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Short-Term Solar Irradiance Forecasting Using Neural Network and Genetic Algorithm

机译：神经网络和遗传算法的短期太阳辐射预报

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摘要

Solar irradiance is a vital factor for a solar plant because the inaccurate prediction can increase the risk and the cost of operation. To reach a high-prediction accuracy, a model for short-term direct normal irradiance prediction is proposed in this paper. First, the inputs of the model were discussed and included historical data, such as direct normal irradiance, air temperature, pressure, and the wind velocity. Then, the model was constructed and optimized by genetic algorithm. Model validation was conducted using data from the National Renewable Energy Laboratory's India Solar Resource Data. The result shows that the forecast skill of the proposed model improved 80 % over the persistence model and also better than that of some published models.
机译：太阳辐照度对于太阳能发电厂是至关重要的因素，因为不准确的预测会增加风险和运营成本。为了达到较高的预测精度，本文提出了一种短期直接法向辐照度预​​测模型。首先，讨论了模型的输入，并包括了历史数据，例如直接法向辐照度，气温，压力和风速。然后，通过遗传算法构建并优化了模型。使用来自国家可再生能源实验室的印度太阳能资源数据的数据进行了模型验证。结果表明，所提出的模型的预测技巧比持久性模型提高了80％，也优于某些已发布的模型。

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