Self-paced Live online

Python for Energy — Advanced

Build predictive models for energy prices, detect market anomalies, and deploy ML pipelines. AI agents as programming assistants, scikit-learn, SHAP, and end-to-end ML engineering.

Jesús López Jesús López
13.5 Hours5 Sessions8 Modules
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New edition coming soon — April 14, 2026

What you'll learn

AI programming agents

Use LLMs as technical copilots for code generation, optimization and validation

Predictive models

Random Forest, Gradient Boosting and time series applied to day-ahead market prices

Pattern detection & anomalies

Clustering, Isolation Forest and correlation analysis on market data

ML in production

Export pipelines, automate daily predictions, integrate with PostgreSQL

Course curriculum

11 modules 38 lessons
Install scikit-learn, SHAP and dependencies
Verify ML environment
Demo: price prediction model 30 min
Demo: anomaly detection in market data 30 min
Environment verification 30 min
What is an LLM and how it works
Using AI for code generation and optimization
Crafting technical prompts and best practices
Validating AI-generated results
Practice: generate a deviation calculation function with AI
ML applied to energy: price and deviation prediction
Train/test split and evaluation metrics
Temporal features, lags and rolling means
Linear Regression baseline
Random Forest for price prediction
Gradient Boosting (conceptual + practical)
Metrics: MAE, RMSE, R²
Building reproducible pipelines
Automated preprocessing in pipelines
Practice: end-to-end pipeline for day-ahead prices
Feature importance
SHAP: explain individual predictions
Sensitivity to key variables: forward price, renewables, demand
Overfitting and underfitting
Saving trained models with joblib
Automating daily predictions
Integration with PostgreSQL and Excel export
Hour, day, month features
Lags and rolling means
Conceptual introduction to ARIMA
Correlation matrix, multicollinearity and heatmaps
K-Means: day types, consumption patterns, price behavior
Anomaly detection with Isolation Forest
Practice: detect atypical days and anomalous prices
Data extraction and feature engineering
Model training and evaluation
Interpretation and SHAP analysis
Excel export: predictions, error estimates, key variables

Who is this course for?

Energy professionals

Looking to apply Machine Learning to market data and forecasting

Analysts

Who want to build predictive models for prices and renewable production

Teams

Needing to automate predictions and deploy models in production

Prerequisites

Requires basic Python knowledge

Completion of the basic course or equivalent knowledge of Python, pandas and PostgreSQL Required
A laptop with admin access to install software Required

Frequently asked questions

What's included

Dedicated PostgreSQL database

With real energy market data from OMIE and e·sios

Interactive notebooks

Exercises and solutions for each module

Session recordings

Access recordings to review at your own pace

AI agents as copilots

Use LLMs for code generation, optimization and validation

Price

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Your instructor

Jesús López 54,000+

Jesús López

Instructor & Tool Builder

Jesús López builds tools that automate daily tasks—from processing thousands of Excel files to organizing entire projects. With over 54,000 students on LinkedIn Learning, he has a passion for teaching others how to create their own solutions, no programming background needed.

54,000+ students · 3 LinkedIn Learning courses

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