Predicting day-ahead solar irradiance through gated recurrent unit using weather forecasting data

In the solar power industry, irradiance forecasts are needed for planning, scheduling, and managing of photovoltaic power plants and grid-combined generating systems. A widely used method is artificial intelligence (AI), in particular, artificial neural networks, which can be trained over both historical values of irradiance and meteorological variables such as temperature, humidity, wind speed, pressure, and precipitation. In this paper, a novel version of the gated recurrent unit (GRU) method is combined with weather forecasts in order to predict solar irradiance. This method is used to forecast irradiance over a horizon of 24 h. Experiments show that the proposed method is able to outperform other AI methods. In particular, GRU using weather forecast data reduces the root mean squared error by 23.3% relative to a backpropagation neural network and 11.9% relative to a recurrent neural network. Compared to long short-term memory, the training time is reduced by 36.6%. Compared to persistence, the improvement in the forecast skill of the GRU is 42.0%. In summary, GRU is a promising technology which can be used effectively in irradiance forecasting.In the solar power industry, irradiance forecasts are needed for planning, scheduling, and managing of photovoltaic power plants and grid-combined generating systems. A widely used method is artificial intelligence (AI), in particular, artificial neural networks, which can be trained over both historical values of irradiance and meteorological variables such as temperature, humidity, wind speed, pressure, and precipitation. In this paper, a novel version of the gated recurrent unit (GRU) method is combined with weather forecasts in order to predict solar irradiance. This method is used to forecast irradiance over a horizon of 24 h. Experiments show that the proposed method is able to outperform other AI methods. In particular, GRU using weather forecast data reduces the root mean squared error by 23.3% relative to a backpropagation neural network and 11.9% relative to a recurrent neural network. Compared to long short-term memory, the training time is reduced by 36.6%. Compared to persistence, the improve...

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