User-Centered Visual Analytics Approach for Interactive and Explainable Energy Demand Analysis in Prosumer Scenarios

Abstract

As part of the energy transition, the spread of prosumers in the energy market requires utilities to look for new approaches in managing local energy demand and supply. Doing this effectively requires better understanding and managing of local energy consumption and production patterns in prosumer scenarios. This situation is particularly challenging for small municipal utilities who traditionally do not have access to sophisticated modeling and forecasting methods and solutions. To this end, we propose a user-centered and a visual analytics approach for the development of a tool for an interactive and explainable day-ahead forecasting and analysis of energy demand in local prosumer environments. We also suggest supporting this with behavioral analysis to enable the analysis of potential relationships between consumption patterns and the interaction of prosumers with energy analysis tools such as customer portals, recommendation systems, and similar. In order to achieve this, we propose a combination of explainable machine learning methods such as kNN and decision trees with interactive visualization and explorative data analysis. This should enable utility analysts to understand how different factors influence expected consumption and perform what-if analyses to better assess possible demand forecasts under uncertain conditions.