Sensor Network Controller Predicting Built Environment States
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Solution Overview
Problem
Current intelligent lighting and environmental control systems lack the ability to predict future states of a built environment effectively, such as occupancy and energy consumption, which hinders efficient resource management and optimization.
Innovation Solution
A sensor network with a controller that collects and classifies sense signals from various sensors over time, using space and time dimensions, and other available information to predict future states like occupancy and energy consumption, enabling more efficient control of built environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional lighting and environmental control systems are used, then the system structure is simple, but the ability to predict future states and optimize resource management is insufficient
Solution Approach 1:
The system performs preliminary classification of sense values based on space type and timing before prediction, preparing data in advance to improve prediction accuracy. The controller classifies collected sense values according to the type of space and timing information, enabling more accurate future state predictions while maintaining a manageable system structure through organized data preparation.
2Measurement precision
If sense values are collected and classified based on space type and timing, then prediction accuracy improves, but data processing complexity increases
Solution Approach 1:
The system segments sense values into different categories based on space type (e.g., office, conference room, corridor) and timing (e.g., morning, afternoon, weekend). This segmentation allows the controller to process and analyze data more efficiently by handling similar types of data together, improving prediction precision while keeping data processing complexity manageable through organized categorization.
3Productivity
If future states are predicted based on classified sense values, then resource management efficiency improves, but computational requirements increase
Solution Approach 1:
The system applies partial action by predicting future states for specific spaces and time periods based on relevant classified sense values rather than processing all possible data. The controller focuses computational resources on predicting occupancy and environmental conditions for spaces that are likely to be occupied or require control, improving resource management efficiency while reducing overall computational power requirements.
Data Source
AI summary
Apparatuses, methods and systems for predicting a future state of a built environment with a sensor network are disclosed. One sensor network includes a plurality of sensors of a built environment and a controller. Each sensor is operative to generate a sense signal. The controller is operative to collect sense values that represent sense signals of the plurality of sensors, wherein the sense values are collected over time, classify each of the collected sense values based on a type of space of the built environment associated with a corresponding one of the plurality of sensors that generated the sense value, based on a timing of the sense value, and based on other available information of the built environment, predict a future state of each of the plurality of sensors based at least in part on the classifications of each of the sense values, and communicate the future state.