Machine Learning For Energy Consumption Prediction IoT

Machine Learning For Energy Consumption Prediction IoT

Smart Energy & Utilities · Grid Modernization

Python · Feature Engineering · Cross-Validation · Model Serving

Operational Focus: short and medium-term electrical load forecasting and peak demand mitigation.

Project Abstract & Technical Scope

This project introduces an end-to-end, production-ready machine learning framework for Machine Learning For Energy Consumption Prediction IoT. Within real-world operational environments, systems face severe obstacles including extreme weather-driven volatility, non-linear air conditioning load curves, and renewable solar/wind intermittency. The objective of this work is to implement a robust, leak-free computational pipeline that translates raw inputs into deterministic, high-confidence decision metrics.

The system ingests and processes records sourced from smart meter AMI time-series, historical substation active load, ambient temperature/humidity, and tariff schedules. Raw attributes undergo automated data sanitization, multivariate imputation, distribution rebalancing, and outlier filtering. Continuous numerical features are scaled using robust statistical scaling techniques, while categorical, spatial, and temporal attributes receive cyclical encoding, high-cardinality target transforms, or dense embeddings.

Modeling evaluates diverse algorithmic paradigms: XGBoost Regressor with Calendar Embeddings, SARIMAX, LSTM Neural Network, Prophet. Rigorous validation protocols employ stratified, temporal, or grouped cross-validation to prevent train-test contamination. Hyperparameter optimization is systematically executed via Bayesian search strategies (Optuna), targeting optimization of Mean Absolute Percentage Error (MAPE), RMSE, Peak Demand Accuracy (%), Ramp-Rate Error rather than uninformative global accuracy.

To ensure practical viability and regulatory transparency, global and local feature contributions are derived using TreeSHAP and Partial Dependence profiles. The winning configuration is serialized into portable ONNX format and served via an asynchronous FastAPI microservice equipped with telemetry logging for real-time concept drift detection.

Tools & Technologies

The standard modern data science stack utilized for feature extraction, model tuning, and REST deployment:

Modular Machine Learning Workflow

Candidate Algorithms Benchmarked

• • XGBoost Regressor with Calendar Embeddings
• • SARIMAX
• • LSTM Neural Network
• • Prophet

Final production selection is based on cross-validated Pareto efficiency balancing Mean Absolute Percentage Error (MAPE), RMSE, Peak Demand Accuracy (%), Ramp-Rate Error against inference latency.