IEEE

Short-Term Active Power Forecasting in a Photovoltaic Plant Using TiDE and LSTM Models

Abstract: This work presents a deep learning approach for short-term forecasting of active power in photovoltaic (PV) plants operating within islanded microgrids. Three forecasting schemes were ...

UGA students are changing how Athens gets its weather forecast

UGA's Weather Dawgs use a high-resolution model to create localized forecasts for Athens, improving accuracy for residents.
IEEE

Explanation-Enhanced Federated Fine-Tuning of Large Language Models for Photovoltaic Power Forecasting

Abstract: Accurate photovoltaic (PV) power forecasting is essential for the reliable operation of modern power systems. While federated learning (FL) preserves data privacy across geographically ...
Surfline

How Surfline is Redefining Surf Forecast Accuracy -- And What Comes Next

Few things on Earth get us as excited as the topic of surf science. After all, we’ve built our business around it, and since pretty much everyone at Surfline surfs, we’re personally invested. So, like ...
Frontiers

An interpretable building air conditioning load forecasting framework using SSA-optimized Bi-LSTM and SHAP analysis

Short-term air conditioning load forecasting in industrial buildings plays a key role in energy management and carbon neutrality efforts. Yet internal disturbances, latent heat variations, and ...
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, ...
GitHub

Agentic RAG for Dummies

Step-by-step tutorial perfect for understanding core concepts. Start here if you're new to Agentic RAG or want to experiment quickly. 2️⃣ Building Path: Modular Project Flexible architecture where ...