基于改进数据流在线分割的超短期负荷预测

Improved Data Stream On-Line Segmentation Based Ultra Short-Term Load Forecasting

为同时提高超短期负荷预测的实时性和准确性,应对现代电力系统对实时负荷预测的更高要求,提出一种基于改进数据流在线分割的超短期负荷预测方法。该方法根据负荷发展的时间趋势,利用数据流实时处理技术进行超短期预测,然后结合蕴含天气因素和负荷周期特性作用的短期负荷预测结果,对分割点的实时预测结果进行修正;其快速分段预测能力,避免了重复建模,提高了预测速度;对分割点的实时修正处理有效地增加了历史信息利用率,降低了分割点误差,使预测精度稳定在一个较高的水平。采用实际负荷数据检验该预测模型的有效性,结果表明,基于该模型的预测精度和速度均优于几种常规超短期预测算法,同时降低了拐点预测误差,在天气突变时也具有稳定的适应性。

To improve both realtime performance and accuracy of ultra short-term load forecasting and to cope with the higher requirement of power grid on realtime load forecasting, based on improved data stream on-line segmentation an ultra short-term load forecasting method is proposed. Based on the time trend of load development, the realtime data stream processing is utilized in the proposed method to perform ultra short-term forecasting, then combining with the results of short-term load forecasting, which contain the whether factors and the effect of load cycle property, the realtime forecasting result at the segmentation point is corrected. The fast segmentation and forecasting ability of the proposed method avoid repeat modeling and improve the speed of forecasting; the realtime correction and handling of the segmentation point increase historical information utilization and decrease the error of segmentation point, thus the forecasting accuracy can be maintained at a better level. Actual load data is adopted to validate the effectiveness of the proposed model, and validation results show that both load forecasting accuracy and speed by the proposed model have an advantage over those by several conventional ultra short-term load forecasting algorithms, besides the forecasting error at the inflection point of load can be decreased and the proposed method can also adapt to the sudden change of the weather.