Existing load forecasting methods typically assume that recent load data are available for prediction. This is not in conformity with reality since there is a time gap between the flow date (when power is consumed) and when measurement values are obtained. To this end

this letter proposes an online learning-based probabilistic load forecasting method considering the impact of the data gap. Specifically

an adaptive ensemble backpropagation-enabled online quantile regression algorithm is developed to optimize the parameters of the attention network recursively using the newly obtained load observations. To further improve the reliability and sharpness of prediction intervals under significant data gaps

we introduce an online interval calibration technique. The proposed online learning method allows us to adaptively capture the dynamic changes in load patterns and alleviate the information lags caused by data gaps. Comparative tests utilizing real-world datasets reveal the superiority of the proposed method.