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The electricity consumption behavior of the inhabitants is a major contributor to the uncertainty of the residential load system. Human-caused uncertainty may have a distributional component, but it is not well understood, which limits further understanding the stochastic component of load forecasting. This study proposes a short-term load-interval forecasting method considering the stochastic features caused by users' electricity consumption behavior. The proposed method is composed of two parts: load-point forecasting using singular spectrum analysis and long short-term memory (SSA-LSTM), and load boundaries forecasting using statistical analysis. Firstly, the load sequence is decomposed and recombined using SSA to obtain regular and stochastic subsequences. Then, the load-point forecasting LSTM network model is trained from the regular subsequence. Subsequently, the load boundaries related to load consumption consistency are forecasted by statistical analysis. Finally, the forecasting results are combined to obtain the load-interval forecasting result. The case study reveals that compared with other common methods, the proposed method can forecast the load interval more accurately and stably based on the load time series. By using the proposed method, the evaluation index coverage rates (CRs) are (17.50%, 1.95%, 1.05%, 0.97%, 7.80%, 4.55%, 9.52%, 1.11%), (17.95%, 3.02%, 1.49%, 5.49%, 5.03%, 1.66%, 1.49%), (19.79%, 2.79%, 1.43%, 1.18%, 3.37%, 1.42%) higher than the compared methods, and the interval average convergences (IACs) are (−18.19%, −8.15%, 3.97%), (36.97%, 21.92%, 22.59%), (12.31%, 21.59%, 7.22%) compared to the existing methods in three different counties, respectively, which shows that the proposed method has better overall performance and applicability through our discussion. [ABSTRACT FROM AUTHOR]