AI-Based-Energy-Consumption-Profiling

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AI-Based-Energy-Consumption-Profiling

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AI-Based Energy Consumption Profiling uses machine learning to analyze usage patterns, identify inefficiencies, and predict future energy needs. By examining real-time and historical data, it enables smarter decision-making, cost reduction, and improved sustainability.

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AI-Based-Energy-Consumption-ProfilingA compact, practical README for the AI-Based-Energy-Consumption-Profiling project. This repository contains code and notebooks to profile and analyze energy consumption patterns using machine learning — useful for demand-side energy management, anomaly detection, load forecasting, and customer segmentation.Table of ContentsProject OverviewFeaturesRepository StructureGetting StartedRequirementsInstallationDataExample SchemaPreparing Your DataUsageQuick Start (notebook)Train a Model (script)Profile & Export ResultsModeling & EvaluationExamplesContributingLicenseContactProject OverviewThis project uses machine learning to create energy consumption profiles from smart meter or building-level energy data. It extracts features, clusters users or devices into consumption archetypes, detects anomalies, and can produce short-term forecasts to inform demand-side management actions.FeaturesData ingestion and cleaning pipelineFeature extraction: daily/weekly patterns, peak/off-peak behavior, seasonality metricsClustering to generate consumption profiles (e.g., K-Means, DBSCAN)Anomaly detection module (statistical + ML-based)Simple forecasting baselines (ARIMA, XGBoost / LightGBM)Export results to CSV/Excel for reportingExample Jupyter notebooks for exploration and demonstrationRepository Structure.
├── data/                      # example and synthetic datasets (do not store PII)
├── notebooks/                 # exploratory notebooks and tutorials
│   ├── 01_data_overview.ipynb
│   ├── 02_feature_engineering.ipynb
│   └── 03_clustering_profiling.ipynb
├── src/                       # core python modules
│   ├── ingest.py
│   ├── features.py
│   ├── clustering.py
│   ├── anomaly.py
│   └── forecasting.py
├── scripts/                   # runnable scripts (train, eval, export)
│   ├── train_model.py
│   └── profile_export.py
├── tests/                     # unit tests
├── requirements.txt
├── README.md                  # this file
└── LICENSEGetting StartedRequirementsPython 3.9+Packages (see requirements.txt), typical examples:pandas, numpy, scikit-learn, scipymatplotlib (for plotting in notebooks)xgboost or lightgbm (optional, for forecasting)openpyxl (for Excel export)jupyterlab / notebookInstallationClone the repo:git clone https://your.git.repo/AI-Based-Energy-Consumption-Profiling.git
cd AI-Based-Energy-Consumption-ProfilingCreate a virtual environment and install dependencies:python -m venv venv
source venv/bin/activate      # macOS / Linux
venv\Scripts\activate         # Windows
pip install -r requirements.txtDataExample SchemaInput data should be a time-series table (CSV/Parquet) with a datetime column and one or more consumption columns. Example:timestampmeter_idconsumption_kwh2024-01-01 00:00:00MTR-0010.422024-01-01 00:15:00MTR-0010.38.........Minimum requirements:timestamp column (ISO format recommended)meter_id (or device_id / building_id) for multi-entity datasetsconsumption column(s) (kWh, W, etc.)Preparing Your DataResample to regular intervals (e.g., 15min, 1H) if necessary.Handle missing data: interpolation, forward/backfill, or masking.Normalize units (kW vs kWh) and timezone-aware timestamps.Split into train/validation/test for forecasting tasks.UsageQuick Start (notebook)Open notebooks/01_data_overview.ipynb and follow the step-by-step guided analysis:jupyter lab
# then open the notebook in the browserTrain a Model (script)Example to train clustering and profiling:python scripts/train_model.py \
  --data data/example_consumption.csv \
  --outdir outputs/ \
  --freq 15min \
  --model clusteringScript options:--data: path to CSV or Parquet`--

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