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In the traditional load flow method, the powers of all loads and generators have to be defined at the start of the calculation, and no uncertainty is considered. The results are exactly as they were specified. This is perfect in an ideal scenario but in reality, that is often not the case. Some of the powers are unknown/not measured, measurements are not exact due to sensor errors, and might represent the variables averaged over some time period. Further, in the load flow formulation no powers/currents through the branches can be specified explicitly.

Data driven loadflow, on the contrary, is flexible in this respect since it allows to use different types of measurements. It takes into account the available measurement data, and with the help of pseudo-measurements the gaps are filled with some variance associated so that after computation the most probable estimate of network state can be found out. In certain sense, this process can be seen as an extension of the load flow method, therefore state estimation can be also referred to as Data Driven Load Flow. For more information, see State Estimation / Data Driven Load Flow.