Smart Power Demand & Generation Prediction System
Overview
Designed a multi-region deep-learning system to forecast optimal power generation from environmental, temporal, and demand data. The system engineers features from weather patterns, renewable energy availability, and historical load data to provide accurate power generation predictions.
Key Features
- Feature Engineering: Extracted features from weather data, renewable availability, and historical load patterns
- Advanced Models: Implemented sequence models (Keras LSTM/GRU) and Transformer architectures
- Comprehensive Evaluation: Used MAE, RMSE, rolling-window validation and compared against ARIMA/regression baselines
- Production Deployment: Dockerized inference behind a REST API with experiment tracking for reproducibility
Technical Implementation
Architecture
- Sequence Models: LSTM and GRU networks for temporal pattern learning
- Transformer Models: Attention-based architectures for long-range dependencies
- Baseline Comparison: ARIMA and regression models for performance benchmarking
Technologies Used
- Framework: Keras, TensorFlow
- Deployment: Docker, REST API
- Evaluation: MAE, RMSE, rolling-window validation
- Tracking: Experiment tracking for reproducibility
Results
- Multi-region Forecasting: Accurate predictions across different geographical regions
- Model Comparison: Outperformed traditional ARIMA and regression baselines
- Production Ready: Deployed system with REST API for real-time inference
Impact
This system enables efficient power grid management by providing accurate generation forecasts, supporting the integration of renewable energy sources and optimizing power distribution across multiple regions.
Satyam Singh
Machine Learning Researcher | Neural Networks & Applied AI
Machine learning researcher focused on building efficient neural network architectures for real-world applications, spanning deep learning, computer vision, and NLP.