HVAC Regression Control for Forecast-Based Energy Cost Reduction
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Solution Overview
Problem
HVAC systems face challenges in efficiently managing energy consumption due to varying environmental conditions and operational costs, as existing controllers lack the ability to effectively account for multiple influencing factors such as ambient temperature, humidity, and energy prices.
Innovation Solution
An HVAC controller that utilizes a regression model to forecast ambient conditions and past operational costs to set optimal temperature set-points, reducing energy consumption by adjusting temperature profiles based on predefined times and comfort limits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If traditional HVAC controllers use fixed set points, then operation is simple, but energy consumption cannot be optimized according to varying environmental conditions and energy prices
Solution Approach 1:
The controller dynamically changes temperature set points based on varying parameters including ambient temperature forecasts, humidity levels, solar exposure, wind conditions, and energy prices. This allows the system to optimize energy consumption by adjusting operational parameters in response to environmental and economic conditions rather than using fixed set points
Solution Approach 2:
The patent replaces traditional rule-based control mechanisms with a machine learning model that processes multiple environmental and economic parameters. This substitution enables more sophisticated energy optimization by using predictive algorithms instead of simple mechanical control logic
2Loss of energy
If HVAC controllers adjust set points frequently based on environmental conditions, then energy efficiency improves, but system complexity and computational requirements increase
Solution Approach 1:
The controller uses forecasted ambient temperature data to proactively adjust set points before environmental conditions change. By performing preliminary adjustments based on predictions rather than reacting to actual changes, the system reduces energy waste while avoiding the need for continuous complex real-time calculations
Solution Approach 2:
The system incorporates feedback from multiple sensors measuring ambient temperature, humidity, solar exposure, and wind conditions. This multi-parameter feedback mechanism enables the controller to optimize energy efficiency by continuously adapting to environmental conditions while maintaining manageable system complexity through integrated processing
3Productivity
If the controller considers multiple factors including ambient temperature, humidity, solar exposure, and energy prices, then energy optimization improves, but the complexity of data processing and model requirements increase
Solution Approach 1:
The machine learning model is designed to process multiple types of input data (temperature, humidity, solar exposure, wind conditions, energy prices) through a single integrated system. This multi-functional approach enables comprehensive energy optimization by considering all relevant factors simultaneously rather than requiring separate control systems for each parameter
Solution Approach 2:
The controller integrates diverse data sources and environmental factors into a unified control strategy. By combining multiple parameters that individually influence energy consumption into a composite decision-making model, the system achieves superior energy optimization efficiency while managing data processing complexity through integrated architecture
Data Source
AI summary
A thermal control system for a building is disclosed, which includes a regression model: Given a forecast temperature outside the building, the regression model predicts how much an HVAC system will cost to run during a day, for a given set of time-varying target temperatures for all the thermostats in the thermal control system. The thermal control system may also include an optimizer, which invokes multiple applications of the regression model. Given a forecast temperature outside the building, the optimizer predicts an optimal set of time-varying target temperatures for all the thermostats in the thermal control system. Running the HVAC system with the optimal set of time-varying target temperatures should have a reduced or a minimized cost, or a reduced or minimized total energy usage. The optimizer works by running the regression model repeatedly, while adjusting the time-varying target temperature for each thermostat between runs of the model.