ML Baseline Energy Prediction for Real-Time Building Consumption
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing energy management solutions lack the ability to evaluate energy consumption patterns in real-time, requiring extensive manual effort and data analysis, and fail to adapt to dynamic factors like weather and occupancy, limiting their effectiveness in identifying energy inefficiencies and forecasting future consumption.
Innovation Solution
An end-to-end automated data pipeline using machine learning methods to compute a baseline energy consumption, incorporating historical data, weather, occupancy, and building characteristics, with a configurable model that learns energy consumption patterns and auto-retrains based on changes, enabling real-time data quality enrichment and alert generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual methods are used to estimate energy consumption, then extensive experience and data analysis are required, but the process requires large manual effort and cannot evaluate energy consumption patterns in real-time
Solution Approach 1:
The system employs machine learning models that automatically learn energy consumption patterns from historical data and dynamically adjust baseline predictions without requiring manual intervention. The model self-trains and adapts to changing conditions, eliminating the need for continuous manual data analysis while maintaining high estimation accuracy.
Solution Approach 2:
The patent replaces manual analytical methods with automated machine learning algorithms. The ML model processes historical energy consumption data, weather information, and occupancy patterns to generate real-time baseline predictions, substituting human expert analysis with computational intelligence that operates continuously without fatigue or delay.
2Reliability
If traditional energy management solutions are used, then baseline estimation may be performed, but the system fails to adapt to dynamic factors like weather and occupancy
Solution Approach 1:
The machine learning model is designed to be dynamic, continuously adapting to changing conditions by incorporating real-time weather data, occupancy information, and building operational parameters. The model recalibrates baseline predictions as new data becomes available, maintaining reliability while responding flexibly to dynamic factors.
Solution Approach 2:
The system implements feedback loops where actual energy consumption data is continuously compared against ML-predicted baselines. Discrepancies trigger model retraining and parameter adjustments, allowing the system to learn from past performance and improve its adaptability to dynamic conditions while maintaining stable baseline estimation.
3Loss of information
If existing energy management solutions are used, then energy consumption data can be collected, but the system lacks the ability to identify root causes of energy inefficiencies
Solution Approach 1:
The system segments energy consumption analysis by breaking down the baseline prediction into contributions from different factors such as weather conditions, occupancy levels, building operations, and equipment usage. This segmentation enables identification of which specific factors are driving energy consumption increases, making root cause detection feasible.
Solution Approach 2:
The machine learning model acts as an intermediary that processes raw energy consumption data along with contextual information from multiple sources (weather stations, occupancy sensors, building management systems). By mediating between these diverse data sources and the final analysis, the ML model synthesizes insights that reveal underlying causes of energy inefficiencies.
4Quantity of substance
If manual baseline generation is performed, then historical data analysis is required, but the process cannot forecast future energy consumption
Solution Approach 1:
The machine learning model performs preliminary learning by training on historical energy consumption data to capture patterns and relationships between various影响因素 and energy usage. This preliminary action enables the model to not only explain past consumption but also forecast future energy needs by applying learned patterns to predicted future conditions.
Data Source
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.