Distributed Thermostat Scheduling Using Apparent Weather Signals
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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retail and food service chains, lack centralized control and scalability, leading to inefficient energy usage due to outdated manual or programmable thermostats, and fail to account for weather conditions beyond temperature, resulting in reduced energy savings.
Innovation Solution
A cloud-based energy management system with a centralized service unit that collects weather data, calculates apparent temperature, and automatically adjusts thermostat schedules based on season and forecast, enabling direct communication with geographically distributed energy management devices to optimize energy usage.
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 control and scalability are lost
Solution Approach 1:
The system segments control functions by implementing distributed thermostats at individual sites that maintain local autonomy for temperature control, while simultaneously enabling centralized management through cloud-based connectivity. Each thermostat operates independently at the local level but can be remotely configured and monitored, resolving the contradiction between local control and centralized management.
Solution Approach 2:
A cloud-based energy management service unit acts as an intermediary between individual thermostats and central management. This intermediary enables centralized control capabilities without requiring direct connection between all thermostats and a central system, allowing scalable deployment while maintaining both local autonomy and centralized oversight.
2Device complexity
If traditional temperature-based scheduling is used, then simple thermostat operation is maintained, but weather conditions beyond temperature are not accounted for
Solution Approach 1:
The system pre-calculates apparent temperature values based on comprehensive weather data (temperature, humidity, wind speed) and stores these calculations in advance. This allows the thermostat to use pre-computed apparent temperature values without performing complex real-time calculations, maintaining operational simplicity while accounting for multiple weather conditions to optimize energy savings.
Solution Approach 2:
A cloud-based service acts as an intermediary that handles complex weather data processing and apparent temperature calculations. The thermostat itself remains simple, relying on the cloud service to perform comprehensive weather analysis and return optimized temperature schedules, thus maintaining device simplicity while capturing energy savings from multi-factor weather accounting.
3Manufacturing precision
If custom-designed energy management systems are implemented, then individual site optimization is achieved, but scalability and centralized control across multiple sites are compromised
Solution Approach 1:
The system implements a universal energy management platform that can serve multiple sites with different requirements through a single cloud-based service. The platform provides site-specific optimization capabilities while maintaining centralized control across all connected sites, enabling both individual site precision and multi-site scalability through a unified system architecture.
Solution Approach 2:
The system segments the energy management function into site-specific thermostat devices and a centralized cloud-based management service. This segmentation allows each site to receive customized optimization while the overall system maintains scalability and centralized control capabilities, resolving the contradiction between individual site precision and multi-site deployability.
4Ease of operation
If local site managers manually manage thermostats, then on-site control is possible, but resource availability and energy optimization are limited
Solution Approach 1:
The thermostat system implements self-service capabilities by automatically adjusting temperature schedules based on apparent temperature calculations and pre-configured preferences. This eliminates the need for continuous manual intervention by site managers while maintaining on-site control functionality, thereby improving energy optimization efficiency without sacrificing operational availability.
Solution Approach 2:
The system incorporates feedback mechanisms where thermostat performance and energy consumption data are automatically collected and analyzed by the cloud-based service. This feedback loop enables continuous optimization of energy usage while maintaining simple local operation, improving productivity in energy optimization without requiring additional human resources.
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.