Python for Renewable Energy Data Processing: An Extensive Online Short Course for Geoscientists and Engineers

Instructor: Dr Roderick Perez Altamar
Language: English
Level: Foundation
Duration and formats:
Online: Extensive Course - self-paced study + 4 live sessions of 1 hour
Classroom: 1 day CPD Points: 5

Course Description

Python for Renewable Energy Data Processing is a comprehensive and hands-on online short course designed specifically for geoscientists and engineers who are interested in harnessing the power of Python programming for renewable energy data analysis. This foundational course provides participants with the necessary knowledge and practical skills to process and analyze renewable energy data using Python.

The course begins with an introduction to renewable energy sources such as hydroelectric, eolic, solar, and geothermal, allowing participants to understand the fundamental principles and applications of each. Next, participants are introduced to the Python programming language and its environment. They learn about essential data structures, functions, and modules, as well as explore popular libraries like Numpy, Pandas, and Matplotlib that are widely used in data analysis.

Throughout the course, participants engage in hands-on exercises that gradually build their proficiency in Python. They apply their newly acquired skills to perform basic statistical analysis, conduct exploratory data analysis (EDA), and create visualizations using real-world energy datasets. Each topic is accompanied by informative videos and presentations with narration, ensuring a comprehensive learning experience.

The course is divided into specific modules dedicated to different renewable energy sources. Participants delve into wind energy, hydroelectric energy, eolic energy, and geothermal energy. They learn how to load and visualize data related to each energy source using Python, gaining valuable insights into the unique characteristics and challenges associated with each type.

To assess their understanding and progress, participants complete quizzes at the end of each chapter. These quizzes are automatically checked, providing immediate feedback on their performance. Additionally, practical assignments are provided, enabling participants to apply their knowledge and reinforce their skills.

An essential aspect of this course is the dedicated sessions where participants can interact with the instructor in real-time webinars. This allows them to seek clarification, ask questions, and receive expert guidance on the practical application of Python programming in renewable energy data processing.

Course Objectives

Upon completion of this extensive online short course, geoscientists and engineers will have gained a solid foundation in Python programming for renewable energy data processing. They will be equipped with the skills and knowledge to effectively analyze and visualize energy datasets, making informed decisions and contributing to the advancement of renewable energy technologies.

Course Outline

Module 1: Introduction to Renewable Energy

  • Introduction to Renewable Energies and applications
    • Hydroelectric
    • Eolic
    • Solar
    • Geothermal

Module 2: Introduction to Python Programming and its environment

  • Data Structures
    • Functions and modules
    • Introduction to Libraries: Numpy, Pandas, Matplotlib

Module 3: Data Processing and Analysis

  • Creating Visualizations using Energy Dataset using Matplotlb, and Seaborn
    • Introduction to Renewable Energy Data
    • Basic Statistical Analysis using Python
    • Exploratory Data Analysis (EDA)
    • Time series and seasonal data

Module 4: Advance Data Visualization Tools

  • Advanced map visualizations using from geopandas, choropleth maps, and Folium

Module 5: Wind Energy

  • Principles of Eolic energy generation
  • Review of the installed energy capacity in the world
  • Visualize wind patterns in a map.
  • Calculate and plot the potential energy as a time series.

Module 6: Hydroelectric Energy

  • Principles of hydroelectric generation
  • Plot the river location in a map
  • Calculate and plot the flood flow for a river

Module 7: Solar Energy

  • Principles of Solar Energy radiation
  • Calculate the potential energy generation of a solar panel
  • Plot the theoretical and real energy radiation as a time series

Module 8: Geothermal Energy

  • Principles of geothermal energy
  • Plot the geothermal gradient of the earth in a map
  • Load and plot the temperature log in a well log

Throughout the course, participants will engage in hands-on exercises, practical assignments, and real-world projects to reinforce their learning and apply data science techniques to solve problems in both the oil and gas and renewable energy domains. The course will bridge the gap between traditional geoscience and engineering practices and the evolving field of data science, empowering participants to leverage data-driven.

Participants’ Profile

This course is intended for geoscientists and engineers who are interested in renewable energy data processing and analysis. Participants should have a basic understanding of programming concepts and familiarity with data analysis principles. Prior knowledge of Python programming is helpful but not mandatory, as the course provides an introduction to Python and its libraries. Participants should also have an interest in renewable energy sources and a desire to apply Python programming skills to analyze and visualize energy datasets.

Prerequisites

  • Basic understanding of programming concepts: Participants should have a general understanding of programming principles such as variables, loops, conditionals, and functions.
  • Familiarity with data analysis principles: It is beneficial for participants to have a basic knowledge of data analysis concepts such as descriptive statistics, data manipulation, and visualization.
  • Basic knowledge of Python (optional): While not mandatory, prior familiarity with Python programming will be helpful in grasping the course content more effectively.
  • Interest in renewable energy sources: Participants should have an interest in renewable energy and a desire to learn how to process and analyze data related to hydroelectric, solar, wind, and geothermal energy sources.

Note: This course is designed to cater to participants with a range of programming backgrounds, including beginners. However, a basic understanding of programming concepts and data analysis principles will enhance the learning experience.

About the Instructor

Dr. Roderick Perez is a highly accomplished and versatile professional with a strong background in geophysics, geology, and data science. With over 15 years of experience in the oil and gas industry, Dr. Perez has established a stellar reputation for expertise in seismic interpretation and a keen interest in applying artificial intelligence (AI) techniques to energy applications.

Dr. Perez holds a Bachelor’s degree in Geophysical Engineering from Universidad Simon Bolivar in Venezuela, followed by a Master’s degree in Geology and a Ph.D. in Geophysics from the University of Oklahoma in the United States. Demonstrating a commitment to continuous learning and professional growth, he pursued an MBA from Universidad de los Andes in Colombia and is currently finishing a Master’s degree in Data Science from the University of Vienna in Austria.

Throughout his career, Dr. Perez has made significant contributions to the industry, authoring numerous technical papers in the development of unconventional reservoirs and receiving accolades such as the Best Technical Paper Award in the AAPG-SEG Interpretation Journal in 2015. He has held key positions at reputable organizations, including Gutmann Bank, where he currently works as a Software Developer, and Wattle Petroleum, where he served as a Geoscience Consultant. His previous roles as VP Geoscience at Scientia Group, Seismic Interpretation Specialist at Pacific Rubiales, and Oil and Gas Expert at DrillingInfo have further enriched his expertise.

Driven by a passion for innovation, Dr. Perez’s current research focuses on the application of Physical Informed Neural Networks (PINNs) and Fourier Neural Operators (FNO) in Reservoir Energy Simulation for geothermal, carbon capture, utilization, and storage (CCUS), and hydrogen storage. He also explores the potential of Generative AI in the renewable energy sector and weather forecasting.

Dr. Perez’s multidisciplinary background, technical prowess, and commitment to advancing the field make him an asset in the intersection of petroleum geosciences, data science, and AI.