Exploring regression models to enable monitoring capability of local energy communities for self‐management in low‐voltage distribution networks

Abstract

Abstract This study proposes a data‐driven approach to enable the self‐management capability of local energy communities (LECs) via transformer congestion monitoring in low‐voltage distribution networks. A set of regression models is adopted in this approach, while the data from residential smart meters (SMs) is leveraged. Four machine learning algorithms, namely ridge regression, support vector regression, random forest regression (RFR) and eXtreme gradient boosting regression (XGBR), are compared to select the best‐performing regression model using a cross‐validation method. A comprehensive framework is provided to facilitate comparison of the algorithm, consisting of data pre‐processing, model fitting and validation, and model deployment. A thorough analysis is also SMs' measurements. The obtained results highlight that the regression‐based method can effectively estimate the transformer loading, that is with the Pearson correlation coefficient R and root mean square error calculated for the real values and the estimated values of around 0.98 and 0.87, respectively, by using only a limited set of SM measurements (5 out of 21 SMs used) provided by the LECs while preserving customers' privacy rights. Among the examined algorithms, the XGBR algorithm appears the best method as it achieves adequate accuracy at significantly less simulation time (i.e. one‐third of the simulation time of the RFR). By applying the proposed approach, the monitoring and self‐management capability of the LECs can be realised.

Keywords

• learning (artificial intelligence)
• power system management
• smart power grids