Accurate load forecasting is a critical step for power system generation planning. Contingency parameters of the system and their dynamic characteristics should be taken into account for load forecasting purpose. In this paper, a probabilistic load forecasting algorithm considering contingency parameters is developed for the peak load forecasting. Using Anderson-Darling test toolbox in MATLAB and the historical data, the probabilistic distribution of the contingency parameters can be determined. In a case study, the Monte-Carlo simulation is run to forecast load demand and generation scenarios of Bangladesh based on the developed adaptive algorithm and the calculated probabilistic distribution. The influence of contingency parameters is evaluated using Bayesian network in a sensitivity study.