Analysis of local demand trends and forecasting through weather correction and benefit to DSO transistion and microgrids Analysis of local demand trends and forecasting through weather correction and benefit to DSO transition and microgrids

Abstract

This paper presents the methodology for separating the effect of weather patterns and customer behaviour on peak demand at a local, primary substation level. It uses real data from SP Energy Networks (SPEN), a UK Distribution Network Operator (DNO), and a demand model developed by Digital Engineering Limited. The innovation presented in this paper is the use of Numerical Weather Prediction Models and Machine Learning techniques to undertake weather correction at a local level. This has benefits for network planning, flexibility in distribution networks and the development of microgrids.