Python package to import data from OMIE (Iberian Peninsula's Electricity Market Operator): https://www.omie.es/

Harvard CS109: A predictive model for electricity prices in the midwest, and more specifically, the prices of nodes where nuclear plants are located

Code and experiments related to the paper: 'An adaptive standardisation methodology for Day-Ahead electricity price forecasting'

Reproduction code for the paper on online multivariate distributional regression for electricity price forecasting

This repository implements multiple deep learning models to forecast day-ahead electricity prices in the Germany-Luxembourg bidding zone, leveraging data from 15 European bidding zones.

InCommodities Case Crunch 2026. Hourly day-ahead electricity price forecasting (FR + UK) with CatBoost/LightGBM/XGBoost/DNN ensemble, residual stacking, STL target decomposition, and dual seasonal calibration.

pull PSE data into Python

Hack Cambridge 2023: Web-app to help decide whether to heat their home with gas or electricity

Developed and compared models to forecast hourly electricity load and prices using over nine years of real-world German market data, spanning linear methods (AR, OLS) and machine learning algorithms (Random Forests, Regression Trees).

Degradation-aware robust MPC for battery arbitrage under electricity price forecasts.

Probabilistic day-ahead LMP forecasting for 8–12 PJM zones using Temporal Fusion Transformer and a frozen semantic-embedding fusion branch. Produces P10/P50/P90 hourly forecasts, attention and variable-importance visualizations, and fusion-gate diagnostics.