Analysis of Electric Energy Consumption Profiles Using a Machine Learning Approach: A Paraguayan Case Study

Félix Morales,Carlos Sauer Ayala,Federico Divina,Federico Daumas-Ladouce,David Becerra-Alonso,Miguel García-Torres,Diego P Pinto-Roa,Francisco Gómez-Vela,Gustavo Velázquez,José Luis Vázquez Noguera,Julio César Mello-Román,Pedro E Gardel-Sotomayor

doi:10.3390/electronics11020267

Abstract

Correctly defining and grouping electrical feeders is of great importance for electrical system operators. In this paper, we compare two different clustering techniques, K-means and hierarchical agglomerative clustering, applied to real data from the east region of Paraguay. The raw data were pre-processed, resulting in four data sets, namely, (i) a weekly feeder demand, (ii) a monthly feeder demand, (iii) a statistical feature set extracted from the original data and (iv) a seasonal and daily consumption feature set obtained considering the characteristics of the Paraguayan load curve. Considering the four data sets, two clustering algorithms, two distance metrics and five linkage criteria a total of 36 models with the Silhouette, Davies–Bouldin and Calinski–Harabasz index scores was assessed. The K-means algorithms with the seasonal feature data sets showed the best performance considering the Silhouette, Calinski–Harabasz and Davies–Bouldin validation index scores with a configuration of six clusters.

Highlights

The identification of electric load profiles is of great interest for electric energy distribution network planners and operators [1] (DNOs)
Other important applications of electrical consumption clustering include the characterization of load curves in a real distribution system [7] and load profiling for tariff design and load forecasting or distribution planning [8]
In which electric energy consumption data from China were under analysis, the results suggest the competitiveness of the proposal for a forecasting purpose

Summary

Introduction

The identification of electric load profiles is of great interest for electric energy distribution network planners and operators [1] (DNOs). Operators often use deterministic and aggregated load models [3]. This approach is straightforward to apply and clear to assess. Despite the improvement with respect to the aggregated model, they require detailed knowledge or assumptions at an appliance level [5]. To overcome this problem, the clustering approach finds the best model according to the data. The clustering approach finds the best model according to the data In this approach, different electric characteristics are taken into consideration to generate the model [2]. Other important applications of electrical consumption clustering include the characterization of load curves in a real distribution system [7] and load profiling for tariff design and load forecasting or distribution planning [8]

Methods

Results

Conclusion