Machine Learning Based HVAC Thermostat Control

Machine Learning-Based Energy-Efficient HVAC System for Optimal Climate Control optimizes building energy efficiency through real-time predictive analytics. The system analyzes real-time sensor data to predict optimal operating conditions, continuously adjusting HVAC settings to minimize energy consumption while maintaining desired comfort levels. This predictive approach enables the system to adapt to changing environmental conditions, occupancy patterns, and weather patterns, leading to improved energy efficiency and reduced operational costs.

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Energy-efficient HVAC system that leverages reinforcement learning to optimize both performance and comfort. The system employs a Transformer-based reinforcement learning model that learns optimal control sequences for HVAC systems by predicting both immediate and long-term outcomes based on current states. This enables adaptive control that balances energy savings with occupant comfort, while continuously adapting to changing environmental conditions. The system employs attention mechanisms to focus on relevant states and actions during training, ensuring efficient learning of both short-term and long-term objectives.

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A system and method for optimizing HVAC control in buildings by dynamically adapting to individual users' schedules and preferences. The system collects data on building occupancy patterns, HVAC operation, and user comfort requirements across different time windows, then uses reinforcement learning to determine optimal HVAC control strategies. The system integrates this data with user-specific schedules to provide personalized HVAC settings, enabling buildings to adapt to changing user needs throughout the day.

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Optimizing HVAC system performance through zone-level control of heating, ventilation, and air conditioning (HVAC) systems in buildings. The method integrates real-time HVAC data with occupant comfort feedback and occupancy schedules to determine optimal zone control strategies. The system employs machine learning models to predict future conditions and occupant preferences, enabling precise zone-specific control decisions that balance comfort, energy efficiency, and operational reliability.

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Predictive control strategy for commercial HVAC systems that optimizes energy efficiency while maintaining indoor thermal comfort and air quality. The strategy employs a novel black-box predictive model that combines state-space dynamics of the HVAC system with machine learning architecture, specifically using a recurrent neural network. This architecture allows for multi-step predictions of indoor environmental parameters, enabling the system to anticipate and adapt to changing conditions without requiring explicit physical models. The predictive model is trained using a combination of historical data and control inputs, enabling the system to learn optimal operating parameters through iterative optimization.

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Smart thermostats that leverage transfer learning from one environment to adapt to new conditions. The system employs a pre-trained machine learning model that is initially trained on a specific set of environments, then fine-tuned to optimize performance in a new environment. This approach enables the thermostat to learn from a broader range of conditions without requiring extensive retraining, while maintaining its ability to adapt to specific environmental characteristics.

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Control of heating, cooling, ventilation, and/or air conditioning (HVAC) systems in buildings is enhanced through predictive control strategies that account for various factors influencing building comfort. The control system employs a thermal model of the building with constraints and disturbances to optimize HVAC operations. This approach enables more efficient and comfortable building operation compared to traditional reactive control methods, particularly in buildings with complex thermal dynamics.

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A method and system for generating and controlling HVAC systems using machine learning. The method employs reinforcement learning to create personalized control strategies for HVAC systems by training agents on specific control sequences. The trained agents are then used to generate target control models that incorporate the learned strategies. This approach enables the creation of customized control models that adapt to specific system conditions and operational requirements, while leveraging the efficiency of machine learning algorithms to automate the control process.

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Self-governed HVAC control using AI to optimize building energy efficiency and occupant comfort. The system involves two main processes: 1) Maximizing energy flow through the building using AI to analyze historical data and predict future energy needs. This allows the HVAC system to proactively adapt and optimize energy usage based on changing conditions. 2) Managing thermal balance using dynamic modulation of the HVAC system to compensate for variations in internal and external conditions. This involves AI calculating the ideal HVAC settings for each zone based on factors like occupancy and weather. The AI then recommends adjustments to the HVAC system to maintain comfort and efficiency over time.

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A reinforcement learning-based HVAC control system that optimizes heating, ventilation, and air conditioning (HVAC) operations through continuous learning and adaptation. The system employs an agent that interacts with the environment through behavior, with the agent's actions influencing future states and rewards. The agent learns to optimize HVAC parameters through a reward function that incorporates both indoor temperature and energy consumption objectives, while also incorporating a clipped loss function to prevent over-optimization. This continuous learning process enables the agent to adaptively balance comfort and energy efficiency, achieving improved long-term performance compared to traditional rule-based approaches.

