HVAC controller with predictive cost optimization
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Solution Overview
Problem
Traditional HVAC systems rely on simplistic control algorithms or manual adjustments, leading to increased costs and energy wastage in maintaining occupant comfort, as they fail to optimize resource consumption and operational efficiency effectively.
Innovation Solution
A controller system that performs dual optimizations for waterside and airside HVAC equipment, using a processing circuit to generate combined control decisions that override initial decisions, optimizing resource usage and operational schedules based on predicted demands and thermal models, thereby reducing energy consumption and costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If traditional simplistic control algorithms or manual adjustments are used, then the system is easy to operate, but energy costs and resource consumption increase
Solution Approach 1:
The system performs preliminary optimization by decomposing the combined optimization problem into waterside and airside dual subproblems before execution. This allows advance calculation of optimal control decisions based on predicted demand and thermal models, reducing real-time energy costs while maintaining manageable system complexity through structured preprocessing
Solution Approach 2:
The control system is segmented into distinct waterside and airside optimization modules, each handling specific HVAC equipment subsets. This segmentation allows independent optimization of each subsystem while maintaining overall system coordination, reducing total energy consumption without requiring a monolithic complex control architecture
2Productivity
If dual optimization with constraint-based override is implemented, then resource allocation efficiency improves, but control decision complexity increases
Solution Approach 1:
The optimization process is segmented into two distinct stages: first optimization generates initial control decisions for both waterside and airside equipment, while the second optimization focuses specifically on waterside equipment with constraints from the first stage. This segmentation improves resource allocation efficiency by addressing different equipment types separately while maintaining overall coordination, without requiring a single monolithic complex optimization algorithm
Solution Approach 2:
The first optimization serves as a preliminary action that generates initial control decisions and constraints before the second optimization is executed. This preliminary stage establishes baseline resource allocation that the second optimization then refines, improving overall resource allocation efficiency while breaking down the complex decision-making process into manageable sequential steps
3Loss of energy
If predictive optimization with thermal models is used, then operational costs decrease, but computational requirements increase
Solution Approach 1:
The computational problem is segmented by separating waterside and airside optimization into distinct dual subproblems with different computational requirements. The airside dual subproblem handles building thermal models while the waterside dual subproblem handles equipment efficiency models, reducing peak computational complexity compared to a unified approach while still achieving predictive optimization that minimizes energy wastage
Solution Approach 2:
Thermal models and resource consumption models are used in preliminary optimization stages to predict future energy consumption patterns and generate pre-optimized control decisions. This preliminary computational action reduces real-time energy wastage by anticipating optimal operating conditions, while the structured decomposition keeps computational requirements manageable through phased calculation
Data Source
AI summary
A controller for heating, ventilation, or air conditioning (HVAC) equipment including a processing circuit configured to perform a first optimization to generate a first set of control decisions for HVAC equipment including waterside HVAC equipment that consume resources from utility providers to generate a heated or chilled fluid and airside HVAC equipment that receive and use the fluid from the waterside HVAC equipment to heat or cool a supply of airflow for a building. The processing circuit is configured to perform a second optimization subject to a constraint based on a result of the first optimization to generate a second set of control decisions for the HVAC equipment and to combine the first and second sets to generate a combined set of control decisions for the HVAC equipment. The processing circuit is configured to operate the HVAC equipment in accordance with the combined set of control decisions.