Harvard CS109: A predictive model for electricity prices in the midwest, and more specifically, the prices of nodes where nuclear plants are located
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Updated
Dec 11, 2015 - Jupyter Notebook
Harvard CS109: A predictive model for electricity prices in the midwest, and more specifically, the prices of nodes where nuclear plants are located
Python package to import data from OMIE (Iberian Peninsula's Electricity Market Operator): https://www.omie.es/
Code and experiments related to the paper: 'An adaptive standardisation methodology for Day-Ahead electricity price forecasting'
Electricity Price Forecasting — ML methodology (HistGBT + LightGBM + XGBoost ensemble with quantile loss)
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.
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).
Hack Cambridge 2023: Web-app to help decide whether to heat their home with gas or electricity
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