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

Abstract

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.