基于改进持续法的短期风电功率预测

为了有效减轻风能波动对电网的影响，提高风电在电力市场中的竞争力，风电功率预测研究具有重要意义。该文提出了基于小波变换的改进持续法，对短期风电功率预测进行研究。该方法首先利用小波变换将原始风速信号分解为高频部分和低频部分，针对高频信号相邻的两个数据之间相似度较低，波动较大的特点，采用滑动平均法进行预测，而低频信号仍然采用持续法预测，最后通过小波重构以及风电功率特性曲线转换得到风电功率预测值。与原持续法相比较，平均相对误差由17.10%降至11.81%，平均绝对误差由39.58 kW降至23.48 kW，有效地提高了短期风电功率预测的精度，具有一定的实际应用价值。

Short-term wind power forecasting based on an improved persistence approach

Wind power forecasting is of importance for power grids. It can mitigate the disadvantageous impacts of wind farms on power systems and enhance the competitiveness of wind power in electricity markets. This paper proposed an improved persistence approach based on wavelet. First, the original data of wind speeds were decomposed into high-frequency component and low-frequency component by using wavelet. Moving average method was used for predicting the high-frequency subseries, in terms of low similarity and easy fluctuation properties in high-frequency component, and the low-frequency sub-series were still predicted by the persistence method. Then, all sub-series were recomposed to form a time series of wind speeds. Wind power of a wind turbine can be further forecasted through a power curve which could transfer wind speeds data into wind power. Compared with the original method, the average relative error reduced to 11.81% from 17.10%, and the average absolute error decreased to 23.48 kW from 39.58 kW. Case study showed that the wind power forecasting accuracy was effectively improved by use of an improved persistence approach which could be put into operation in practice.