Smart Energy Platform for Dynamic HVAC Cost and Comfort Control
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Solution Overview
Problem
Existing systems fail to efficiently control energy consumption at properties due to inability to account for varying internal and external factors such as temperature, occupancy, and energy pricing, leading to inefficiencies and increased costs.
Innovation Solution
A smart energy platform that integrates data from smart thermostats, weather conditions, energy pricing, and solar panel generation using machine learning and AI to adapt energy usage schedules dynamically, optimizing energy consumption and generation based on user preferences and real-time data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single fixed schedule is used for energy consumption, then the system is simple to operate, but it cannot adapt to changing temperatures, occupancy, and energy pricing conditions leading to increased costs
Solution Approach 1:
The patent implements dynamic energy schedules that automatically adjust based on real-time conditions including temperature variations, occupancy detection, and energy pricing fluctuations. The system transitions from static predetermined schedules to dynamic adaptive scheduling, allowing the HVAC system to respond to changing environmental and operational conditions without requiring manual intervention.
Solution Approach 2:
The system incorporates multiple feedback loops including temperature sensors that monitor indoor and outdoor conditions, occupancy sensors that detect presence, and energy pricing data feeds that provide real-time cost information. This feedback enables the control algorithm to continuously optimize energy consumption patterns based on actual system performance and external conditions.
2Use of energy by moving object
If manual monitoring and adjustment of energy usage is implemented, then energy costs may be reduced, but the process becomes difficult to predict and inefficient to implement
Solution Approach 1:
The system enables self-service energy management by automatically performing monitoring, analysis, and adjustment functions without requiring user intervention. The control algorithm autonomously optimizes HVAC operation based on sensor data and pricing information, eliminating the need for manual energy management tasks while maintaining high energy efficiency.
Solution Approach 2:
The patent replaces manual mechanical adjustment processes with automated electronic control systems. Sensors, microprocessors, and communication modules substitute for human monitoring and manual thermostat adjustment, providing continuous optimization without the limitations of human reaction time and prediction accuracy.
3Loss of energy
If solar-generated energy is managed manually, then the property can offset energy purchases, but managing the use and sale of energy becomes difficult and time consuming
Solution Approach 1:
The system provides multi-functionality by simultaneously managing HVAC operation, solar energy generation, energy storage, and grid interaction through a single integrated control platform. The control algorithm optimizes when to consume solar energy, when to store excess energy, and when to sell back to the grid, all while managing HVAC systems, eliminating the need for separate manual management of each function.
Solution Approach 2:
The system performs preliminary actions by pre-charging energy storage systems when solar generation is high and electricity prices are low, and by pre-cooling or pre-heating spaces during periods of high solar availability. This proactive energy management maximizes the use of self-generated energy and reduces the need for expensive grid power during peak periods.
4Use of energy by moving object
If energy consumption is optimized for off-peak times, then costs are reduced, but the system requires complex prediction and adjustment that is difficult to implement
Solution Approach 1:
The system dynamically changes operational parameters including temperature setpoints, HVAC cycle timing, and energy storage charge/discharge rates based on real-time pricing signals and conditions. Rather than using fixed off-peak scheduling, the system continuously adjusts parameters to capture cost savings opportunities as they arise, adapting to both predictable patterns and unpredictable price fluctuations.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The smart energy platform effectively maintains a comfortable temperature at a property while minimizing energy costs by dynamically adjusting energy usage schedules in response to changing conditions, enhancing energy efficiency and user savings.
Implementation Method 1
solar panels installed on a property may be configured to collect sunlight received at the property and convert it into energy
Data Source
AI summary
A smart energy platform for controlling energy consumption at a property, the smart energy platform configured to: (i) receive an input from a user associated with the property indicating a maximum comfort temperature and a minimum comfort temperature of the property; (ii) receive property data; (iii) retrieve weather data; (iv) determine property usage; (v) receive energy pricing data from an energy company; (vi) build a model based upon the input from the user, the property data, the weather data, and the property usage; (vii) input the energy pricing data and the weather data into the model; (viii) output an energy schedule for the property to maintain a temperature of the property within the maximum comfort temperature and the minimum comfort temperature at a minimum cost to the user; and/or (ix) control the temperature of the property based upon the energy schedule for the property.