HVAC Setpoint Temperature Control Using Forecasted Renewable Power
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Solution Overview
Problem
Conventional HVAC energy management systems lack automated control for optimizing energy consumption, requiring user intervention to determine optimal setpoint temperatures, and do not leverage renewable power sources for efficient operation.
Innovation Solution
An intelligent and predictive energy management system that uses a processor to determine the setpoint temperature based on actual or forecasted renewable power, allowing real-time adaptive control to maximize energy efficiency and user thermal comfort, by comparing forecasted and maximum power consumption with available renewable power.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional energy management systems are used for HVAC units, then user flexibility to select between thermal comfort and energy efficiency is provided, but automated control for optimizing energy consumption is not available and user intervention is required
Solution Approach 1:
The energy management system performs self-service by automatically determining optimal setpoint temperatures without requiring user intervention. The processor autonomously compares forecasted power consumption with available renewable power and adjusts HVAC operating conditions accordingly, enabling the system to manage itself while optimizing energy consumption.
Solution Approach 2:
The system performs preliminary action by using forecasted power consumption data and predicted renewable power generation before actual operation occurs. This allows the energy management system to pre-determine optimal setpoint temperatures and prepare HVAC operating conditions in advance, enabling proactive rather than reactive control.
2Loss of energy
If HVAC units operate without automated energy management, then system operation is simple, but energy consumption optimization is not achieved
Solution Approach 1:
The energy management system implements feedback by continuously monitoring actual renewable power generation from photovoltaic panels and comparing it with forecasted values. This feedback loop allows the system to adjust setpoint temperatures in real-time, ensuring optimal energy consumption while accounting for variations in renewable power availability.
Solution Approach 2:
The system performs preliminary action by using forecasted power consumption data and predicted renewable power generation before actual operation occurs. This allows the energy management system to pre-determine optimal setpoint temperatures and prepare HVAC operating conditions in advance, enabling proactive rather than reactive control.
3Loss of energy
If setpoint temperature is adjusted to maximize energy efficiency, then energy consumption is reduced, but user thermal comfort may be compromised
Solution Approach 1:
The energy management system applies partial action by adjusting setpoint temperatures only to the extent necessary to match available renewable power while maintaining acceptable thermal comfort levels. Rather than extreme temperature adjustments for maximum energy savings, the system finds optimal balance points that provide sufficient energy efficiency improvements without compromising user comfort.
4Loss of energy
If HVAC units operate based on forecasted renewable power, then energy efficiency is optimized, but measurement precision requirements increase
Solution Approach 1:
The system performs preliminary action by using forecasted power consumption data and predicted renewable power generation before actual operation occurs. This allows the energy management system to pre-determine optimal setpoint temperatures and prepare HVAC operating conditions in advance, enabling proactive rather than reactive control.
Data Source
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AI summary
The present disclosure relates to a device for determining a setpoint temperature of a heating, ventilation, and air-conditioning (HVAC) unit operating in a first mode, the device comprising a processor configured to obtain: a first parameter indicative of a forecasted power consumption of the HVAC unit, the forecasted power consumption of the HVAC unit based on a reference table, the reference table comprising an estimated power consumption of the HVAC unit at a plurality of timepoints; and a second parameter indicative of a renewable power generated by a renewable power source configured to supply renewable power to the HVAC unit; wherein the processor is further configured to: compare the first parameter and the second parameter, and determine the setpoint temperature based on the comparison of the first parameter and the second parameter. Further disclosed is a method for determining a setpoint temperature of the HVAC unit.