Zone-Specific Thermal Load Prediction Using Multi-Source Data Models
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Solution Overview
Problem
Existing thermal load prediction systems face challenges in accurately predicting thermal loads for individual air-conditioning zones within a building, as they typically focus on outdoor units rather than zone-specific loads.
Innovation Solution
A thermal load prediction system that includes an environmental data acquisition unit, operation data acquisition unit, storage unit, learning unit, and prediction unit, which acquires and processes external and internal environmental data, as well as operation data from indoor units to train models for predicting thermal loads for each air-conditioning zone based on heat exchange amounts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If thermal load prediction focuses on outdoor units, then system-level control is simplified, but zone-specific thermal load prediction accuracy deteriorates
Solution Approach 1:
The patent segments the thermal load prediction system into zone-specific models, where each air-conditioning zone has its own prediction model trained on zone-specific data (indoor environmental data, outdoor environmental data, and operation data from indoor units in that zone). This segmentation enables accurate zone-specific thermal load prediction while maintaining manageable system complexity through modular model architecture.
2Productivity
If zone-specific thermal load prediction is implemented, then air conditioning efficiency for each zone is improved, but data acquisition and model training complexity increases
Solution Approach 1:
The patent implements a universal data acquisition framework that collects multiple types of data (indoor environmental data, outdoor environmental data, operation data) through standardized interfaces. The learning unit uses a consistent model training approach across all zones, applying the same algorithmic process to zone-specific data. This universal approach reduces the actual complexity despite zone-specific customization, as the overall system architecture and methodology remain standardized.
Data Source
AI summary
A thermal load prediction system predicts a thermal load of an air conditioning zone in a building. The thermal load prediction system includes an environmental data acquisition unit, an operation data acquisition unit, a storage unit, a learning unit and a prediction unit. The environmental data acquisition unit acquires at least one of external environmental data and internal environmental data. The operation data acquisition unit acquires operation data of an air conditioner configured to perform air conditioning for the air conditioning zone. The storage unit stores the data acquired by the environmental data acquisition unit and the operation data acquisition unit. The learning unit trains a model with which the thermal load of the air conditioning zone is predicted by using learning data obtained from the storage unit. The prediction unit predicts the thermal load of the air conditioning zone by using the model.