Based on a previously detailed data mining about primary renewable sources concerning solar radiation and wind speed, the authors present some results related to reliability of supply using Bayesian networks (BNs). The paper focuses on how the structure and parameters of BN can be obtained from the data. A short review of learning methods, scoring function types and specific algorithms is included. The case study includes details about Necessary Path Condition (NPC) algorithm as well the reliability of supply (LOLP) with respect of a given load node of the power network considered. The input data are the result of the systematic measurements of meteorological parameters and their conversion to generated power while the load profile is according to that attached to RTS system. The analysis and calculations were performed using Hugin Expert software.