基于灰色神经网络优化组合的风力发电量预测研究

文中提出一种新型灰色神经网络优化组合的风力发电量预测研究,将人工神经网络预测模型和灰色预测模型有效结合,不仅考虑了风力、风向和温度等影响因素,而且将往年风力发电量的历史数据综合考虑,结合两种预测优点,从而提高了预测的准确度并降低预测误差。算例结果证明,这种新型的灰色神经网络优化组合预测值误差低于单一的灰色预测或神经网络预测。

Study on wind power capacity prediction based on grey neural network optimal combination forecasting technique

In this paper, combining artificial neural network (ANN) prediction model with gray prediction model (GM) effectively as an optimal combination forecasting technique is proposed. It can reduce the prediction error when it is applied in wind power generation capacity forecasting. Taking the factors affecting the wind power generation capacity into account are wind velocity, wind direction, temperature, and the wind power generation amount in previous years and so on. Combining with the advantages of both ANN and GM model, using the optimal combination of the forecasting techniques can improve the prediction accuracy and reduce the prediction error. From the result, the optimal combination of the forecasting techniques error is less than a single gray prediction and neural network prediction. It has research value for the wind power generation foresting in the future.