HVAC Controller Predicting Critical Peak Pricing Events
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Solution Overview
Problem
HVAC systems lack an efficient mechanism to adjust energy consumption in response to varying utility pricing during peak demand periods, requiring manual adjustments or automated communication links that may not be feasible for all users.
Innovation Solution
An HVAC controller that allows manual entry of utility pricing schedules and predicts Critical Peak Pricing events, modifying energy consumption by adjusting setpoints based on observed environmental conditions and load measurements, without requiring automated communication links.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If HVAC systems use automated communication links to receive utility pricing signals, then energy consumption can be optimized during peak periods, but device complexity and installation cost increase
Solution Approach 1:
The HVAC controller automatically performs multiple functions including receiving utility pricing signals, predicting CPP events, observing environmental conditions, and adjusting setpoints without requiring user intervention or external communication infrastructure. The system serves itself by integrating all these capabilities into a single local device.
Solution Approach 2:
The patent introduces an intermediary prediction algorithm that processes utility pricing signals and environmental data to generate predicted CPP events. This intermediary layer translates complex utility pricing structures into actionable setpoint adjustments, simplifying the overall system architecture.
2Use of energy by moving object
If users manually adjust HVAC setpoints in response to utility pricing, then energy bills can be reduced, but user time and operational complexity increase
Solution Approach 1:
The HVAC controller automatically receives utility pricing signals, predicts CPP events, observes environmental conditions, and adjusts setpoints without requiring user intervention. The system performs all these energy optimization functions autonomously, eliminating the need for users to manually monitor pricing or adjust settings.
Solution Approach 2:
The system continuously monitors utility pricing signals and environmental conditions, then automatically adjusts HVAC setpoints based on this feedback. This closed-loop control ensures energy optimization occurs automatically in response to changing utility rates without user involvement.
3Use of energy by moving object
If HVAC controllers continuously monitor environmental conditions and utility pricing, then energy consumption can be minimized during peak periods, but device complexity increases
Solution Approach 1:
The HVAC controller is designed as a multi-functional device that combines utility signal reception, CPP event prediction, environmental condition monitoring, and automatic setpoint adjustment all in one unit. This universal approach consolidates multiple functions into a single controller, managing complexity through integration rather than multiplication of separate components.
Solution Approach 2:
The controller performs preliminary actions by predicting CPP events before they occur and pre-adjusting setpoints accordingly. This proactive approach allows the system to prepare for peak pricing periods in advance, optimizing energy consumption without requiring complex real-time decision-making during actual peak events.
Data Source
AI summary
The present disclosure provides a method for operating an HVAC system for conditioning inside air of a building. The HVAC system includes an HVAC unit and a local HVAC controller. In some instances, control of an HVAC system may be modified based upon predictions of Critical Peak Pricing (CPP) events. For example, and in an illustrative but non-limiting example, a local HVAC controller may control an HVAC unit in accordance with at least one nominal HVAC control parameter, such as a nominal setpoint. At least one measure related to an environmental condition in or around the building and/or load on the HVAC unit may be observed, and a CPP event of a utility supplying power to the building may be predicted based at least in part on the observed measure(s). If the CPP event is predicted, the local HVAC Controller may then control the HVAC unit according to at least one CPP HVAC control parameter, which results in the HVAC unit consuming less energy during the CPP event relative to controlling according to the nominal HVAC control parameter(s).