Combining Machine Learning with Visual Analytics for Explainable Forecasting of Energy Demand in Prosumer Scenarios

Abstract

Abstract This paper presents the design and a prototypical implementation of a tool for short-term energy demand forecasting in prosumer scenarios in local energy systems. The prototype combines explainable machine learning and visual analytics to facilitate the forecasting and analysis of energy demand and supply in a way usable for small utilities not used to complex analytical approaches. It applies a kNN (k-Nearest Neighbour) algorithm to forecast energy demand and supply and presents the results in an interactive visual dashboard that allows the user to analyze different forecast alternatives, to understand how they relate to the input parameters and identify consumption and production patterns. Initial assessment of the forecasting accuracy (MAPE 5.77%) on a limited dataset and the feedback from target users support the feasibility of the proposed approach.