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Electrical distribution companies struggle to find precise estimations of energy demand for their networks. They have at their disposal statistical tools such as power load profiles, which are however usually not precise enough and do not take into account factors such as the presence of electrical heating devices or the type of housing of the end users. In this paper, we show how a genetic algorithm generated with the EASEA language can be successfully applied to solve a noisy blind source separation problem and create accurate power load profiles using real world data provided by "Électricité de Strasbourg Réseaux". The data includes load measurements of 20kV feeders as well as the energy consumption of more than 400,000 end users. The power load profiles obtained demonstrate considerable improvement in the estimation of load curves of 20kV feeders.