Machine learning for smart energy systems.

  • Celeita Rodriguez, David Felipe (PI)

Project: Research Project

Project Details


The main objective is to identify feasible machine learning methods that could potentially improve maintenance practices, where the data will lead to successful predictive decisions consistent with upgrades.

Standardization. In this sense, the following results are expected:

 Develop a proof of concept based on IEE 3007 and machine learning methods to expand maintenance practices for industrial and commercial power systems.

 Provide a useful taxonomy where the new generation of engineers can quickly visualize.

Techniques to take advantage of a large volume of data in electrical installations.

 Design and implement a multipurpose interface based on the cloud, which allows the evaluation of different scenarios related to predictive and proactive maintenance for a case study (IEEE 3007 - Board 480 V)

 Foster interdisciplinary efforts with early student engagement in technical, practical, and fundamental concepts that have been in the outreach priority of many IAS committees.

Commitments / Obligations

(2) Proceedings ICPRE 2022

(3) proceedings IEEE 2022

(1) proceedings IEEE 2023

(1) Artículo de revista – 2023
Effective start/end date2/1/232/1/24

UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):

  • SDG 8 - Decent Work and Economic Growth

Main Funding Source

  • Competitive Funds
  • Starter Funds


  • Bogotá D.C.


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