基于指数平滑和前馈神经网络的小型风力发电机组发电量预测

Zaccheus Olaofe Olaofe
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引用次数: 5

摘要

本文介绍了利用指数平滑技术和多层前馈神经网络对小型40kw风力发电机组的发电量预测进行比较。对于风能预测,基于指数平滑的数学模型被用来平滑在现场获得的时间序列数据中出现的任何季节性。该模型采用0.20、0.65和0.90三个平滑常数值,以及0.90平滑常数值与季节调整因子的组合,用于预测12个月的小型风力发电机组输出。此外,采用基于多层前馈神经网络的能量模型对水轮机发电量进行了计算。与所选的3个平滑常数相比,经季节调整后的预测模型预测风力发电量准确,预测误差最小。将季节调整后的预测模型和多层前馈神经网络的能量预测结果与考虑塔高时水轮机实际发电量的预测误差值进行比较。关键词:时间序列数据;平滑与季节因子;指数平滑法;前馈神经网络(FNN),小型风力发电机
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Wind Energy Predictions of Small-Scale Turbine Output Using Exponential Smoothing and Feed- Forward Neural Network
This article presents the comparisons of energy production predictions of a small-scale 40 kW wind turbine using an exponential smoothing technique and multilayer feed-forward neural network. For wind energy predictions, the developed mathematical model based on exponential smoothing was used to smoothen any seasonality arising in the time series data obtained at the site. This model was developed using three smoothing constant values of 0.20, 0.65, and 0.90, as well as a combination of a smoothing constant value of 0.90 with a seasonal adjustment factor for prediction of a small-scale wind turbine output for a period of 12 months. In addition, an energy model based on a multilayer feed-forward neural network was used to compute the energy generation of the turbine. The seasonally adjusted forecast model accurately predicted the wind energy output with the lowest forecast errors when compared to the chosen three smoothing constants. The energy forecasts obtained from the seasonal adjusted forecast model and multilayer feed-forward neural network were compared to the actual energy generation of the turbine at the considered tower height in terms of their forecast erroneous values. KeywordsTime Series Data (TSD); Smoothing and Seasonal Factor; Exponential Smoothing; Feed-Forward Neural Network (FNN), Small-Scale Wind Turbine
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