To improve decision-making during contingencies in power systems and reduce the impact while a failure is repaired, it is necessary to know the consequences of losing different elements of the system. These consequences need to consider the current system conditions and the short-term forecasting of the demand. Hence, this work proposes a procedure to run N-1 contingencies by using load forecasting obtained from historical records. This allows to know the assets in the system that present overloads or voltages outside of the security limits and can affect the security operation of system in a horizon of 24 h. To validate the procedure, the IEEE transmission system of 39 buses was used. To forecast the load, a script in Python using historical load profiles was implemented. To generate the list of assets with violations during contingencies, the pandapower module of Python was used in order to run the load flow. As a result, a list of affected assets was obtained per each hour of the day when lines and transformers were disconnected.