HVAC Demand Response Control With Offline Strategy Pooling
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Solution Overview
Problem
On-line control optimization for HVAC systems is hindered by intensive computation requirements, making simulation-based methods impractical for real-time decision-making, especially in commercial buildings with medium to large spaces, due to high engineering effort and heavy computation loads.
Innovation Solution
A two-stage simulation-based optimization scheme is implemented, where off-line computations for weather patterns generate an optimal strategy pool, and on-line simulations quickly identify the best strategy for real-time HVAC control, reducing computation load and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If simulation-based optimization is used for HVAC demand response, then prediction accuracy can be improved, but computation load becomes heavy and real-time decision making is not feasible
Solution Approach 1:
The patent pre-calculates and stores optimal control strategies for various weather patterns and building conditions in an offline stage. During online operation, the system only needs to retrieve and execute pre-determined strategies based on current conditions, avoiding real-time simulation computations while maintaining high prediction accuracy
Solution Approach 2:
The patent divides the optimization problem into discrete weather patterns and building condition scenarios. By segmenting the continuous optimization space into manageable categories, the system can pre-compute strategies for each segment and quickly select appropriate strategies during online operation without performing full simulations
2Measurement precision
If whole building simulation engines are used, then acceptable accuracy can be provided, but much engineering effort is required to develop and calibrate the simulation model for each specific building
Solution Approach 1:
The patent develops a generic building model that can be applied to different building types without requiring extensive customization. The model uses standardized parameters and relationships that can be adapted to various buildings through simple configuration rather than complete recalibration, reducing engineering effort while maintaining accuracy
Solution Approach 2:
The patent employs a simplified set of key building parameters that capture the essential thermal and energy characteristics of buildings. By focusing on critical parameters rather than detailed building-specific properties, the model achieves acceptable accuracy across different building types with minimal calibration effort
Data Source
AI summary
A computer-implemented method of optimizing demand-response (DR) of a heating, ventilation, and air-conditioning (HVAC) system of a building, includes determining (30, 31, 32) a value of an objective function Fij of a HVAC system for each of a plurality of DR strategies j for each of a plurality of weather patterns i that is a weighted sum of an energy cost of the HVAC system and a thermal comfort loss of the HVAC system, assigning (33, 34, 35, 36) a likelihood score Li,j to each of a selected subset of near-optimal DR strategies j for each weather pattern i, and selecting (37, 38) those near-optimal DR strategies with large overall likelihood scores Lj to create an optimal strategy pool of DR strategies. An optimal strategy pool can be searched (39) in real-time for an optimal DR strategy for a given weather pattern.