What is the SOTA technique for forecasting day-ahead and intraday market prices for electricity in Germany?

ConfRank+: Extending Conformer Ranking to Charged Molecules

⚡ AI-powered energy consumption prediction app. Flutter + FastAPI + ML. Reduce bills & carbon footprint . Smart energy optimization! 🌱

SynapticGrid is an AI-driven system designed to make cities more efficient, sustainable, and livable by optimizing smart energy grids, waste management, and traffic flow through IoT sensors, real-time data processing, and reinforcement learning algorithms. The modular platform continuously learns and improves, helping urban environments

Pytorch implementation of Alchemical Kernels from Phys. Chem. Chem. Phys., 2018,20, 29661-29668

Pipeline complet de prédiction de consommation électrique. Multiples modèles (ML, Deep Learning, séries temporelles) avec interface Streamlit et optimisation d'hyperparamètres.

This Python project demonstrates real-world AI solutions across multiple domains: motion detection using OpenCV, environmental monitoring with anomaly detection, energy consumption forecasting, and predictive maintenance for machinery. The system integrates Streamlit dashboards for interactive visualization and allows users to upload custom dataset

Predicting electricity demand using LSTM and Random Forest models. A Comparative study with load & weather data

Geospatial + ML pipeline for identifying and ranking university campus rooftops for solar PV deployment — XGBoost energy prediction (R²=0.944) + AHP-TOPSIS multi-criteria ranking across 135 buildings at the University of Lagos.

Physics-guided machine learning for predicting molecular SCF energies at B3LYP level. Achieves <1 second predictions with 2.8 kcal/mol MAE by embedding Hückel theory, ZPVE modeling, and solvation corrections into a 4-stage gradient boosting ensemble. Trained on QM9, generalizes to drug-like molecules.