HVAC Neural Network Control for Comfort-Energy Tradeoffs
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Solution Overview
Problem
Existing HVAC systems face challenges in minimizing energy consumption while maintaining occupant comfort, as precise temperature control often leads to high energy costs and discomfort when trying to reduce energy usage.
Innovation Solution
A method and system utilizing a neural network to determine optimal control dispatches for HVAC equipment by modeling estimated costs over simulated scenarios, with learned weights minimizing energy expenditure through online control adjustments based on actual measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If precise temperature control is implemented to maintain occupant comfort, then comfort level is improved, but energy consumption increases
Solution Approach 1:
The system performs preliminary actions by pre-cooling or pre-heating spaces during off-peak hours when energy costs are lower, and by predicting future thermal loads based on historical data and weather forecasts. This allows the HVAC system to maintain comfort while reducing peak energy consumption through advance preparation.
Solution Approach 2:
The system dynamically adjusts temperature setpoints, equipment operation schedules, and control strategies based on real-time conditions including occupancy patterns, outdoor temperature, humidity levels, and energy prices. This dynamic adaptation allows the system to optimize the balance between comfort and energy consumption continuously.
2Use of energy by moving object
If energy consumption is reduced to lower operational costs, then energy efficiency is improved, but occupant comfort deteriorates
Solution Approach 1:
The system continuously monitors multiple parameters including space temperature, humidity, occupancy, equipment status, and energy consumption, then uses this feedback to automatically adjust control strategies. Machine learning algorithms analyze this feedback data to learn optimal control patterns that maintain comfort while minimizing energy use, adapting to changing conditions and occupancy preferences.
Solution Approach 2:
The system changes operational parameters such as temperature setpoints, equipment run schedules, and control algorithm parameters based on learned patterns from historical data and real-time conditions. These parameter adjustments are optimized to maintain comfort within acceptable ranges while reducing overall energy consumption through smarter operation.
3Device complexity
If traditional control methods are used to manage HVAC equipment, then system simplicity is maintained, but energy optimization capability is insufficient
Solution Approach 1:
The system replaces traditional mechanical control methods with data-driven machine learning models and automated control algorithms. These intelligent systems analyze historical and real-time data to predict optimal control actions, substituting complex computational processes for simpler traditional control mechanisms while achieving superior energy optimization.
Solution Approach 2:
The system performs self-learning and self-optimization by automatically analyzing operational data, identifying patterns, and adjusting control strategies without requiring manual intervention. The machine learning models continuously improve their performance by learning from accumulated data, enabling the system to optimize energy consumption autonomously while maintaining simplicity of operation.
Data Source
AI summary
A method includes operating equipment to affect a variable state or condition of a space and determining a set of learned weights for a neural network by modeling an estimated cost of operating the equipment over a plurality of simulated scenarios. Each simulated scenario includes simulated measurements relating to the space. The neural network is configured to generate simulated control dispatches for the equipment based on the simulated measurements. The method also includes configuring the neural network for online control by applying the set of learned weights, applying actual measurements relating to the space to the neural network to generate a control dispatch for the equipment, and controlling the equipment in accordance with the control dispatch.