Sustainable and smart distribution networks

Abstract

Increasing distributed generation of and demand for electrical energy results ever more in problems like congestion. Forecasting the demand, photovoltaic power generation and the number of electric vehicles connected to charging station for a residential neighbourhood can be an important part of a smarter distribution network for such a neighbourhood and can thereby increase the usage of renewable energy. In this thesis an overview of the design steps for making such a forecasting system is given and the steps are applied to a fictional dutch residential neighbourhood of the future. Some key findings are that for accurately forecasting the load, the temperature and time are the most important features. For accurately forecasting the PV power generation, especially the irradiation is most important, but time, horizontal view and humidity are also important features. Furthermore, it is shown that random forest regression models can accurately forecast both the demand and PV power generation with an accuracy above 90%. Artificial neural networks are also adequate models for the forecasting problems, but because they are harder to understand and not necessarily better, it is recommended to start with random forest regressors before making neural networks. Support vector machines seem less suitable for these particular forecasting problems.