Building Occupancy Prediction for Energy-Saving Climate Control
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Solution Overview
Problem
Homeowners and building managers face inefficiencies and costs due to the need to manually adjust heating and lighting when away from their homes for extended periods, leading to discomfort upon return.
Innovation Solution
A system using sensors to detect occupancy and predict schedules, associating real-time events with occupancy states to adjust building parameters such as climate and lighting, ensuring energy conservation and comfort upon return.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Temperature
If the furnace and lights are kept operating at a high level when no occupants are present, then comfort is maintained, but energy consumption and costs increase
Solution Approach 1:
The system performs preliminary actions by predicting future occupancy states and pre-adjusting building parameters before occupants actually arrive or leave. The predictive schedule anticipates when the building will be occupied or unoccupied, allowing the system to proactively set temperature and lighting levels in advance, thus avoiding energy waste while ensuring comfort is ready when needed.
Solution Approach 2:
The system enables self-service by using occupancy sensors and predictive algorithms to automatically detect when the building is occupied or unoccupied and autonomously adjust building parameters without manual intervention. The system serves itself by making intelligent decisions based on sensor data and predicted schedules, eliminating the need for occupants to manually control temperature and lighting.
2Loss of energy
If the thermostat is manually adjusted to a lower temperature during extended absence, then energy costs are reduced, but comfort is compromised upon return
Solution Approach 1:
The system performs preliminary action by predicting the occupant's return time based on the predictive schedule and real-time events, then pre-adjusting the temperature and lighting to comfortable levels before the occupant actually returns. This eliminates the discomfort of returning to a cold or dark building while still maintaining energy savings during the unoccupied period.
Solution Approach 2:
The system uses feedback from occupancy sensors, door events, and real-time location data to continuously monitor and adjust building parameters. The predictive schedule provides feedback about expected occupancy patterns, allowing the system to learn and adapt, ensuring both energy efficiency and comfort are optimized based on actual occupancy behavior.
3Measurement precision
If multiple sensors and real-time event detection are used to accurately determine occupancy state, then energy conservation is improved, but system complexity increases
Solution Approach 1:
The system merges multiple data sources including occupancy sensors, door event detectors, and real-time location events into a unified predictive schedule. By combining these diverse inputs through a centralized prediction algorithm, the system achieves high occupancy detection accuracy without requiring each individual sensor to be overly complex, as the intelligence is distributed across the integration layer rather than concentrated in single components.
Data Source
AI summary
Methods and systems are described for controlling parameters in a building. According to at least one embodiment, a method for controlling a building system includes using at least one sensor to detect occupancy in a building over time, determining a predictive schedule based on the occupancy detected with the at least one sensor, and associate real time events that occur simultaneously with an occupancy state of the predictive schedule.