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Wind Power Forecasting

机译：风电预测

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摘要

Accurate short-term wind power forecast is very important for reliable and efficient operation of power systems with high wind power penetration. There are many conventional and artificial intelligence methods that have been developed to achieve accurate wind power forecasting. Time-series based algorithms are known to be simple, robust, and have been used in the past for forecasting with some level of success. Recently some researchers have advocated for artificial-intelligence based methods such as Artificial Neural Networks (ANNs), Fuzzy Logic, etc., for forecasting because of their flexibility. This paper presents a comparison of conventional and two artificial intelligence methods for wind power forecasting. The conventional method discussed in this paper is the Autoregressive Moving Average (ARMA) which is one of the most robust and simple time-series methods. The artificial intelligence methods are Artificial Neural Networks (ANNs) and Adaptive Neuro-fuzzy Inference Systems (ANFIS). Simulation results for very-short-term and short-term forecasting show that ANNs and ANFIS are suitable for the very-short-term (10 minutes ahead) wind speed and power forecasting, and the ARMA is suitable for the short-term (1 hour ahead) wind speed and power forecasting.
机译：准确的短期风电预测对于具有高风电渗透的可靠和有效的电力系统运行非常重要。已经开发出许多传统和人工智能方法，以实现精确的风力预测。已知基于时间序列的算法是简单的，稳健的，并且过去已经使用，以便以某种程度的成功预测。最近，一些研究人员已经主张用于人工智能的方法，如人工神经网络（ANNS），模糊逻辑等，因为它们的灵活性为预测。本文介绍了常规和两种人工智能方法对风力预测的比较。本文中讨论的传统方法是自回归移动平均（ARMA），其是最强大和最简单的时间序列方法之一。人工智能方法是人工神经网络（ANNS）和自适应神经模糊推理系统（ANFIS）。对非常短期和短期预测的仿真结果表明，ANNS和ANFIS适用于非常短期（提前10分钟）风速和电力预测，ARMA适用于短期（1一小时提前）风速和电力预测。

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