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Application of Neural Network to One-Day-Ahead 24 hours Generating Power Forecasting for Photovoltaic System

机译:神经网络在前方24小时内应用光伏系统的功率预测

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In recent years, introduction of an alternative energy source such as solar energy is expected. However, insolation is not constant and output of photovoltaic (PV) system is influenced by meteorological conditions. In order to predict the power output for PV system as accurate as possible, it requires method of insolation estimation. In this paper, the authors take the insolation of each month into consideration, and confirm the validity of using neural network to predict one-day-ahead 24 hours insolation by computer simulations. The proposed method in this paper does not require complicated calculation and mathematical model with only meteorological data.
机译:近年来,预期介绍了诸如太阳能等替代能源。然而,呈现不是恒定的,光伏(PV)系统的输出受气象条件的影响。为了预测PV系统的功率输出尽可能准确,需要估计方法。在本文中,作者考虑了每个月的纠正,并确认了使用神经网络预测计算机模拟预测每一天24小时的有效性。本文中所提出的方法不需要具有气象数据的复杂计算和数学模型。

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