pv magazine Australia

Guided-learning model for PV power forecasting without irradiance sensors

South Korean researchers have developed a guided-learning framework that accurately predicts PV power without requiring ...
GitHub

Prometheus Time Series Anomaly Detection with LSTM Autoencoder

This project implements a system for detecting anomalies in time series data collected from Prometheus. It uses an LSTM (Long Short-Term Memory) autoencoder model built with TensorFlow/Keras to learn ...
IEEE

Time-Series Forecasting of Energy Consumption Using LSTM Networks for Optimized Microgrid Management

Abstract: Accurate energy consumption forecasting is crucial for the efficient management of microgrids, especially as renewable energy sources become increasingly integrated into power systems. In ...