⚡ NEM12 Data Processor

This Python script is designed to efficiently parse, transform, and process NEM12 CSV files into structured and analyzable formats using pandas. It supports multi-interval energy readings and prepares output files for deeper inspection and analysis.

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📌 Project Purpose

The NEM12 Data Processor streamlines the extraction of energy meter data from NEM12-formatted CSVs—standard files used by electricity retailers in Australia. It processes multiple interval data types like E1, Q1, B1, and K1, and outputs clean, timestamped CSVs ready for analysis or integration into data pipelines.

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⚙️ How It Works

1. Reads NEM12 CSV input containing interval records.
2. Parses and categorizes data by NMI suffix (E1, Q1, B1, K1).
3. Dynamically constructs timestamped dataframes based on the interval duration (e.g., 5, 10, 15, 30 minutes).
4. Outputs intermediate CSVs for each reading type.
5. Merges all dataframes into:
   • Individual interval-specific datasets (e.g., inspectionE1.csv)
   • A consolidated dataset (finalResult.csv) combining all readings on a common timestamp.

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🛠️ Features

• ✅ Parses NEM12 CSV files with 200, 300, and 900 markers
• ✅ Extracts interval readings (E1, Q1, B1, K1)
• ✅ Supports multiple interval durations (5, 10, 15, 30 minutes)
• ✅ Automatically generates timestamps
• ✅ Merges and cleans dataframes
• ✅ Outputs clean CSVs ready for analysis
• ✅ Deletes intermediate files after processing

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📁 File Structure

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├── NEM12processor.py                # Main script
├── /Nem12/
│   └── /samples/
│       └── Copy 1.csv              # Input file (NEM12 format)
├── /output/
│   ├── inspectionE1.csv
│   ├── inspectionQ1.csv
│   ├── inspectionB1.csv
│   ├── inspectionK1.csv
│   └── finalResult.csv            # Final combined dataset
├── README.md                       # Project documentation
└── LICENSE                         # MIT License file

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⚙️ Requirements

• Python 3.7+
• pandas

Install dependencies:

pip install pandas

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📊 Output Sample

## 📊 Output Example

| timestamp           | E1 reading | Q1 reading | B1 reading | K1 reading |
|---------------------|------------|------------|------------|------------|
| 2024-01-01 00:00:00 | 123.45     | 456.78     | NaN        | 321.65     |
| 2024-01-01 00:30:00 | 124.10     | 458.22     | NaN        | 322.12     |