Temperature controller, server, air conditioning system and temperature setting method
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Solution Overview
Problem
Existing electric appliances for temperature control, such as air conditioning systems, inefficiently utilize green electricity, contributing significantly to carbon emissions.
Innovation Solution
A temperature controller and server system that adjusts the set temperature based on a green electricity index, optimizing energy usage by increasing or decreasing the set temperature in response to changes in green electricity availability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If the set temperature is adjusted according to green electricity index to improve green electricity utilization, then carbon emissions are reduced, but temperature control stability deteriorates
Solution Approach 1:
The system performs preliminary actions by pre-cooling or pre-heating spaces when green electricity availability is high, storing thermal energy in the building structure (walls, floors, ceilings) before periods of low green electricity availability. This allows the temperature controller to maintain stable temperatures during periods when grid electricity is less environmentally friendly, reducing the need for frequent temperature adjustments and thereby maintaining temperature control stability while still achieving carbon emission reductions.
Solution Approach 2:
The system dynamically changes the set temperature parameter based on the green electricity index while incorporating prediction algorithms that anticipate future green electricity availability. This allows smooth parameter transitions rather than abrupt changes, maintaining temperature stability while responding to varying green electricity conditions. The prediction component enables proactive parameter adjustment that prevents temperature fluctuations.
2Productivity
If real-time temperature adjustment based on green electricity index is implemented to maximize green electricity usage, then green electricity utilization efficiency improves, but system complexity increases
Solution Approach 1:
The system implements feedback mechanisms where the temperature controller continuously monitors the green electricity index and adjusts temperature settings accordingly. The feedback loop incorporates both current green electricity availability and predicted future availability, enabling intelligent decision-making about when to adjust temperatures. This feedback-based approach maximizes green electricity utilization while using algorithmic predictions to simplify real-time decision-making, avoiding the need for complex manual control systems.
Solution Approach 2:
The temperature controller autonomously adjusts temperature settings based on received green electricity index data and internal predictions, without requiring complex external control systems or manual intervention. The system serves itself by making intelligent temperature adjustments based on energy availability information, reducing the need for sophisticated external control infrastructure while still achieving high green electricity utilization efficiency.
3Object-generated harmful factors
If temperature controller autonomously adjusts set temperature based on green electricity index to reduce carbon emissions, then carbon saving capability improves, but user comfort control deteriorates
Solution Approach 1:
The system performs preliminary temperature adjustments during periods of high green electricity availability, storing thermal energy in building structures before user-occupied periods. This allows the autonomous system to achieve carbon savings during off-peak hours while maintaining comfortable temperatures when users are present, effectively decoupling carbon saving operations from user comfort requirements.
Solution Approach 2:
The temperature control system dynamically adapts its behavior based on timing and usage patterns, implementing more aggressive autonomous adjustments during unoccupied periods and maintaining stricter temperature control during occupied periods. This dynamic approach allows the system to maximize carbon savings when user comfort is less critical while preserving user comfort control during important periods.
Data Source
AI summary
Disclosed is a temperature controller, a server, an air conditioning system and a temperature setting method. The temperature controller may be configured to operate in a carbon saving mode, and when operating in the carbon saving mode, the set temperature of the temperature controller is adjusted according to a green electricity index, and the green electricity index is the proportion of green electricity in a power grid to which the temperature controller is connected. When the proportion of the green electricity is high, i.e., the electric energy used is mainly from renewable energy sources, the set temperature is adjusted to produce more cold energy or heat energy for storage; and when the green electricity index decreases, the stored cold energy or heat energy can be used to meet the demand for cooling or heating, thereby reducing carbon emissions.