Forecast-based automatic scheduling of a distributed network of thermostats with learned adjustment
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Solution Overview
Problem
Current energy management systems for commercial sites, especially small footprint retail and food service chains, lack centralized control and scalability, leading to inefficient energy usage and high costs due to outdated manual or custom-designed solutions that fail to account for various weather conditions affecting perceived temperature.
Innovation Solution
A cloud-based energy management system with a centralized service unit that collects weather data to calculate apparent temperature, selects and transmits automated schedules to energy management devices, enabling optimized temperature control across multiple sites and reducing energy consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual or programmable thermostats are used at individual sites, then local temperature control is achieved, but centralized management and scalability are lost
Solution Approach 1:
The system segments temperature control into two independent layers: local execution (thermostats at individual sites) and centralized management (cloud-based platform). Each thermostat operates autonomously using received schedules and local sensors, while the central platform provides high-level scheduling and monitoring. This segmentation enables both local control and centralized management without interference.
Solution Approach 2:
The patent introduces an intermediary communication layer (network infrastructure including WiFi, cellular, or other wireless networks) that connects individual thermostats to the centralized cloud platform. This intermediary enables scalable centralized management by allowing multiple thermostats to communicate with the central system without direct point-to-point connections, resolving the contradiction between local operation and centralized control.
2Loss of energy
If custom-designed energy management systems are implemented at individual sites, then site-specific energy optimization is achieved, but cost and scalability are worsened
Solution Approach 1:
The patent implements a universal thermostat design that can be deployed at any number of sites without custom modification. The same hardware model serves multiple functions: local temperature control, weather-based schedule adjustment, and centralized platform communication. This universality achieves site-specific optimization through software configuration rather than hardware customization, dramatically reducing cost and improving scalability.
Solution Approach 2:
The system achieves site-specific energy optimization by changing software parameters (temperature setpoints, schedule timing, weather data sources) rather than hardware configuration. Each site receives customized temperature schedules based on its specific weather conditions, occupancy patterns, and energy costs, all managed through software updates from the centralized platform without requiring custom hardware design.
3Ease of operation
If traditional temperature-based control is used, then simple thermostat operation is maintained, but weather conditions affecting perceived temperature are not accounted for
Solution Approach 1:
The system performs preliminary action by proactively adjusting temperature schedules based on forecasted weather conditions before they occur. The centralized platform receives weather forecasts, calculates apparent temperature changes, and updates thermostat schedules in advance, so thermostats automatically adapt to upcoming weather conditions without real-time intervention. This maintains operational simplicity while adding weather responsiveness.
Solution Approach 2:
The patent implements a feedback loop where the centralized platform continuously monitors actual weather conditions, compares them to forecasts, and adjusts temperature schedules accordingly. Weather data (temperature, humidity, wind speed) feeds into apparent temperature calculations, which then feed back into schedule optimization. This closed-loop feedback enables automatic adaptation to weather conditions while keeping individual thermostat operation simple.
Data Source
AI summary
Heating and cooling systems at various geographical locations are controlled by a central energy management service unit to maintain comfortable indoor temperatures. In some weather conditions, people may intuitively prefer a slightly warmer or cooler indoor temperature. In systems equipped with environmental learning capabilities, an apparent outdoor temperature is determined based on the geographic location itself, the season at the geographic location, the forecasted actual temperature, and one or more seasonal weather factors such as wind velocity or humidity. The apparent temperature and a trained machine learning system are used to select an automated schedule for the geographic location to be directly transmitted to devices at the location. The automated schedule can vary from typical schedules by causing the heating and cooling systems to maintain a temperature that is slightly warmer or cooler than typical indoor temperatures.