Building Energy Prediction Models for Fast Upgrade Recommendations
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for improving energy efficiency in buildings are time-consuming and inefficient, requiring lengthy onsite assessments and manual evaluation of energy upgrades, which limits the ability to quickly retrofit buildings and reduce greenhouse gas emissions.
Innovation Solution
A method and system for predicting energy-related metrics in buildings using a process that selects and iterates through service regions, processing building-related data to recommend upgrades and predict energy characteristics, leveraging machine learning algorithms and energy analysis models to provide rapid and accurate energy efficiency recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional onsite assessment methods are used for building energy evaluation, then measurement precision and reliability are improved, but loss of time and productivity deteriorate
Solution Approach 1:
The system performs preliminary actions by pre-processing building data, pre-training energy models, and preparing prediction algorithms before actual energy assessment is needed. Building data is collected and validated in advance, and multiple energy models are pre-configured with different parameters and thresholds, enabling rapid execution during the actual assessment without time-consuming setup
Solution Approach 2:
The patent replaces manual mechanical assessment processes with automated computational systems. Physical onsite inspections and manual calculations are substituted by machine learning algorithms, automated data processing systems, and computational energy models that can rapidly analyze building data and generate energy assessments without human intervention
2Measurement precision
If multiple energy models are used to improve prediction accuracy, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system implements feedback mechanisms where the results from multiple energy models are continuously evaluated and compared. The system receives feedback on model performance, accuracy metrics, and prediction quality, then automatically adjusts model parameters, selects optimal models, and refines predictions based on this feedback loop, resolving the complexity of managing multiple models through automated adaptive selection
Solution Approach 2:
The patent applies parameter changes by dynamically adjusting model parameters, thresholds, and configuration settings based on building characteristics, data quality, and assessment requirements. Different models are activated with optimized parameters specific to each building type and data availability, simplifying the complexity by adapting parameters rather than managing fixed complex model configurations
3Measurement precision
If comprehensive building data processing is performed to improve energy metric prediction, then measurement precision is improved, but use of energy and computational resources increases
Solution Approach 1:
The system applies partial action by processing only the most relevant building data and energy models needed for each specific assessment, rather than comprehensively analyzing all available data. The system selectively activates energy models based on building characteristics and data availability, performing sufficient analysis to achieve accurate predictions without the excessive computational burden of processing every possible data point and model
Data Source
AI summary
Methods and systems for predicting energy-related metrics of a building are provided, including creating and selecting models for recommending a building upgrade and predicting energy savings based on a recommended building upgrade. Automate building energy efficiency evaluations and do not require input by the building owner. Without participation of building owners or the need for onsite home energy performance evaluations, energy metrics and upgrade recommendations can be quickly and automatically provided to many homeowners.