Python package for working with demand-side grid projects, datasets and queries
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Updated
Oct 30, 2026 - Python
Python package for working with demand-side grid projects, datasets and queries
Life Cycle Assessment of Technical Building Services of Large Residential Building Stocks using Semantic 3D City Models
🔋 Time Series Forecasting for Energy Demand Spike Prediction 🔋
A rigorous benchmark of 11 time-series forecasting models on hourly energy demand data — from a seasonal naive baseline to gradient-boosted trees, deep recurrent networks, and a stacking ensemble.
Code written for a co-op term on Integrated Assessment Models
LSTM-based hourly electricity demand forecasting model with 3.95% MAPE, using PGCB data to enable proactive grid management.
Dashboard and data processing routines for European green fuel demand projections developed in the PtX Markets Project
This project seeks to analyze the behavior of solar energy generation in Spain and how this generation is affected by various climatic/atmospheric variables.
End-to-end time series analysis of hourly electricity consumption across 4,400+ London households using smart meter data
A module to prepare demand profiles for Europe at arbitrary spatial resolution, based on ENTSO-E historical load data.
Interactive dashboard displaying the latest trends in publicly reported data center energy use.
En este proyecto se busca analizar el comportamiento de la generación de energía solar en España y como dicha generación es afectada por diversas variables climáticas/atmosféricas.
Academic AI/Data Science project analysing the relationship between climate variables and electricity demand.
AI-powered electricity load forecasting and grid demand planning for Southern California. SARIMA time series forecasting, EV adoption and solar penetration scenario builder, and executive capacity planning dashboard.
30-day hourly electricity demand forecasting on PJM West data. Compares ARIMA, SARIMAX, Prophet, Random Forest & XGBoost, with a Streamlit dashboard. Includes honest recursive back-testing.
pull PSE data into Python
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