IEEE

A Hybrid CNN-LSTM Model with Signal Decomposition for Multi-Step Electrical Load Forecasting

Abstract: Accurate power load forecasting is a cornerstone for the reliable operation and economic dispatch of modern power grids, particularly as the integration of Variable Renewable Energy ...
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

A Short-Term Photovoltaic Power Forecasting Method Based on Meteorological Data Using GRF ...

Abstract: This paper proposes a short-term photovoltaic power forecasting method based on a Genetic Algorithm-Random Forest (GRF)-LSTM-XGBoost hybrid model, aimed at improving accuracy by addressing ...