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 scalable solutions for optimizing energy usage across multiple locations, failing to account for various weather conditions that affect perceived temperature, leading to inefficient energy consumption and increased costs.
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 centralized control and optimization of energy usage across multiple sites while considering factors like temperature, wind velocity, humidity, and precipitation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual or programmable thermostats are used at individual sites, then each site can control its own temperature, but centralized control and scalable optimization across multiple sites cannot be achieved
Solution Approach 1:
A cloud-based energy management service unit acts as an intermediary between individual thermostats and centralized control. The service unit receives thermostat data, processes it through analytics modules, and sends back control commands, enabling centralized optimization without direct complex connections between all components
Solution Approach 2:
The energy management service unit provides multiple functions including data collection from thermostats, weather data integration, analytics processing, and control command generation within a single unified platform, eliminating the need for separate specialized systems for each function
2Productivity
If custom-designed energy management systems are implemented, then optimization for individual sites can be achieved, but scalability and cost-effectiveness for multiple sites are compromised
Solution Approach 1:
The system uses standardized thermostat devices and a replicated service unit architecture that can be deployed across multiple sites. The same software platform and processing logic are copied and instantiated at each site, enabling consistent optimization performance without custom design for each location
Solution Approach 2:
The energy management system is segmented into independent, modular components including individual thermostat units, a cloud-based service unit, and analytics modules. Each segment operates semi-independently but contributes to the overall system, allowing scalable deployment where sites can be added or removed without redesigning the entire system
3Ease of operation
If traditional temperature control is used without considering weather conditions, then simple control logic can be maintained, but perceived comfort and energy efficiency are reduced
Solution Approach 1:
The system incorporates feedback loops where thermostat data is continuously collected, analyzed against weather conditions and energy prices, and used to generate optimized control commands. This feedback mechanism enables dynamic adjustment of temperature settings based on real-time conditions, improving both comfort and energy efficiency
Solution Approach 2:
The system dynamically changes temperature setpoint parameters based on varying weather conditions, energy prices, and occupancy patterns. Instead of fixed temperature controls, the system adjusts parameters like heating and cooling setpoints in response to changing external conditions, optimizing comfort and energy usage
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.