Daily electricity revenue forecasting is of great value for electricity tariff management as well as electricity supply assurance. To enhance the timeliness of the samples used for electricity tariff forecasting and to quantify the uncertainty of the forecasting results, an adaptive hybrid interval forecasting model based on a rolling time window was proposed. Firstly, the rolling time window length was set, and the proposed combined data preprocessing strategy of improved variational mode decomposition-fuzzy entropy (IVMD-FE) was applied to decompose and reconstruct the tariff data, so as to achieve efficient and accurate extraction of tariff data features. On this basis, the maximum and minimum values of each reconstructed sequence was employed using fuzzy information particles for interval construction. Finally, the optimization algorithm echo state network (WOA-ESN) model was applied to predict irregular sequences, and the autoregressive integrated moving average model (ARIMA) was used to predict regular sequences, and the tariff intervals were dynamically obtained by updating the data in the rolling time window.The results show that the proposed model obtains electricity rate prediction intervals with excellent coverage and interval width, which can accurately cover the actual electricity revenue and provide more valuable decision-making information for electricity rate recovery.