Method for energy saving by scheduling of the energy supplied for air-conditioning, according to the previous and/or expected power consumption and the knowledge in advance of weather data
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Solution Overview
Problem
Current HVAC systems for residential and industrial use lack the ability to automatically or semi-automatically adjust energy consumption based on advanced weather data and user inputs, leading to inefficient energy use and increased pollutant emissions, particularly in multi-user settings where centralized control systems do not allow real-time adjustments or democratic decision-making.
Innovation Solution
A method and device for automatically managing central HVAC systems by integrating immediate and forecasted weather data, user inputs, and solar radiation to optimize energy consumption, allowing users to democratically decide on system operation and energy use through a network of sensors, actuators, and a control panel that processes data for real-time and scheduled operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If HVAC systems use immediate weather data only for control, then the system is simple to operate, but energy saving potential is limited
Solution Approach 1:
The system acquires weather forecast data in advance (24-48 hours ahead) and performs preliminary thermal simulations to predict future heating/cooling demands. This allows the HVAC system to be pre-conditioned before extreme weather events, reducing peak energy consumption while maintaining a manageable control structure through scheduled pre-conditioning operations.
Solution Approach 2:
The control system dynamically adjusts operational parameters based on the combination of immediate sensor data and forecasted conditions. The simulation model continuously updates thermal predictions as new weather data becomes available, allowing the system to adapt its heating/cooling strategy in real-time while incorporating advance weather information for optimized energy management.
2Ease of operation
If HVAC systems allow individual user control, then user comfort is improved, but centralized energy management efficiency deteriorates
Solution Approach 1:
The system divides the building into multiple controllable zones with individual user control capabilities. Each zone can be independently managed by occupants while the central system provides coordinated control based on aggregate energy optimization goals. This segmentation allows local autonomy without sacrificing overall energy efficiency through centralized simulation-based scheduling.
Solution Approach 2:
The system implements multi-level feedback mechanisms where individual user actions are monitored and fed back to the central simulation model. The model adjusts future scheduling recommendations based on actual user behavior patterns, creating a closed-loop system that balances individual comfort preferences with centralized energy optimization objectives through continuous adaptation.
3Use of energy by moving object
If HVAC systems use advanced weather forecasts and simulations, then energy saving is improved, but system complexity and computational requirements worsen
Solution Approach 1:
The system employs reduced-order thermal simulation models that use simplified parameter relationships to predict building thermal behavior. By changing from complex full-physics simulations to parameter-based predictive models, the system achieves adequate energy prediction accuracy with significantly reduced computational requirements, enabling real-time integration with weather forecast data without excessive complexity.
4Temperature
If HVAC systems operate continuously to maintain comfort, then room comfort is ensured, but energy consumption increases
Solution Approach 1:
The system performs pre-conditioning of spaces before forecasted extreme weather events by adjusting temperatures in advance when conditions are favorable. This preliminary action reduces or eliminates the need for continuous high-capacity HVAC operation during peak demand periods, maintaining comfort while significantly reducing overall energy consumption through strategic advance preparation.
Solution Approach 2:
Instead of continuous operation, the system uses periodic HVAC cycling based on predicted thermal conditions and actual room temperature feedback. The simulation model determines optimal on/off scheduling that maintains comfort within acceptable ranges while minimizing runtime, replacing continuous operation with intelligent periodic activation based on forecasted and actual weather conditions.
Data Source
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AI summary
It is the object of the present invention to create an articulated and automatic system, which allows the reduction the primary energy consumption for room air-conditioning. Such objects are achieved by a device for automatic management and thermoregulation of central systems, that allows individual direct users, and only the users, in the most precise and absolutely democratic manner, to decide how the HVAC systems should operate, both in terms of operation duration and in terms of performance and yields. It is another object of the invention a complex method which allows the carrying out of the automatic room thermoregulation both by knowing the immediate weather data, and by the information obtained from the weather forecast, integrated with the user decision to restrict or, however, to regulate the primary energy consumption for the room air-conditioning, in respect to duration times of air-conditioning and expected room climate parameters.