System and method for controlling an environmental condition of a zone
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Solution Overview
Problem
Existing HVAC systems lack precision and efficiency in controlling environmental conditions due to reliance on static schedules and historical data, failing to account for actual occupancy and user behavior, leading to suboptimal energy consumption and comfort.
Innovation Solution
A computer-implemented method that integrates planning, personal, and environmental data to predict user-specific behavior, using AI and Markov chains to dynamically adjust climate control systems for optimal comfort and energy efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If HVAC systems use static schedules and preset parameters, then system operation is simple and reliable, but forecast accuracy and energy efficiency deteriorate
Solution Approach 1:
The system transitions from static schedules to dynamic forecasting by continuously updating predictions based on real-time occupancy data, historical patterns, and environmental conditions. The forecast is recalculated at each control cycle, allowing the system to adapt to changing conditions while maintaining manageable complexity through automated data processing.
Solution Approach 2:
The system incorporates feedback loops where actual occupancy measurements and environmental sensor data are continuously fed back into the forecasting model. This feedback mechanism allows the system to learn from past performance and improve forecast accuracy over time without requiring manual intervention or system redesign.
2Adaptability or versatility
If HVAC systems rely on historical loads, then implementation is straightforward, but ability to predict future occupancy and environmental requirements deteriorates
Solution Approach 1:
The system performs preliminary forecasting of occupancy and environmental requirements before actual events occur. By predicting future states based on planned events, historical patterns, and real-time data, the system can proactively adjust HVAC operations to meet anticipated demands rather than reacting to past conditions.
Solution Approach 2:
The system adds new dimensions to the forecasting model by incorporating occupancy data, event information, and environmental sensors alongside traditional historical load data. This multi-dimensional approach captures user behavior patterns and contextual factors that single-dimensional historical analysis cannot detect.
3Productivity
If HVAC systems use simple thermostats, then device complexity is low, but energy efficiency and comfort optimization deteriorate
Solution Approach 1:
The system performs self-optimization by automatically processing occupancy data, generating forecasts, and adjusting HVAC operations without manual intervention. The control algorithm independently analyzes multiple data sources and makes real-time decisions to optimize energy consumption and comfort, eliminating the need for complex manual control procedures.
Solution Approach 2:
The system replaces traditional mechanical thermostat control with an intelligent software-based forecasting and optimization platform. This substitution enables sophisticated energy efficiency optimization through computational algorithms while maintaining simple physical hardware infrastructure.
Data Source
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AI summary
The invention relates to a computer-implemented method for controlling the environmental state of a zone (1a-d), comprising acquiring (M1) planning data (21) relating to the planning of future events from a database (12), acquiring (M2) personal data (22) relating to the current locations and movements of persons, acquiring (M3) environmental data (23) relating to the current environmental state of at least one zone (1a-d), calculating (M4) a predicted occupancy (24) of the at least one zone (1a-d) based on the planning data (21) and the personal data (22), calculating (M5) a comfort environmental state (25, 26) of the at least one zone (1a-d) based on the planning data (21), the personal data (22), the environmental data (23) and the predicted occupancy (24) of the zone (1a-d), and controlling (M6) an air conditioning system (2a-d) for the at least one zone (1a-d) based on the calculated comfort environmental condition.