Smart Thermostat Setpoint Control for Peak Demand Charges
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Solution Overview
Problem
Residential customers lack an easy and automated way to manage peak power consumption to avoid extra charges associated with peak demand rates, as they are not informed when their total household power consumption is about to exceed thresholds, leading to negative impacts on utility bills.
Innovation Solution
A smart thermostat system that includes a processing system capable of receiving instantaneous energy usage data and adjusting the HVAC system's setpoint schedule to reduce energy consumption when projected peak usage is detected, using a custom API for communication with utility providers to implement demand charge management plans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If customers manually monitor and adjust their power consumption to avoid peak demand charges, then utility bill costs are reduced, but the complexity of operation and user burden increase significantly
Solution Approach 1:
The thermostat system automatically monitors total household power consumption, detects projected peak demand events, and autonomously adjusts HVAC setpoint temperatures to prevent exceeding demand thresholds. This self-service mechanism eliminates the need for customers to manually track or adjust their consumption patterns, resolving the contradiction between cost reduction and operational simplicity
Solution Approach 2:
The system continuously receives real-time power consumption data from utility providers via custom APIs, compares current usage against historical patterns and demand thresholds, and provides automated feedback by adjusting HVAC operations. This closed-loop feedback system enables automatic adaptation to peak demand conditions without requiring user intervention, addressing both cost reduction and ease of operation requirements
2Loss of energy
If the thermostat frequently adjusts setpoint temperatures to prevent peak demand charges, then energy cost savings are maximized, but user comfort may be compromised
Solution Approach 1:
The system uses machine learning algorithms to predict future power consumption patterns and identify projected peak demand events before they occur. By taking preliminary action to pre-adjust setpoint temperatures in anticipation of demand charges, the system prevents costly peaks while maintaining comfort during actual peak periods, resolving the contradiction between cost savings and comfort reliability
Solution Approach 2:
The thermostat dynamically adapts setpoint adjustments based on learned user preferences, historical comfort patterns, and real-time environmental conditions. Rather than applying fixed adjustments, the system modulates temperature changes to match user behavior patterns, ensuring comfort is maintained while still achieving demand charge reduction goals
3Extent of automation
If the thermostat system continuously monitors and adjusts to projected peak demand events, then automatic demand charge management is achieved, but the device complexity and computational requirements increase
Solution Approach 1:
The thermostat system integrates multiple functions including traditional temperature control, real-time power consumption monitoring, machine learning-based prediction, automated setpoint adjustment, and communication with utility providers via custom APIs. By consolidating these diverse functions into a single multi-functional platform, the system achieves high automation while managing complexity through functional integration rather than separate components
Data Source
AI summary
A thermostat may include one or more memory devices comprising a stored setpoint schedule, one or more temperature sensors configured to provide temperature sensor measurements, and a processing system configured to be in operative communication the one or more memory devices to determine a setpoint temperature, and in still further operative communication with a heating, ventilation, and air conditioning (HVAC) system to control the HVAC system based at least in part on the setpoint temperature and the temperature sensor measurements. The processing system may be configured to control the HVAC system by receiving an indication that a total instantaneous energy usage rate for a structure in which the thermostat is installed is projected to exceed a threshold amount; and altering the stored setpoint schedule to reduce an energy usage rate of the HVAC system.