Adaptive Automation Module for Building Energy Scheduling
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Solution Overview
Problem
Existing programmable energy-saving devices for buildings, such as thermostats, face challenges in effectively reducing energy consumption due to improper programming, occupant discomfort from rapid changes, and instability caused by sporadic sensor events, with previous methods requiring significant computational power and memory resources.
Innovation Solution
An adaptive automation module that uses an event recorder and timeline pattern generator to create markers from historical sensor data, allowing for optimized scheduling of energy usage without intense computational needs, and can adjust settings based on external data like weather and power pricing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If neural networks are used to perform stochastic analysis of sensor data, then predictive output accuracy is improved, but computational power and memory resources required increase dramatically
Solution Approach 1:
The patent replaces expensive, resource-intensive neural networks with a simpler, less computationally demanding algorithm that achieves comparable predictive accuracy without requiring tremendous parallel computing power or significant memory resources
Solution Approach 2:
The patent changes the algorithmic approach from stochastic neural network analysis to a deterministic pattern recognition method, fundamentally altering how sensor data is processed to reduce computational requirements while maintaining predictive accuracy
2Speed
If reactive methods are used to control heating and cooling, then response to sensor input is immediate, but occupant discomfort increases due to rapid output change
Solution Approach 1:
The patent uses historical sensor data to predict future occupancy patterns and pre-adjust heating and cooling settings before occupancy changes occur, avoiding rapid output changes while maintaining responsive control
Solution Approach 2:
The patent implements a feedback mechanism that continuously monitors sensor data and adjusts heating and cooling output based on predicted occupancy patterns, balancing rapid response with occupant comfort by smoothing transitions
3Extent of automation
If predictive methods correlate all sensor events equally, then scheduling can be generated, but instability occurs due to sporadic sensor events in low traffic areas
Solution Approach 1:
The patent applies different correlation weights to different sensor events based on their location and significance, treating events in high-traffic areas differently from those in low-traffic areas to prevent instability while maintaining automated scheduling
Solution Approach 2:
The patent selectively correlates only certain sensor events that meet specific criteria, rather than treating all events equally, thereby achieving stable scheduling without being overly sensitive to sporadic events in low-traffic areas
Data Source
AI summary
A method and apparatus for a computer-implemented adaptive automation module comprising an event recorder to store one or more events for a predetermined period, and a timeline pattern generator logic to create a timeline for the predetermined period. The module further comprising marker creator logic to generate a marker to abstract the timeline data from the event data for controlling a device.