Central plant control system based on load prediction through mass storage model
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Solution Overview
Problem
Existing HVAC systems face inefficiencies in power consumption due to the practice of cycling chillers based on fixed temperature rules, which can lead to brief periods of chiller operation at suboptimal conditions, resulting in higher energy costs and reduced efficiency.
Innovation Solution
A controller system that predicts thermal energy loads and generates models to dynamically manage the temperature of the working fluid in the HVAC system, optimizing chiller operation by determining thermal mass and adjusting loads to maintain temperature within allowable ranges, thereby reducing energy consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a chiller is cycled on and off based on fixed temperature rules, then the chiller can meet cooling loads during low load periods, but the chiller operates at suboptimal conditions during brief transition periods, increasing power consumption
Solution Approach 1:
The system performs preliminary cooling action by pre-chilling the water loop before peak load periods. The controller predicts future cooling loads and activates the chiller in advance to store thermal energy in the water loop, so that when actual cooling demand occurs, the chiller can operate more efficiently or be temporarily bypassed, reducing overall power consumption during high-demand periods
Solution Approach 2:
The system transitions from static, fixed-temperature cycling control to dynamic, predictive control. The controller continuously monitors multiple parameters (water temperature, flow rate, predicted load, weather forecasts) and dynamically adjusts chiller operation in real-time, optimizing the balance between meeting cooling demands and minimizing energy consumption during transition periods
2Use of energy by moving object
If a chiller operates continuously to maintain temperature, then power efficiency improves, but the system lacks adaptability to varying thermal energy loads and cannot optimize for demand charge reduction
Solution Approach 1:
The system implements multi-loop feedback control where the controller continuously monitors water temperature, flow rate, chiller performance, and predicted thermal loads. This feedback information is used to dynamically adjust chiller operation, enabling the system to adapt to varying thermal demands while maintaining optimal efficiency. The feedback mechanism allows the system to respond to both actual and predicted load conditions, optimizing the balance between continuous operation benefits and load-matching flexibility
Solution Approach 2:
The controller uses weather forecasts and historical data to predict future thermal energy loads and proactively adjusts chiller operation in advance. This preliminary action allows the system to prepare for upcoming load variations, maintaining efficient operation while being adaptable to changing conditions without reactive cycling
3Device complexity
If the system uses simple temperature-based control rules, then the control logic is simple and easy to implement, but the system cannot accurately predict induced thermal energy loads or optimize chiller operation for energy efficiency
Solution Approach 1:
The system replaces simple mechanical temperature-based switching control with an intelligent predictive control system that uses computational algorithms, weather forecasting data, and real-time sensor information. This substitution of computational intelligence for mechanical simplicity enables accurate prediction of induced thermal loads and optimized chiller operation, while the controller integrates these complex functions into a unified system that manages the increased complexity
4Loss of energy
If the chiller operates at minimum load during transition periods, then the chiller remains on and avoids cycling losses, but the system cannot effectively reduce demand charges or optimize for peak load periods
Solution Approach 1:
The system uses predictive control to perform preliminary cooling action before peak demand periods. By anticipating future thermal loads based on weather forecasts and historical patterns, the controller pre-chills the water loop in advance, storing thermal energy that can be used during high-demand periods. This allows the system to reduce chiller operation during peak demand, effectively reducing demand charges while avoiding the need to operate at inefficient minimum load levels during transitions
Data Source
AI summary
Disclosed herein are related to a system, a method, and a non-transitory computer readable medium for operating an energy plant. In one aspect, the system generates a regression model of a produced thermal energy load produced by a supply device of the plurality of devices. The system predicts the produced thermal energy load produced by the supply device for a first time period based on the regression model. The system determines a heat capacity of gas or liquid in the loop based on the predicted produced thermal energy load. The system generates a model of mass storage based on the heat capacity. The system predicts an induced thermal energy load during a second time period at a consuming device of the plurality of devices based on the model of the mass storage. The system operates the energy plant according to the predicted induced thermal energy load.