HVAC Control System Using Neural Network for Cost-Comfort Optimization
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Solution Overview
Problem
HVAC systems face challenges in minimizing energy consumption without causing occupant discomfort, as maintaining comfortable temperatures often leads to high energy costs and inefficiencies.
Innovation Solution
A building management system that uses a neural network to classify the current state of a building, determines temperature bounds, and adjusts a cost function with penalty terms to optimize indoor air temperature setpoints, thereby reducing energy consumption while maintaining occupant comfort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If HVAC systems maintain comfortable temperatures at all times, then occupant comfort is improved, but energy consumption increases
Solution Approach 1:
The system dynamically adjusts temperature setpoints based on real-time conditions including occupancy patterns, outdoor temperature, and energy prices. The neural network continuously learns and adapts to building-specific patterns, transforming the static temperature maintenance approach into a dynamic optimization system that balances comfort and energy consumption.
Solution Approach 2:
The system performs preliminary cooling or heating during off-peak hours when energy prices are lower, storing thermal energy in the building's thermal mass. This allows the HVAC system to reduce or shut off during peak pricing periods while maintaining occupant comfort through the stored thermal energy, thus avoiding high energy costs.
2Loss of energy
If HVAC systems reduce energy consumption, then energy costs decrease, but occupant comfort deteriorates
Solution Approach 1:
The system continuously monitors indoor temperature, occupancy patterns, and energy consumption, feeding this data back to the neural network. The network uses this feedback to refine temperature predictions and adjust setpoints in real-time, ensuring that energy reduction actions do not compromise occupant comfort while achieving cost savings.
Solution Approach 2:
The system changes temperature setpoint parameters dynamically based on multiple factors including time of day, day of week, outdoor conditions, and energy pricing. By adjusting these parameters optimally rather than maintaining fixed comfortable temperatures, the system achieves energy cost reduction without significant comfort deterioration.
3Device complexity
If HVAC systems use traditional control methods, then system complexity is low, but energy optimization capability is insufficient
Solution Approach 1:
The patent replaces traditional mechanical control systems with an intelligent neural network-based control system. The neural network processes building data, predicts temperature patterns, and determines optimal setpoints, substituting complex computational intelligence for simple on/off or proportional control mechanisms.
Solution Approach 2:
The neural network acts as an intermediary between raw building data and HVAC control decisions. It processes and interprets multiple data sources including occupancy patterns, weather forecasts, and energy prices, transforming this information into optimized temperature setpoints that balance comfort and energy costs.
Data Source
AI summary
A building management system includes HVAC equipment operable to affect an indoor air temperature of a building, a system manager configured to obtain a cost function that characterizes a cost of operating the HVAC equipment, obtain a dataset relating to the building, determine a current state of the building by applying the dataset to a neural network, select a temperature bound associated with the current state, augment the cost function to include a penalty term that increases the cost when the indoor air temperature violates the temperature bound, and determine a temperature setpoint for each of a plurality of time steps in the future time period. The temperature setpoints achieve a target value of the cost function over the future time period. The building management system also includes a controller configured to operate the HVAC equipment to drive the indoor air temperature towards the temperature setpoint.