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首页> 外文会议>2014 International Conference on Electrical Sciences and Technologies in Maghreb >Solar radiation forecasting using artificial neural network for local power reserve
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Solar radiation forecasting using artificial neural network for local power reserve

机译：使用人工神经网络进行本地电力储备的太阳辐射预测

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摘要

Renewable energy sources have a variable nature and are greatly depending on weather conditions. The load is also uncertain. Hence, it is necessary to use power reserve equipment to compensate unforeseen imbalances between production and load. However, this power reserve must be ideally minimized in order to reduce the system cost with a satisfying security level. The quantification of power reserve could be calculated through analysis of forecasting uncertainty errors of both generation and load. Therefore, in this paper, a back propagation artificial neural network approaches is derived to forecast solar radiations. Predictions have been analyzed according to weather classification. Some error indexes have been introduced to evaluate forecasting models performances and calculate the prediction accuracy. Forecasting results can be used for decision making of power reserve for renewable energy sources system with some probability or possibility methods.
机译：可再生能源具有可变的性质，并且在很大程度上取决于天气条件。负载也不确定。因此，有必要使用动力储备设备来补偿生产和负载之间无法预料的不平衡。但是，必须在理想情况下最小化此功率储备，以降低系统成本并具有令人满意的安全级别。可以通过分析发电和负荷的不确定性误差来计算电力储备的量化。因此，在本文中，推导了一种反向传播人工神经网络方法来预测太阳辐射。已根据天气分类分析了预测。引入了一些误差指标来评估预测模型的性能并计算预测精度。预测结果可以通过某种概率或可能性方法用于可再生能源系统的动力储备决策。

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