HVAC Energy Analytics Engine for Predictive Consumption Control
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Solution Overview
Problem
Conventional HVAC systems lack the ability to accurately predict energy consumption and adjust operations based on real-time energy prices and occupancy patterns, leading to inefficiencies and deviations in targeted operating costs.
Innovation Solution
An HVAC energy management control system that includes an analytics engine capable of learning from historical data to predict energy consumption, incorporating thermal load characteristics and real-time energy pricing, and automatically adjusting operations to optimize energy use and cost alignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional HVAC systems operate based on simple thermostat control, then the system is easy to operate, but the energy consumption prediction accuracy is poor
Solution Approach 1:
An analytics engine is introduced as an intermediary component between the thermostat and HVAC system. This engine processes historical data, thermal load characteristics, and real-time pricing information to generate accurate energy consumption predictions, thereby improving measurement precision without requiring fundamental changes to the core HVAC operation
Solution Approach 2:
The patent replaces simple mechanical thermostat control with a data-driven analytics system that uses historical data processing and thermal load modeling. This substitution enables accurate energy consumption prediction by transitioning from rule-based control to predictive analytics based on learned patterns
2Adaptability or versatility
If HVAC systems use digital thermostats with scheduling, then energy efficiency is improved, but the system cannot adapt to real-time energy prices and occupancy patterns
Solution Approach 1:
The analytics engine implements continuous feedback loops by monitoring real-time energy pricing, occupancy patterns, and actual energy consumption. This feedback enables the system to dynamically adjust operations, adapting to changing conditions while optimizing energy usage based on learned historical patterns and current constraints
Solution Approach 2:
The system transitions from static scheduling to dynamic adaptation by continuously learning from historical data and adjusting to real-time conditions. The analytics engine processes evolving occupancy patterns and energy prices to dynamically optimize HVAC operations, making the system adaptable rather than fixed
3Reliability
If HVAC systems operate without predictive analytics, then the device complexity is low, but the alignment with user-set cost targets is poor
Solution Approach 1:
The analytics engine performs preliminary actions by proactively predicting energy consumption and identifying optimization opportunities before they become critical. By analyzing historical data and thermal load characteristics in advance, the system can pre-adjust operations to align with user-set cost targets, ensuring reliable cost management
Data Source
AI summary
A heating, ventilation, and air conditioning (HVAC) energy management control system includes an HVAC system configured to deliver at least one of heated air and cooled air to a targeted area; and a computing server including an HVAC energy analytics engine in signal communication with the HVAC system. The HVAC energy analytics engine is configured to actively learn historical data of the HVAC system based on at least one of the heated air and the cooled air produced over a time period. The HVAC energy analytics engine determines a predicted energy consumption of the HVAC system based on the historical data, and the HVAC system operates based on the predicted energy consumption.