Building Heating Energy Savings Prediction Using Degree Days
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Solution Overview
Problem
Existing energy management systems require significant processing and data storage resources to predict energy savings, which can be costly and impractical for remote or secluded locations without reliable internet access.
Innovation Solution
A method and controller that minimizes complexity by using a simple model to estimate energy savings for heating buildings, incorporating minimal processing and data storage, allowing local generation and maintenance of estimates without remote support.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex models and algorithms are used to predict energy savings, then prediction accuracy is improved, but processing resources and data storage requirements increase
Solution Approach 1:
The patent uses simple linear regression models instead of complex machine learning algorithms, accepting slightly lower prediction accuracy in exchange for dramatically reduced processing requirements. The model is intentionally kept simple and can be easily recalculated with new data, embodying the principle of using simple, replaceable solutions rather than complex, permanent ones.
Solution Approach 2:
Instead of starting with complex models and trying to optimize them, the patent inverts the approach by starting with the simplest possible model (linear regression) and only adding complexity when absolutely necessary. The system deliberately chooses simplicity first, then incrementally adds features only when the marginal benefit justifies the increased complexity.
2Measurement precision
If comprehensive data storage is implemented, then analysis accuracy is improved, but data storage resources increase
Solution Approach 1:
The patent extracts only the essential features needed for energy consumption prediction (temperature, humidity, occupancy, weather data) and stores them in a simplified format. Non-essential data is deliberately excluded, and the stored data is normalized to reduce storage requirements while maintaining prediction accuracy.
Solution Approach 2:
The patent transforms raw sensor data into processed features (e.g., temperature deviations from setpoints, humidity ratios, occupancy patterns) that are more compact and directly useful for prediction. This parameter transformation reduces storage requirements while preserving the information needed for accurate energy consumption analysis.
3Loss of energy
If sophisticated energy management systems are deployed, then energy efficiency is improved, but system cost increases
Solution Approach 1:
The patent implements automated energy optimization where the system independently analyzes its own operational data, identifies inefficiencies, and adjusts settings without requiring external consultants or complex manual intervention. The system serves itself by automatically generating and implementing energy-saving strategies based on its own performance data.
Solution Approach 2:
The patent uses historical data analysis to predict future energy consumption patterns and proactively adjusts system settings before peak consumption occurs. By analyzing past performance and weather patterns, the system preemptively optimizes heating and cooling schedules to avoid high-energy periods, achieving efficiency gains before problems arise.
Data Source
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AI summary
An implementation of the disclosed technology provides a method that includes determining an average outdoor temperature over a time period for a building location, measuring a heating energy consumption for the building over the time period, adding the average outdoor temperature and heating energy consumption to a data set including multiple average outdoor temperatures and corresponding heating energy consumptions over multiple time periods of similar duration to the time period, calculating a base temperature where heating is required to maintain the building at a setpoint temperature based on the data set, generating a model of heating the building based on the data set, the model providing a heating energy consumption per heating degree day, wherein a number of heating degree days is based on the base temperature, and using the heating energy consumption per heating degree day to estimate energy savings based on a change in the setpoint temperature.