Electricity Demand Forecaster
Tap into RNNs to nail super-accurate electricity demand predictions.
Project Description
This project rocks Recurrent Neural Networks (RNNs) like LSTM and GRU to forecast electricity demand with serious precision.
Trained on historical demand data, mixed with weather and calendar features for max accuracy.
Perfect for showcasing how RNNs can level up energy management with killer temporal insights.
Key Features
RNN Powerhouse
LSTM and GRU models crush it at capturing time-based patterns.
Loaded Dataset
Blends historical demand, weather, and calendar data for sharp predictions.
Energy Game-Changer
Delivers spot-on forecasts for real-world energy planning.
Technical Details
Python
Versatile programming language for web development, data science, and automation
PyTorch
Deep learning framework with dynamic computation graphs
RNN
Recurrent Neural Networks for sequence prediction tasks
LSTM
Long Short-Term Memory networks for time series and sequence data
GRU
Gated Recurrent Units for efficient sequence modeling