Improved model for the spatial load forecasting of the Slovenian distribution network

Abstract

In accordance with the EU directive (EU 2009/72/EC) at least 80% of consumers will have to be equipped with smart meters until 2020. Therefore, the distribution companies are currently massively replacing old Ferraris meters with the new AMI (Advanced Metering Infrastructure) meters. The analysis of metering data from smart meters allows a better understanding of the network conditions in all operating states and help accurately assess the load of existing and new consumers. The paper presents a new analytics application based on big data from smart meters. Using unsupervised machine learning methods of grouping (clustering), the daily load profiles can be determined from a large amount of input data. By examining the load probability distribution in each cluster, consumers' stochastic models are made. The original daily load profiles are reproduced by using the Monte Carlo method, which allows very accurate analysis of LV and MV networks. The results obtained are used for spatial load forecasting. The paper briefly presents how it all fits together to evaluate the future load development for the entire considered area.