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A data driven scenario-based approach is applied to predict the adoption and expected local penetration levels of photovoltaic (PV) installations in an actual distribution network area in the Netherlands. Local PV adoption probabilities are scaled according to a trained statistical model. Integration of this model in the scenarios is shown to provide a significant improvement in prediction accuracy. In addition, a probabilistic forecast is simulated highlighting the local impact on the electricity network for several future scenarios.

Application and evaluation of a probabilistic forecasting model for expected local PV penetration levels