IEEE

Mid-Term Load Forecasting with Minimal Data: An In-Context-Learning-Aware Approach Using Large Language Models

Abstract: Accurate mid-term load forecasting at the building level is vital for the strategic planning, operation, and sustainability of modern power systems. Machine learning approaches often require ...
IEEE

A Two-Stage Mixed Integer Programming Model for Distributionally Robust State-Based Non-Intrusive Load Monitoring

Abstract: This paper presents a non-intrusive load monitoring (NILM) model based on two-stage mixed-integer linear programming theory. Compared with other mixed integer optimization-based models, this ...
GitHub

GitHub - kossisoroyce/timber: Ollama for classical ML models. AOT compiler that turns XGBoost, LightGBM, scikit-learn, CatBoost & ONNX models into native C99 inference code ...

Timber takes a trained ML model — XGBoost, LightGBM, scikit-learn, CatBoost, ONNX (tree ensembles, linear models, SVMs), or a URDF robot description — runs it through a multi-pass optimizing compiler, ...