Geospatial Forecasting of Electric Energy in Distribution Systems Using Segmentation and Machine Learning with Convolutional Methods - Héctor Chávez; Yuri Molina (Energies)
This paper proposes an innovative methodology for geospatial forecasting of electrical demand across various consumption segments and scales, integrating machine learning and discrete convolution within the framework of global system projections. The study was conducted in two phases: first, machine learning techniques were utilized to classify and determine the relative growth of segments with similar consumption patterns. In the second phase, convolution methods were employed to produce accurate spatial forecasts by incorporating the influence of neighboring areas through a “core matrix” and accounting for geographical constraints in regions with and without consumption. The proposed approach enhances the precision of spatial forecasts, making it suitable for large-scale distribution systems and implementable within short timeframes. The proposed method was validated using data from a Peruvian distribution system serving over one million users, employing 204 historical records and analyzing three georeferenced consumption segments at scales of 1:10,000, 1:1000, and 1:100. The results demonstrate its effectiveness in forecasting across different time horizons, thereby contributing to improved planning of electrical infrastructure.