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Short-Term Wind Speed Forecasting of Lelystad Wind Farm by Using ANN Algorithms

机译:基于人工神经网络算法的莱斯特斯塔德风电场短期风速预测

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The installation of wind energy based electricity systems is growing at a very fast pace all over the world because of the increased urge of using renewable energy resources and environmental concerns regarding electricity generation. Forecasting wind speed is found to be critical for wind energy systems since it greatly influences its large-scale integration. The intermittent nature of wind speed leads to further problems in its large-scale integration in the power systems. Wind speed forecasting is essential to operate wind energy based power systems in an efficient and secure way. In this paper, different ANN algorithms have been applied to forecast short-term wind speed of Lelystad Wind Farm, Nederland using MATLAB R1 pmb4a. The data used in the forecasting are hourly historical data of the wind direction & wind speed. The simulation results have shown accurate one hour ahead forecasts with small error in wind speed forecasting.
机译:基于风能的电力系统的安装在全世界范围内以非常快的速度增长,这是因为人们越来越强烈地渴望使用可再生能源,并且对发电的环境问题也越来越关注。发现风速对风能系统至关重要,因为它极大地影响了风能系统的大规模集成。风速的间歇性导致其在电力系统中的大规模集成带来了进一步的问题。风速预测对于以有效和安全的方式运行基于风能的电力系统至关重要。在本文中,已使用MATLAB R1 pmb4a将不同的ANN算法应用于荷兰莱里斯塔德风电场的短期风速预测。预测中使用的数据是风向和风速的每小时历史数据。仿真结果显示了准确的一小时提前预报,风速预报误差很小。

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