This project focuses on analyzing smart grid energy data from Advanced Metering Infrastructure (AMI). It applies data preprocessing, exploratory data analysis (EDA), machine learning, and GIS techniques to detect consumption anomalies, forecast energy demand, and visualize geospatial trends in a simulated smart grid environment.
Raw Data Ingestion → Preprocessing & Cleaning → Feature Engineering → Model Training → Forecasting → Geospatial Mapping
Core programming language
Data storage and querying
Data processing
Machine learning
Identified billing discrepancies and unusual consumption patterns
Segmented customers based on energy usage patterns
Accurate predictions of future energy consumption
Visualized consumption zones for utility planning
Apache Spark for streaming data processing
Autoencoders and LSTM for anomaly detection
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