HVAC Energy Prediction Using Indoor and Outdoor Temperature Models
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Solution Overview
Problem
HVAC systems consume a significant portion of a building's energy, making it difficult for users to estimate energy usage and evaluate the trade-off between operating temperature and costs, as existing methods do not account for factors like specified operating temperature and outside temperature.
Innovation Solution
A method is provided to predict energy consumption by using models for indoor temperature and HVAC system states, combined with predicted future outdoor temperatures, to estimate future energy usage, allowing users to adjust settings for improved efficiency and cost reduction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If HVAC systems operate at a specific operating temperature to maintain building comfort, then user comfort is improved, but energy consumption increases and operating costs rise
Solution Approach 1:
The system performs preliminary actions by predicting future energy consumption and providing cost estimates to users before they make temperature setting decisions. This allows users to anticipate the energy costs of different temperature settings and make informed choices that balance comfort with energy expenditure, rather than reacting to high bills after the fact.
Solution Approach 2:
The system implements feedback by providing users with predicted energy consumption data and cost estimates based on their selected operating temperatures. This feedback loop enables users to adjust their temperature settings based on the projected energy costs, creating a closed-loop system where information about energy consumption influences future operational decisions.
2Adaptability or versatility
If users want to estimate HVAC energy usage to evaluate trade-offs, then energy management capability is improved, but system complexity increases due to multiple prediction models
Solution Approach 1:
The system achieves universality by using a single integrated prediction framework that handles multiple functions: predicting indoor temperatures, estimating energy consumption, calculating operating costs, and providing forecasts for different future time periods. This multi-functional approach consolidates what could be separate complex systems into one unified energy management tool.
Solution Approach 2:
The system applies parameter changes by utilizing predicted future outdoor temperatures as input parameters to generate predictions for various future time points. By changing the time parameter and using weather forecast data, the system generates multiple energy consumption scenarios without requiring fundamentally different prediction models for each scenario.
Data Source
AI summary
A method for predicting energy consumption of an HVAC system is provided. The method includes providing a model for an indoor temperature of a building, a model for an operating state of the HVAC system, and predicted future outdoor temperatures. Utilizing at least the models for the indoor temperature and the operating state of the HVAC system and the predicted future outdoor temperatures, a predicted future energy consumption of the HVAC can be estimated.