Cloud-Based Building Automation for Dynamic Energy Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current building automation systems use reactive control strategies that do not consider changes in energy prices or occupant preferences, leading to inefficiencies and discomfort, as they rely on fixed schedules rather than dynamic adjustments based on weather and energy market data.
Innovation Solution
A cloud-enabled energy management control system that integrates weather, occupancy, and energy price data to generate optimized control signals for building systems, allowing for proactive and personalized management of temperature, humidity, and lighting across different zones, using a dual-loop structure for micro-zoning and high-level strategy planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If reactive control strategies with fixed schedules are used, then system simplicity is maintained, but energy efficiency and cost effectiveness deteriorate
Solution Approach 1:
The patent transforms the static, fixed-schedule control system into a dynamic one that continuously adjusts building systems based on real-time energy price signals, weather conditions, and occupancy data. The control strategy evolves from reactive to proactive by using predictive algorithms that anticipate optimal operation times for HVAC and lighting systems.
Solution Approach 2:
The system implements closed-loop feedback by continuously monitoring energy consumption, comparing it against real-time pricing signals and weather forecasts, and automatically adjusting control parameters. This feedback mechanism enables the system to learn from past performance and optimize future operations based on actual energy costs and environmental conditions.
2Ease of operation
If centralized facility manager control is used, then system management is simplified, but occupant comfort and personalization deteriorate
Solution Approach 1:
The patent divides the building into multiple controllable zones with independent control capabilities. Each zone can be managed according to its specific occupancy patterns and preferences, while the overall system remains coordinated through the facility management platform. This segmentation enables both centralized oversight and localized customization.
Solution Approach 2:
The system introduces an intelligent control layer that acts as an intermediary between facility managers and occupants. This layer translates occupant preferences into optimized control commands that balance comfort requirements with energy efficiency goals, mediating between centralized management constraints and individual occupancy needs.
3Loss of energy
If real-time data collection and processing is implemented, then energy optimization improves, but system complexity and computational requirements increase
Solution Approach 1:
The patent extracts computationally intensive data processing and optimization algorithms from the local building control system and relocates them to external cloud computing platforms. This extraction allows the building system to leverage powerful remote computational resources without increasing local hardware complexity or infrastructure requirements.
Solution Approach 2:
The system employs a multi-functional control platform that handles diverse tasks including real-time data acquisition, weather forecasting integration, energy price signal processing, occupancy pattern analysis, and optimization algorithm execution. This universal platform consolidates multiple functions into a single integrated system, reducing overall complexity.
Data Source
AI summary
A method of controlling energy consumption in a building. The method includes receiving occupant request data including a plurality of requests, wherein each of the plurality of requests corresponds to one of a plurality of zones in the building wherein the occupant request data is received via a cloud computing resource. The method also includes receiving weather data including at least one of current weather measurement data and weather forecast data wherein the weather data is received via a cloud computing resource. In addition, a facility management rule is received via a cloud computing resource. Further, the method includes generating a plurality of output control signals via cloud computing resource, wherein each of the plurality of output control signals is based on one of a plurality of requests and predicted occupant schedules, energy price data and the facility management rule. The control signals are generated by using simulation-based model predictive control method to determine a set of optimized control signals based on optimized energy use or optimized energy cost. The optimized control signals are transmitted to controllers.