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首页> 外文期刊>Procedia Computer Science >Forecasting Power Output of Solar Photovoltaic System Using Wavelet Transform and Artificial Intelligence Techniques
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Forecasting Power Output of Solar Photovoltaic System Using Wavelet Transform and Artificial Intelligence Techniques

机译：基于小波变换和人工智能技术的太阳能光伏系统功率预测。

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摘要

With increased penetration of solar as a variable energy resource (VER), solar photovoltaic (PV) power production is rapidly increasing into large-scale power industries. Since power output of PV systems depends critically on the weather, unexpected variations of their power output may increase the operating costs of the power system. Moreover, a major barrier in integrating this VER into the grid is its unpredictability, since steady output cannot be guaranteed at any particular time. This biases power utilities against using PV power since the planning and overall balancing of the grid becomes very challenging. Developing a reliable algorithm that can minimize the errors associated with forecasting the near future PV power generation is extremely beneficial for efficiently integrating VER into the grid. PV power forecasting can play a key role in tackling these challenges. This paper presents one-hour-ahead power output forecasting of a PV system using a combination of wavelet transform (WT) and artificial intelligence (AI) techniques by incorporating the interactions of PV system with solar radiation and temperature data. In the proposed method, the WT is applied to have a significant impact on ill-behaved PV power time-series data, and AI techniques capture the nonlinear PV fluctuation in a better way.
机译：随着太阳能作为可变能源（VER）的普及程度不断提高，太阳能光伏（PV）的发电量正迅速扩大到大型电力行业。由于光伏系统的功率输出严重依赖于天气，因此其功率输出的意外变化可能会增加功率系统的运营成本。此外，将VER集成到电网中的主要障碍是其不可预测性，因为在任何特定时间都无法保证稳定的输出。由于电网的规划和总体平衡变得非常具有挑战性，这使电力公司偏向于不使用PV电源。开发可将与预测不久的将来的光伏发电量相关的误差最小化的可靠算法，对于将VER有效地集成到电网中极为有利。光伏发电预测可以在应对这些挑战中发挥关键作用。本文结合小波变换（WT）和人工智能（AI）技术，通过结合光伏系统与太阳辐射和温度数据的相互作用，提出了光伏系统的一小时前输出功率预测。在所提出的方法中，WT被应用来对不正常的光伏发电时间序列数据产生重大影响，而AI技术可以更好地捕获非线性光伏波动。

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