A Scalable Load Forecasting System for Low Voltage Grids

Abstract

A recent research trend is driven to increase the monitoring and control capabilities of low voltage networks. This paper describes a probabilistic forecasting methodology based on kernel density estimation and that makes use of distributed computing techniques to create a highly scalable forecasting system for LV networks. The results show that the proposed algorithm outperforms three benchmark models (one for point forecast and two for probabilistic forecasts) and demonstrate the applicability of the distributed in-memory computing solution for a practical operational scenario. The ultimate goal is to integrate information about net-load forecasts in power flow optimization frameworks for low voltage networks in order to solve technical constraints with the available home energy management system flexibility.