Kalman filter model, as a tool for short-term forecasting of solar potential: case of the Dakar site

Abstract

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.

Solar Radiation Prediction Using a Novel Hybrid Model of ARMA and NARX

“…Energies 2021, 14, x FOR PEER REVIEW 4 of 29 of time series. Its effectiveness to predict solar radiation has been proven in several research works [17,20]. The AR presumes that each point can be predicted performing the p previous points and taking into account a random error term as presented in Equation (1).…”

“…These good forecasting results have been explained by the convenience, the accurate prediction, and the simple computations which characterize the ARIMA model. ARMA has been the focus of several other studies [16,17], it was used in [16] to forecast the solar radiation and then compared with the persistence model. Simulation results have shown that ARMA outperforms the persistence model on the short and medium horizon.…”

“…Simulation results have shown that ARMA outperforms the persistence model on the short and medium horizon. The study presented in [17] has applied the ARMA model to predict the global solar radiation for 24 h at Dakar, Senegal. The effectiveness of ARMA has been proven in this work and it can be used as a decision support tool for planning PV solar power plant production in Senegal and its surroundings.…”

Day-Ahead Solar Irradiance Forecasting Using Hybrid Recurrent Neural Network with Weather Classification for Power System Scheduling

“…The performance was evaluated using four indicators that are widely used in the literature to evaluate the performance of forecasting models. These are: the root mean square error (RMSE), the normalized root mean square error (NRMSE), the mean absolute error (MAE), and the normalized mean absolute error (NMAE), as defined by Equations ( 3)-( 6) [65][66][67]:…”

Short-term Multi Horizons Forecasting of Solar Irradiation Based on Artificial Neural Network with Meteorological Data: Application in the North-west of Senegal

“…To complete this limit, we propose forecasting models for short-term horizons from 30 minutes to 06 hours. Using Kalman filters, solar potential forecasting is carried out over a 20-minute horizon in Dakar [16] ; the results are obtained with a normal quadratic error of 4.8%. The works [17] studied short-term forecasting of solar potential in Dakar using the autoregressive moving average (ARMA) method; annual meteorological data from the "École Supérieure Polytechnique Dakar" were used; the results show a correlation coefficient of 0.97 and a root mean square error (RMSE) of 0.629.…”