Data Model Design for Explainable Machine Learning-based Electricity Applications

The transition from traditional power grids to smart grids, significant increase in the use of renewable energy sources, and soaring electricity prices has triggered a digital transformation of the energy infrastructure that enables new, data driven, applications often supported by machine learning models. However, the majority of the developed machine learning models rely on univariate data. To date, a structured study considering the role meta-data and additional measurements resulting in multivariate data is missing. In this paper we propose a taxonomy that identifies and structures various types of data related to energy applications. The taxonomy can be used to guide application specific data model development for training machine learning models. Focusing on a household electricity forecasting application, we validate the e ff ectiveness of the proposed taxonomy in guiding the selection of the features for various types of models. Finally, using a feature importance techniques, we explain individual feature contributions to the forecasting accuracy.1. Introduction The transition from traditional power grids to smart grids, significant increase in the use of renewable energy sources, and soaring electricity prices has led to an increase in complexity [1], particularly with the adoption of smart meters (SMs), energy management systems (EMSes), and intelligent electronic devices (IEDs) at the low voltage (L V) level. These devices enable innovative energy [2] and non-energy applications [3, 4], such as energy cost optimization and matching consumption with self-production from renewable energy sources. On the distribution system operator (DSO) side of the L V grid, reliability and latency are the main challenges, and complete ob-servability of the L V grid for each substation is crucial.