Chiller unit energy consumption calculating method, calculating device, and chiller unit
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Solution Overview
Problem
Existing methods for predicting chiller unit power in HVAC systems, such as data-driven and building physics-based models, suffer from inaccuracies due to data quality issues and complexity, hindering the application of advanced control methods like model predictive control (MPC) for energy savings.
Innovation Solution
A method and device that utilize a combination of data-driven and physical prediction models to accurately predict and calculate chiller unit power by distributing load among multiple chillers based on preset efficiency relationships and characteristic curves, enhancing the accuracy of power prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a data-driven model is used to predict chiller unit power, then the prediction can be performed quickly, but the accuracy is highly dependent on data quality and availability
Solution Approach 1:
The patent combines data-driven models with building physics-based models into a hybrid prediction system. The data-driven component provides quick predictions based on historical data patterns, while the physics-based component ensures accuracy by incorporating thermal dynamics and heat transfer principles. This merging resolves the contradiction by achieving both speed and accuracy simultaneously.
2Measurement precision
If a building physics-based model is used to predict chiller unit power, then the prediction accuracy is improved, but the model becomes very complex and not suitable for control applications
Solution Approach 1:
The patent segments the prediction model into multiple modular components: data-driven prediction modules for different time scales, physics-based correction modules, and optimization modules. Each segment handles specific aspects of the prediction task, reducing overall complexity while maintaining accuracy. This segmentation makes the model suitable for real-time control applications.
Solution Approach 2:
The patent applies different modeling approaches to different aspects of the prediction problem locally. Data-driven methods are used where patterns are clear and quick responses are needed, while physics-based methods are applied where accuracy is critical and computational resources allow. This local differentiation optimizes the balance between complexity and accuracy.
3Ease of operation
If manual control or traditional PID control methods are used for chiller units, then the control implementation is simple, but the energy-saving effect is not ideal
Solution Approach 1:
The patent implements a closed-loop control system with model predictive control that continuously monitors system state, predicts future energy consumption, and adjusts control actions to optimize energy savings. The feedback mechanism includes real-time data collection from sensors, model-based prediction of energy usage, and automatic adjustment of chiller operations, achieving significant energy savings while maintaining manageable complexity through automated decision-making.
Data Source
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AI summary
This invention provides a method for predicting and calculating energy consumption of a chiller unit, a device for predicting and calculating energy consumption of a chiller unit, and a chiller unit. The method includes: a chiller unit load prediction step of predicting and outputting a total load of the chiller unit based on a preset cooling load prediction model; a chiller load distribution step of distributing the total load of the chiller unit to generate a first chiller load and a second chiller load, according to a preset chiller load distribution logic; a chiller energy efficiency value acquisition step of correspondingly acquiring a first chiller energy efficiency value and a second chiller energy efficiency value according to a preset chiller load-energy efficiency relationship; and a chiller power calculation step of calculating a first chiller input power and a second chiller input power according to the first chiller energy efficiency value and the second chiller energy efficiency value.