Abstract
Accurate prediction of solar irradiance is beneficial in reducing energy waste associated with photovoltaic power plants, preventing system damage caused by the severe fluctuation of solar irradiance, and stationarizing the power output integration between different power grids. Considering the randomness and multiple dimension of weather data, a hybrid deep learning model that combines a gated recurrent unit (GRU) neural network and an attention mechanism is proposed forecasting the solar irradiance changes in four different seasons. In the first step, the Inception neural network and ResNet are designed to extract features from the original dataset. Secondly, the extracted features are inputted into the recurrent neural network (RNN) network for model training. Experimental results show that the proposed hybrid deep learning model accurately predicts solar irradiance changes in a short-term manner. In addition, the forecasting performance of the model is better than traditional deep learning models (such as long short term memory and GRU).
Highlights
While the overall world’s energy consumption increases, photovoltaic energy generation is becoming increasingly important
An efficient and effective way of utilizing solar irradiance power is highly demanded for those countries [3,4]
The Gated recurrent unit (GRU) model: unlike Long short term memory (LSTM), gated recurrent unit (GRU) has a simple structure with only two gates: a reset gate and an update gate
Summary
While the overall world’s energy consumption increases, photovoltaic energy generation is becoming increasingly important. Non-renewable energy resources (coal, oil, natural gas, etc.) have the disadvantages of having limited storage, providing high pollution, and causing landscape changes [1]. As part of the process of replacing traditional energy resources with renewable energy resources, the factor of environmental protection is highly relevant. Clean and non-polluting renewable energy resources (solar, wind, and geothermal, etc.) have been attracting both scientists’ and engineers’. For countries with a large landscape, such as China, the use of solar energy as a replacement for traditional oil-based energy resources is an urgent priority. The solar radiation in the whole country is 3340 MJ/m2 –8400 MJ/m2. An efficient and effective way of utilizing solar irradiance power is highly demanded for those countries [3,4]
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