Weather Based Thermostat Control for HVAC Systems

Adapting heating mode to individual comfort needs through machine learning-based predictive modeling. The system analyzes real-time temperature, humidity, and wind data to predict weather patterns, then adjusts heating parameters to maintain optimal indoor conditions. The predictive model incorporates advanced statistical techniques, including ARIMA and LSTM networks, to capture complex relationships between environmental factors and human comfort responses. This enables personalized heating strategies that dynamically adapt to changing weather conditions, ensuring optimal indoor comfort while minimizing energy consumption.

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Automatically adjusting cabin climate conditions of a vehicle based on user preferences, departure times, and external weather conditions. The vehicle system monitors outdoor weather, user schedules, and navigation data to predict departure times. It can then proactively adjust the cabin climate to reach a desired setpoint by the predicted departure time. If the user requests climate control changes, it balances that with the proactive adjustments. The system can also send climate control requests to remote devices to precondition the cabin before starting the vehicle.

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HVAC system optimization using weather forecasting enables proactive control of indoor climate conditions. The system analyzes future weather patterns and adjusts HVAC operation in real-time to maintain optimal indoor conditions. This predictive approach enables the system to anticipate and respond to temperature and humidity fluctuations, thereby improving energy efficiency and indoor air quality. The system integrates weather data with traditional HVAC controls to dynamically adjust fan speeds, blower operation, and refrigerant flow, ensuring optimal indoor climate conditions while minimizing energy consumption.

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Air conditioning system that dynamically adjusts indoor temperature based on solar radiation levels. The system monitors both solar radiation and room temperature to determine the optimal indoor temperature. When solar radiation exceeds a predetermined threshold, the system automatically sets the room temperature to a reference value. However, if the room temperature exceeds this reference, the system adjusts the temperature threshold to prevent excessive heating. This approach enables precise temperature control even in high-sunlight environments, while preventing overheating.

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Real-time weather control for air conditioning systems that enables users to receive personalized weather alerts and instructions directly on the control panel. The system utilizes a communication link to receive weather forecasts and intensity data, then dynamically displays relevant weather conditions and alerts on the control panel. This enables users to receive weather-specific information and instructions without relying on separate environmental monitoring devices. The system can switch between weather conditions and intensity levels based on the user's preferences, providing a more intuitive and proactive approach to weather management.

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Smart thermostat that can control the ambient temperature in one area when the thermostat is located in a different area. The thermostat uses setpoint modification factors to adjust the desired temperature based on factors like time of day, season, location, and weather conditions. This allows the thermostat to better regulate the temperature in the controlled area even when it's located in a different area.

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Smart air conditioning control method and system that uses artificial intelligence to optimize energy consumption and indoor comfort of air conditioning systems. The method involves using a deep learning model to predict future outdoor temperatures and humidity, then adjusting the indoor temperature setpoint based on the predictions to balance energy savings and comfort. The model learns over time to make better adjustments. The system collects indoor and outdoor environment data from sensors, runs the predictions through the model, and updates the model based on the resulting energy consumption and comfort levels.

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Smart thermostat with enhanced features to reduce energy usage, improve user comfort, and provide home monitoring. The thermostat has advanced capabilities like dynamic energy optimization based on real-time rates, demand response, and comfort balancing. It also has presence detection using sensors and devices to automatically arm/disarm the system when authorized users are present. The thermostat communicates with remote servers for rate plans and demand response signals.

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Air-conditioned ventilation system that automatically adjusts indoor temperature without manual operation. The system integrates weather forecasting data with location-based control, enabling precise temperature regulation based on real-time weather conditions. The system continuously monitors indoor temperature and adjusts ventilation rates accordingly, eliminating the need for manual operation or scheduling.

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Air conditioning control method and device that enables dynamic adaptation to weather conditions through user-defined scene templates. The method employs a user-configurable scene template system that allows users to define specific operating conditions for their air conditioning system. The system then dynamically adjusts the system's performance parameters to match these defined conditions, providing personalized comfort and energy efficiency.

