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Energy Consumption Forecasting Using LSTM Networks on Google Cloud ML Engine

Energy Consumption

Energy Consumption Forecasting Using LSTM Networks on Google Cloud

  • Use Case : Energy providers, smart grid operators, and industrial facilities need accurate energy consumption forecasts to optimize generation, reduce costs, and manage renewable energy integration. Time-series data from smart meters, IoT sensors, and historical usage can be modeled using LSTM (Long Short-Term Memory) networks for precise short-term and long-term predictions.

Objective

  • Develop time-series forecasting models using LSTM to predict energy demand.

    Optimize energy generation, distribution, and storage for efficiency and cost reduction.

    Leverage Google Cloud ML Engine for scalable training and deployment of deep learning models.

    Provide real-time forecasting insights for utilities, smart buildings, and industrial systems.

Project Description

  • This project implements an energy consumption forecasting system using LSTM networks on Google Cloud:

    Data Collection: Gather historical energy consumption data from smart meters, IoT sensors, and external factors (temperature, weather, occupancy).

    Data Preprocessing: Normalize, handle missing values, and structure data as sequences for LSTM input.

    Model Training: Train LSTM models on ML Engine / Vertex AI with GPU/TPU acceleration for high-volume data.

    Prediction & Deployment: Deploy trained LSTM models as real-time prediction endpoints to forecast energy consumption.

    Monitoring & Feedback: Compare predicted vs actual consumption to retrain and improve model accuracy.

Key Technologies & Google Cloud Platform Services

  • GCP Service Purpose
    Cloud Storage Stores historical energy data, IoT sensor readings, and preprocessed datasets.
    Pub/Sub Streams real-time energy sensor data to processing pipelines.
    Dataflow Processes and aggregates real-time and batch energy data for LSTM training and inference.
    BigQuery Stores structured energy consumption data for historical analysis and feature engineering.
    Vertex AI / ML Engine Train and deploy LSTM models for energy forecasting; supports GPUs/TPUs for faster computation.
    Vertex AI Pipelines Automates end-to-end ML workflow: preprocessing → training → evaluation → deployment.
    Cloud Functions Event-driven triggers for model retraining or prediction updates when new data arrives.
    Cloud Monitoring / Logging Monitors model performance, latency, and resource utilization.
    Looker / Data Studio Visualizes energy forecasts, trends, and deviations from actual consumption.