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A method and apparatus for optimizing building heating and cooling through predictive modeling of environmental changes. The system employs a recurrent neural network (RNN) to predict future environmental conditions from historical data, then uses this prediction to guide real-time control of HVAC systems. By continuously monitoring environmental factors and learning their patterns, the system dynamically adjusts heating and cooling output to maintain optimal conditions, enabling more efficient energy management compared to traditional manual control methods.

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Machine learning-based HVAC control system for public buildings that optimizes energy consumption while maintaining occupant comfort. The system uses machine learning algorithms to analyze building energy consumption patterns and HVAC performance, then dynamically adjusts supply air temperature to maintain optimal conditions. The system incorporates historical data, real-time environmental conditions, and occupant comfort metrics to predict and optimize HVAC performance. This approach enables energy savings while maintaining occupant comfort, particularly in large public buildings where energy consumption is a significant concern.

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Predictive HVAC control system for commercial buildings that enables personalized temperature management through machine learning analysis of individual room characteristics. The system employs a network of sensors to monitor temperature, occupancy, and environmental conditions across the building, then applies predictive analytics to generate optimized temperature control instructions for each room. By incorporating occupancy patterns, user preferences, and environmental factors, the system provides targeted temperature adjustments across multiple rooms, enabling buildings to achieve optimal energy efficiency and occupant comfort while minimizing energy consumption.

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Building management system (BMS) that optimizes temperature control in zones by learning from historical occupancy patterns. The system trains multiple dynamic models to represent the zone's response to occupancy, then determines the optimal temperature setpoint trajectory through direct policy optimization. This approach enables real-time energy efficiency while maintaining thermal comfort through adaptive control, without requiring extensive training data.

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HVAC system optimized through neural network-based predictive modeling. The system employs interconnected artificial neural networks to predict building temperature and energy consumption based on environmental data. These networks are trained using initial operational data from existing HVAC systems, enabling the system to learn optimal operating parameters without requiring extensive physical modeling. The predictive model continuously updates its parameters through online supervised learning, enabling real-time energy optimization and temperature control.

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An adaptive HVAC control system that predicts when a user will be away from a space and when they will return, allowing optimized energy savings and comfort. The system uses machine learning to train a model based on user behavior patterns. It receives the current time as input and outputs predicted return time and setpoint temperature for when the user is away. The model is trained using historical occupancy and temperature data. This allows the system to automatically adjust HVAC settings for energy savings when the user is away and bring them back to comfortable levels before return.

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Optimization control method for indoor thermal environment through deep reinforcement learning that improves learning efficiency by leveraging neural networks to predict and control heating capacity. The method employs a neural network model to iteratively predict indoor temperature, air-conditioning power consumption, and brain activity, which is then used to train a DQN agent to optimize heating capacity decisions. The agent learns to balance comfort constraints with energy efficiency through continuous trial and error, achieving improved learning efficiency compared to traditional model-based approaches.

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Air conditioning control system that optimizes temperature distribution in a space using a learning-based approach instead of rules. An infrared sensor detects the space's thermal image, which is used to determine the temperature distribution. A server controls the air conditioner using a learned deep reinforcement learning model to bring the detected distribution closer to the target. The learning is iterative, updating the model after each cycle based on the new detected distribution. This adaptive learning improves air conditioning efficiency compared to fixed rules.

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Smart thermostat system for multi-zonal buildings that uses artificial intelligence (AI) algorithms and Model Predictive Control (MPC) techniques deployed on the cloud to optimize energy consumption while maintaining comfort. The system involves smart thermostats with sensors in each zone that send data to the cloud for processing. AI algorithms analyze occupancy, weather, building profiles, etc to predict optimal setpoints. MPC optimizes control of heating/cooling systems. Commands are sent back to switch valves/power. The system continuously learns and updates itself.

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Temperature control method, device, and system that optimizes temperature adjustments through intelligent learning. The method employs an estimated network model trained from historical data to predict optimal temperature adjustments. The model's performance is evaluated using a greedy strategy that selects the most effective learning action values based on the estimated model's performance. This approach enables the system to dynamically adapt to changing environmental conditions while minimizing energy consumption and temperature fluctuations.

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