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Regulating heating, cooling, and air conditioning systems based on weather forecasts to improve comfort and efficiency. The system integrates weather data into conventional temperature-based control by receiving forecast information through a dedicated receiver. This enables proactive adjustment of heating/cooling performance to anticipate temperature changes, maintaining optimal indoor conditions even before external temperature shifts occur.

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A weather forecast-based air conditioning system adjustment method that optimizes indoor temperature control in subway stations by predicting cooling demand through weather forecasts. The method involves collecting real-time indoor and outdoor temperature and humidity data, as well as forecasted weather conditions, to determine the required cooling load. By analyzing the forecasted cooling demand, the system adjusts its control strategy to achieve indoor temperature targets within a specified time frame, enabling more precise and efficient temperature control compared to traditional load-based control methods.

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Thermostat for controlling heating, ventilation, and air conditioning systems that automatically adjusts temperature settings based on environmental conditions. The thermostat incorporates sensors for temperature and humidity, and employs a dew point detection method to maintain optimal heating levels. The system automatically adjusts temperature settings when the dew point exceeds the setpoint, ensuring the air remains within the desired temperature range. This feature prevents overheating and condensation issues that can occur when heating systems are set too low.

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Predictive free cooling system for HVAC systems that optimizes energy savings through precise temperature control. The system employs advanced sensors and algorithms to monitor refrigerant flow and temperature dynamics, enabling accurate detection of optimal cooling periods. This predictive approach eliminates the need for manual intervention, reduces energy waste, and eliminates the need for frequent system updates and maintenance. The system integrates with existing building management systems (BMS) to automate the control sequence, ensuring continuous operation while maintaining precise temperature control.

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Indoor climate control system that integrates independent fresh air devices and HVAC through intelligent linkage control, enabling energy-efficient operation by dynamically coordinating heating and ventilation modes. The system features an integrated controller that monitors and regulates indoor air quality, temperature, and humidity while automatically controlling HVAC operation through intelligent linkage modes. The system incorporates sensors for air quality, carbon dioxide levels, and lighting conditions, enabling precise control of the indoor environment. The system achieves energy savings through optimized fresh air distribution and HVAC operation, while maintaining optimal indoor conditions.

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A system that automatically adjusts HVAC operation parameters based on real-time weather forecasts. The system maintains multiple control plans for different weather scenarios and automatically selects the most appropriate plan when deviations from the forecast occur. This enables real-time optimization of HVAC performance while maintaining energy efficiency. The system continuously monitors weather conditions and selects the best control plan from a predefined set, ensuring continuous operation of HVAC equipment even when forecast accuracy is compromised.

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Air conditioner with improved comfort control through predictive solar radiation monitoring. The system enables precise temperature control by analyzing solar radiation data and comparing it against a baseline. When the baseline is met, the system adjusts the air conditioner's capacity accordingly. This predictive approach ensures optimal comfort even when solar radiation levels are variable, eliminating the need for traditional solar radiation sensors.

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A method and apparatus for optimizing heating system performance through adaptive predictive modeling. The system uses weather forecasts to determine optimal heating signals, incorporating specific building characteristics like insulation, window size, and orientation. By analyzing historical weather patterns and building-specific factors, the system dynamically adjusts heating parameters to achieve optimal energy efficiency. The method incorporates multiple weather parameters, including solar radiation, wind, and precipitation, and incorporates building-specific coefficients to account for unique thermal characteristics. The system continuously learns and adapts to changing weather conditions, enabling more precise heating control and improved energy savings.

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Intelligent temperature control system that optimizes room temperature based on real-time weather forecasts and historical data. The system integrates with location-based weather services to predict future temperature conditions, then adjusts room temperature settings in response. This enables proactive temperature management through both weather forecasting and past data analysis. The system also incorporates door and window opening detection, allowing precise temperature control during occupancy periods. The system can be integrated into various applications, including smart home systems and hotel room control systems.

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Air conditioner control method that combines meteorological parameters with air conditioning technology. The method analyzes weather patterns and temperature differences to predict future indoor temperature changes, then adjusts the air conditioner's operating parameters in advance to maintain optimal comfort. This approach enables proactive temperature control by anticipating changes in weather conditions before they occur, ensuring consistent indoor comfort levels throughout the day.

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