Particle filtering (PF) is an eminent simulation-based state estimation technique, which is capable of handling massive data sets and diverse external factors. Here, an effective selection of the importance density plays a pivotal role in performances of PFs by not only preventing degeneracy problems at early stages of process, but also taking both the transition prior and likelihood into account when the likelihood appears in the tail of the prior. To this end, we propose a novel importance density selection scheme for PF based on the minimum relative entropy principle. Theoretical derivation of the proposed scheme is presented, and its effectiveness is evaluated against various sampling schemes that exist in the literature using synthetic experiments. Finally, the performance of the proposed minimum relative entropy-based density selection scheme is successfully demonstrated for short-term electricity demand forecasting of a company located in Miami, FL, USA. Note to Practitioners-This paper was motivated by the necessity of reducing utility costs in terms of energy consumption in large-scale enterprises. Determination of the lowest cost method of providing the required amount of electricity heavily relies on accurate load demand forecast. In addition, demand forecasting problems also exist in various fields, including supply chain management, manufacturing, and microgrid networks. This paper provides an eminent approach to estimate the posterior state (i.e., demand in forthcoming periods) given the prior states (i.e., past demand) and measurements. In this paper, we mathematically derive the method using the minimum relative entropy principle. To validate the proposed method, a series of synthetic experiments are designed, and a case study of short-term electricity demand forecasting is carried out. The results obtained from the proposed method have been benchmarked against those obtained from four other approaches that exist in the literature. In future research, we will extend the application scope of this paper to tracking in naval radar systems and state estimation in distributed power systems with renewables.