Building Energy Control Using Occupancy and Weather Data
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Solution Overview
Problem
Traditional building automation systems (BAS) operate on fixed schedules and assumptions, failing to utilize dynamic occupancy, weather, and energy price data effectively, leading to suboptimal energy management and occupant comfort.
Innovation Solution
An energy management control system that integrates occupancy data, weather data, and energy price data to generate output control signals for building automation systems, enabling proactive and optimized energy management by adjusting building control devices such as thermostats and lighting systems, and implementing strategies like pre-cooling or pre-heating to avoid peak energy load times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If traditional building automation systems operate on fixed schedules and maximum occupancy assumptions, then the system operation is simple and reliable, but energy consumption is suboptimal and occupant comfort is compromised
Solution Approach 1:
The system transitions from fixed static schedules to dynamic real-time control by continuously adjusting building control device setpoints based on current occupancy data, weather conditions, and energy price signals. This dynamic adaptation enables optimal energy consumption while maintaining comfort without requiring complex manual intervention.
Solution Approach 2:
The system implements closed-loop feedback by continuously monitoring occupancy status, weather conditions, and energy consumption, then using this information to adjust control signals in real-time. This feedback mechanism enables the system to automatically optimize energy usage based on actual building conditions rather than relying on fixed assumptions.
2Productivity
If building control systems use fixed schedules and maximum occupancy assumptions, then the control logic is simple, but energy management efficiency is reduced
Solution Approach 1:
The system performs preliminary actions by pre-cooling or pre-heating building zones during periods of low energy cost or low occupancy, before peak demand periods occur. This anticipatory control strategy reduces peak energy consumption and improves overall management efficiency without requiring complex real-time decision-making during high-demand periods.
Solution Approach 2:
The system dynamically changes control parameters such as temperature setpoints, lighting levels, and equipment operation schedules based on varying occupancy patterns, weather conditions, and energy price signals. This parameter adaptation enables efficient energy management by adjusting operational characteristics to match actual building needs rather than using fixed conservative assumptions.
3Reliability
If building automation systems do not utilize dynamic occupancy and weather data, then the system operation is straightforward, but occupant comfort is compromised
Solution Approach 1:
The system integrates multiple data sources including occupancy sensors, weather forecasts, and energy price signals into a unified control framework that manages multiple building systems (HVAC, lighting, blinds). This multi-functional integration ensures reliable occupant comfort across diverse conditions while sharing common processing infrastructure to manage complexity efficiently.
Solution Approach 2:
The system divides the building into controllable zones and processes control decisions independently for each zone based on local occupancy data and environmental conditions. This segmentation allows the system to provide reliable comfort in occupied zones while reducing energy consumption in unoccupied areas, managing complexity through modular zone-level control rather than building-wide centralized processing.
Data Source
AI summary
A method of controlling energy consumption in a building includes receiving occupancy data including at least one of occupant request data and occupant schedule data, receiving weather data including at least one of current weather measurement data and weather forecast data, generating an output control signal based on the occupancy data and the weather data, and transmitting the output control signal to a building automation system (BAS) of the building. The occupant request data includes a current request, and the occupant schedule data includes a predicted occupant schedule. The output control signal adjusts a building control device in a zone in the building.