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

A. Yona, T. Senjyu, A. Saber, T. Funabashi, H. Sekine, Chul-Hwan Kim
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引用次数: 119

Abstract

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.
神经网络在光伏发电系统一天前24小时发电量预测中的应用
近年来,人们期望引进一种替代能源,如太阳能。然而,日照并不是恒定的,光伏发电系统的发电量也会受到气象条件的影响。为了尽可能准确地预测光伏系统的输出功率,需要估算日照量的方法。本文考虑了各月的日照情况,通过计算机模拟验证了神经网络预测1天前24小时日照的有效性。本文提出的方法不需要复杂的计算和数学模型,只需要气象数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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