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Characterization of Demand Profiles for Medium-Voltage Substations in Bogotá Using a Python Interface

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Bibliographic Details
Title: Characterization of Demand Profiles for Medium-Voltage Substations in Bogotá Using a Python Interface
Authors: Herrera Briñez, María Camila, Montoya Giraldo, Oscar Danilo, Gil-González, Walter Julián
Source: Statistics, Optimization & Information Computing; Vol 14 No 5 (2025); 2663-2687 ; 2310-5070 ; 2311-004X
Publisher Information: International Academic Press
Publication Year: 2025
Collection: International Academic Press (IAPress)
Subject Terms: Operational Demand Forecast, Python Script, Power Demand Analysis, Data Processing Workflow, Colombian National Interconnected System
Description: This document presents a comprehensive methodology for analyzing operational demand forecast data from XM’s website using a Python script executed in Google Colaboratory (Colab). The script is designed to process text files containing forecast data, which are compressed in a zip file and uploaded to the Colab environment for processing. The key functions of the script include decompressing and organizing files, filtering data based on specified keywords, calculating average power values, and normalizing these averages. The script is structured into several sections: first, it describes various libraries and functions used, and then it presents a main script that implements these functions. Finally, the obtained vectors are utilized to plot normalized active and reactive curves, facilitating the visualization of demand patterns over a 24-hour period. This provides insights into the operational dynamics of Colombia's National Interconnected System (SIN). The analysis focuses on forecast data from May 2023 to May 2024, which includes both reactive and active power. The integration of this script into the data processing workflow aims to streamline the analysis of XM’s operational demand forecasts, supporting more informed decision-making for energy management and planning. By transforming raw forecast data into actionable insights, this approach improves understanding of demand patterns, contributing to the effective management of Colombia’s energy resources.
Document Type: article in journal/newspaper
File Description: application/pdf
Language: English
Relation: http://www.iapress.org/index.php/soic/article/view/2454/1394; http://www.iapress.org/index.php/soic/article/view/2454
DOI: 10.19139/soic-2310-5070-2454
Availability: http://www.iapress.org/index.php/soic/article/view/2454
https://doi.org/10.19139/soic-2310-5070-2454
Rights: Copyright (c) 2025 Statistics, Optimization & Information Computing
Accession Number: edsbas.647EF462
Database: BASE
Description
Abstract:This document presents a comprehensive methodology for analyzing operational demand forecast data from XM’s website using a Python script executed in Google Colaboratory (Colab). The script is designed to process text files containing forecast data, which are compressed in a zip file and uploaded to the Colab environment for processing. The key functions of the script include decompressing and organizing files, filtering data based on specified keywords, calculating average power values, and normalizing these averages. The script is structured into several sections: first, it describes various libraries and functions used, and then it presents a main script that implements these functions. Finally, the obtained vectors are utilized to plot normalized active and reactive curves, facilitating the visualization of demand patterns over a 24-hour period. This provides insights into the operational dynamics of Colombia's National Interconnected System (SIN). The analysis focuses on forecast data from May 2023 to May 2024, which includes both reactive and active power. The integration of this script into the data processing workflow aims to streamline the analysis of XM’s operational demand forecasts, supporting more informed decision-making for energy management and planning. By transforming raw forecast data into actionable insights, this approach improves understanding of demand patterns, contributing to the effective management of Colombia’s energy resources.
DOI:10.19139/soic-2310-5070-2454