A high-performance data pipeline for processing and analyzing smart meter energy consumption data. The application ingests raw meter readings, performs data cleaning and transformation, applies anomaly detection algorithms, and generates insightful visualizations for energy consumption patterns.
Real-time data ingestion from multiple meter formats
Automated data cleaning and validation pipelines
Statistical anomaly detection using Z-score and IQR methods
Time-series analysis for consumption pattern identification
Interactive dashboards with Matplotlib and Plotly
Automated report generation with consumption insights
Performance optimization using NumPy vectorization
Achievements
Built data pipeline processing 10,000+ daily meter readings
Implemented anomaly detection algorithms for usage patterns
Created visualization dashboards for consumption trends
Optimized data processing reducing runtime by 60%
Technical Challenges
Processing large volumes of time-series data efficiently
Implementing accurate anomaly detection with seasonal variations
Optimizing memory usage for datasets exceeding available RAM
Creating meaningful visualizations from complex multi-dimensional data
Handling missing data and meter reading errors gracefully
System Architecture
The pipeline uses Pandas for data manipulation, NumPy for numerical computations, and Matplotlib for visualizations. Data flows through stages: ingestion, validation, transformation, analysis, and visualization. The application uses chunking for large datasets and implements caching for frequently accessed computations.
Gallery
Real-time energy consumption dashboard showing £0 cost for both electricity and gas usage with green status indicators
