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In this paper, the simplest data-in/data-out fusion architecture was used to combine neural network, fuzzy logic, statistical, and persistence forecasts using four different experimental strategies to produce a single predicted output.
When the wind is steady, the persistence forecasts yield good results [27, 28].
PV power forecasts based on the multiple feed forward irradiance forecast model outperform the PV power persistence forecasts.
Their method achieves an (8.2%) MAE reduction compared to hourly persistence forecasts, giving an MAE-based skill score of 0.08.
Our model also provides forecasts that outperform persistence forecasts by between (6%) and (80%) in terms of mean absolute error.
We also show that the VAR model performs similarly to or better than other methods proposed in the literature, including persistence forecasts.
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The results indicate that the forecasts have skill; outperforming a persistence forecast and demonstrating reliability, resolution and discrimination.
But this approach is less accurate when looking further ahead.To improve upon persistence forecasting, companies rely on numerical weather prediction (NWP), the approach used by meteorologists to produce national weather forecasts.
The firm says eWind can reduce the hour-ahead prediction error from as high as 7.5% for persistence forecasting to as low as 4% of a wind farm's rated capacity.
These two forecasting systems showed a significant improvement over the pure persistence forecast.
The GHI persistence forecast overestimates the GHI on a cloudy day.
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