Machine Learning Energy Baselines for Real-Time Building Consumption
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Solution Overview
Problem
Existing energy management solutions lack the ability to evaluate energy consumption patterns in real-time, requiring extensive manual effort and historical data, and are not adaptable to different building types and locations.
Innovation Solution
An end-to-end automated data pipeline using machine learning to ingest, store, analyze, and maintain models for real-time energy baseline estimation, incorporating data quality checks, digital twins, and self-retraining algorithms to adapt to changing conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual energy baseline estimation is performed using historical data and expert analysis, then accuracy of energy consumption evaluation is improved, but time consumption and manual effort increase significantly
Solution Approach 1:
The patent replaces manual expert analysis and mechanical data processing with machine learning algorithms that automatically ingest, clean, and analyze energy consumption data. The ML models process historical energy data, weather data, and occupancy data to generate baseline predictions without requiring manual intervention, thereby maintaining accuracy while dramatically reducing time consumption.
Solution Approach 2:
The system implements self-service through automated data pipelines that continuously ingest, validate, and prepare data without human intervention. The machine learning models automatically retrain and update baselines based on new data, eliminating the need for manual model updates and baseline recalculations while maintaining high accuracy through continuous learning.
2Reliability
If extensive historical data and manual analysis are used to establish energy baselines, then reliability of energy consumption patterns is improved, but device complexity and implementation difficulty increase
Solution Approach 1:
The patent segments the complex data processing task into distinct modular components: data ingestion module, data cleaning module, feature engineering module, model training module, and prediction module. Each module handles a specific aspect of the pipeline, making the overall system more manageable and easier to implement while maintaining reliability through specialized processing at each stage.
Solution Approach 2:
The patent creates a universal machine learning framework that can process multiple data types (energy consumption data, weather data, occupancy data) and apply to different building types and locations. The standardized pipeline and models provide reliable energy baseline estimation across diverse applications without requiring separate complex systems for each case.
3Adaptability or versatility
If traditional energy management solutions are used, then existing infrastructure is maintained, but real-time evaluation capability and adaptability to different building types are lost
Solution Approach 1:
The patent implements dynamic adaptability through machine learning models that continuously learn from new data and adjust to different building types, locations, and operational patterns. The system automatically adapts to various building characteristics by processing location-specific weather data, occupancy patterns, and energy consumption data, providing real-time baseline evaluation without requiring manual reconfiguration for each building type.
Data Source
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AI summary
The present solution, approach or method, including an end-to-end automated data pipeline for data ingestion, storage, analysis, deployment, and a machine learning model maintenance. The present solution, approach or method, which computes a baseline using machine learning methods, may help in the following ways. Accurate real time estimation may help evaluate the deviation in the actual energy consumption, effectively identifying underlying root causes for an increase in actual consumption, as compared to the estimated energy. Triangulating the time of day and place of high energy consumption results in quicker resolution. Accurately quantifying energy savings may be helpful. Forecasting energy consumption in the future, may enable planning for future energy needs. Energy saving calculations may be done by comparing actual consumption versus baseline predicted consumption based for a specific baseline period. This solution may offer a configurable machine learning model, which takes on energy consumption patterns.