Polytechnic Higher School of Dakar for one year. The simulation results following the 20 minute horizon show a good correlation between the prediction and the measurement with an NRMSE of 4.8%, an NMAE of 0.27% and an NMBE of 0.04%. This model could contribute to help photovoltaic based energy providers to better plan the production of solar photovoltaic plants in Sahelian environments.
Key words: Kalman filter / solar potential / Prediction / Dakar / short-term
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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