Abstract
We present a method to predict the global horizontal irradiance (GHI) one hour ahead in one-minute resolution using Artificial Neural Networks (ANNs). A feed-forward neural network with Levenberg–Marquardt Backpropagation (LM-BP) was used and was trained with four years of data from all-sky images and measured global irradiance as input. The pictures were recorded by a hemispheric sky imager at the Institute of Meteorology and Climatology (IMuK) of the Leibniz Universität Hannover, Hannover, Germany (52.23° N, 09.42° E, and 50 m above sea level). The time series of the global horizontal irradiance was measured using a thermopile pyranometer at the same site. The new method was validated with a test dataset from the same source. The irradiance is predicted for the first 10–30 min very well; after this time, the length of which is dependent on the weather conditions, the agreement between predicted and observed irradiance is reasonable. Considering the limited range that the camera and the ANN can “see”, this is not surprising. When comparing the results to the persistence model, we observed that the forecast accuracy of the new model reduced both the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE) of the one-hour prediction by approximately 40% compared to the reference persistence model under various weather conditions, which demonstrates the high capability of the algorithm, especially within the first minutes.
Highlights
The production of solar energy is subject to strong spatial and temporal fluctuations due to the dependence on meteorological boundary conditions
The results show that results showvalues that the forecast the Artificial Neural Networks (ANNs)
Model matched the measured and the forecast of the ANNvalues model of closely matched theclosely measured values, and both thevalues, Root Mean Square Error (RMSE) and both the were smaller in the new model than in the persistence model for the Mean Absolute Error (MAE) were smaller in the new ANN model than in the persistence model for the entire simulated the entire simulated hour hour
Summary
The production of solar energy is subject to strong spatial and temporal fluctuations due to the dependence on meteorological boundary conditions. This leads to uncertainties in the planning of energy supplies and, to economic inefficiencies. The installed PV power increases by a double-digit percentage per year [2]. This trend makes photovoltaics an even more important alternative for global power supply. New models for the forecast of solar energy production can help to reduce the difficulties of integrating PV systems into existing power supply structures. With the help of reliable predictive models, the market price of the solar energy is determined by supply and demand
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