Enhancing short‐term probabilistic residential load forecasting with quantile long–short‐term memory

In the study of load forecasting, short‐term (ST) load forecasting in the horizon of individuals is prone to manifest non‐stationary and stochastic features compared with predicting the aggregated loads. Hence, better methodologies should be proposed to forecast ST residential loads more accurately, and refined representation of forecasting results should be reconsidered to make the prediction more reliable. A format of ST probabilistic forecasting results in terms of quantiles is offered, which can better describe the uncertainty of residential loads, and a deep‐learning‐based method, quantile long–ST memory, to implement probabilistic residential load forecasting. Experiments are conducted on an open dataset. Results show that the proposed method overrides traditional methods significantly in terms of average quantile score.