Cloud Based HVAC Monitoring and Control Systems

Remote digital cloud platform for managing and optimizing energy systems in large-scale commercial and residential buildings. The platform integrates device-level sensors and edge computing to monitor and analyze building performance data, while leveraging machine learning and AI to optimize energy distribution and consumption. The system enables real-time monitoring of building-wide energy usage patterns, automates energy management processes, and provides predictive analytics for peak demand management and energy storage optimization. The platform integrates with existing smart home systems, enables remote maintenance and operation, and provides data analytics and reporting capabilities.

Source

Temperature and humidity monitoring system integrating IoT and cloud computing enables real-time monitoring of switchgear conditions through a networked architecture. The system comprises a perception layer with wireless sensors and a control layer that integrates temperature and humidity data from these sensors. The perception layer collects data from multiple sensors, which are then transmitted to a gateway platform for processing and analysis. The gateway uploads data to a cloud computing platform, where it is processed and analyzed through machine learning algorithms. The system enables remote monitoring, control, and decision-making through a mobile application, ensuring real-time insights into switchgear operating conditions.

Source

Cloud-based intelligent control system for building HVAC systems that optimizes energy efficiency and comfort through real-time monitoring and predictive analytics. The system integrates cloud computing, AI, and IoT sensors to provide continuous environmental data, predict unmeasured parameters, and dynamically adjust HVAC parameters based on real-time conditions. The system enables intelligent control of multiple HVAC components, including air conditioning, through a user-friendly interface, while incorporating advanced waste heat recovery strategies to maximize system performance.

Source

Real-time power monitoring system for improved energy efficiency that enables precise energy consumption analysis across entire facilities, buildings, or equipment. The system integrates temperature sensors, microcontrollers, and advanced data processing to provide real-time energy consumption data, enabling users to pinpoint areas of high energy use and implement targeted energy-saving strategies. The system also analyzes energy consumption patterns over time, allowing users to identify trends and optimize equipment operation for more energy-efficient solutions.

Source

Energy management platform that integrates real-time energy usage data with predictive analytics to optimize facility operations. The system collects and analyzes energy consumption patterns through IoT sensors, generating detailed usage data that is then analyzed by AI-driven predictive models. This enables proactive facility management, energy cost optimization, and predictive maintenance through automated alerts and alerts-driven decision-making. The platform integrates with external management systems to facilitate real-time data exchange and collaboration.

Source

Intelligent analysis of equipment energy consumption and carbon savings using IoT sensors and big data processing to accurately monitor, analyze, and optimize energy usage for industries and equipment. The method involves deploying IoT sensors on front-end equipment to collect real-time data, processing it through a big data platform to summarize and analyze the energy consumption, and pushing the results to terminals for monitoring and management. This allows precise determination of energy usage by industry, region, equipment, etc. to help enterprises optimize energy use, detect anomalies, and achieve energy conservation and emission reduction goals.

Source

Cloud-based IoT platform for remote monitoring and control of industrial and agricultural environments. The system integrates environmental sensors, predictive analytics, and cloud-based processing to enable real-time monitoring and predictive maintenance across multiple parameters. It employs a distributed sensor network architecture with wireless communication, advanced analytics, and cloud-based processing to provide continuous monitoring and predictive capabilities. The system enables remote monitoring, automated anomaly detection, and historical data analysis through its user-friendly interface.

Source

Energy-saving optimization for HVAC systems through a carbon reduction model that leverages IoT data collection and machine learning. The system employs an intelligent body that monitors real-time HVAC operation, predicts energy consumption, and continuously updates models based on cluster analysis. The optimized models are trained using hyperparameter optimization techniques and then applied to real-time system operation. This approach enables continuous model adaptation and improves accuracy over time, enabling real-time carbon reduction optimization.

Source

Device for remotely monitoring energy consumption of HVAC systems like air conditioners and heating systems. The device consists of the HVAC equipment itself, with a connected capillary network. By integrating monitoring capabilities into the HVAC components themselves, it allows remote tracking of energy usage without needing separate devices or sensors.

Source

Intelligent building air conditioning energy saving system using edge computing to optimize HVAC performance and reduce energy consumption. The system has edge nodes near building equipment, like air conditioners, that collect real-time operating data and local environment conditions. Edge computing terminals in the building analyze the data and generate optimal strategies. The terminals send control signals to the equipment. This distributed edge computing approach improves automation, adaptability, and speed compared to centralized control. It leverages edge nodes close to equipment for local data collection, analysis, and control.

Source

Cloud-based real-time monitoring and optimization of HVAC systems to reduce energy consumption, carbon footprint, and maintenance costs. The system uses sensors in the HVAC equipment to measure parameters like pressures, temperatures, and refrigerant charge levels. This data is sent to a cloud platform where it is analyzed to monitor system health and performance. The cloud platform can also control the HVAC components autonomously based on the sensor data. This allows proactive maintenance and optimization of the HVAC system to improve efficiency and reduce failures. The cloud platform provides real-time cost monitoring, daily performance reports, and insights into energy savings potential.

Source

A system for real-time environmental monitoring and automated device control through cloud-based data management. The system integrates sensors, devices, and cloud infrastructure to monitor environmental conditions, while enabling remote device configuration, automatic updates, and real-time notifications. The system employs local storage for device configurations and provides cloud-based management through a user-friendly interface. This enables scalable device management across multiple locations, while ensuring device control through automated configuration and real-time monitoring.

Source

Intelligent environmental control terminal device and its control method that enables automatic temperature and brightness regulation through real-time monitoring of people and ambient conditions. The device employs advanced sensor systems, including human body pyroelectric detection, light intensity sensors, and traffic statistics monitoring, to dynamically adjust temperature and brightness levels. This integrated approach enables the device to automatically adjust parameters based on occupancy patterns, ambient temperature, and lighting conditions, thereby optimizing energy efficiency and occupant comfort.

Source

Cloud-based HVAC system that leverages AI-driven predictive optimization to achieve energy efficiency through intelligent scheduling and control. The system integrates data acquisition nodes, control nodes, and a cloud-based monitoring platform to manage building operations. The control nodes process building data and HVAC operation information, while the data acquisition nodes collect real-time building and HVAC data. The platform employs machine learning algorithms to predict building energy consumption, optimize HVAC operation, and balance energy distribution. This enables real-time energy management and predictive maintenance through AI-driven scheduling and control.

Source

A distributed computing system for HVAC monitoring and control that optimizes building operations through automated data collection and analysis. The system enables real-time monitoring of HVAC systems through client terminals, which are assigned virtual machines that execute container processes. These container processes collect and analyze data from relevant Building Automation System (BAS) servers, while the client terminals operate independently. The system dynamically allocates computing resources between client terminals and virtual machines based on client activity, ensuring efficient use of hardware resources.

Source

Cloud-based collaborative central air conditioning system that optimizes energy efficiency by leveraging cloud analysis and control. The system involves monitoring operating data and energy consumption of air conditioning equipment, uploading it to a cloud analysis device, and receiving real-time optimized strategies back. This allows coordinated control of the entire central air conditioning system to balance energy savings with comfort. The cloud device analyzes overall system data and provides optimized strategies based on principles of optimal efficiency and terminal comfort.

Source

Building management system (BMS) that enables remote control of HVAC systems through cloud-based environmental monitoring. The system integrates with existing HVAC infrastructure to collect real-time data on temperature, humidity, and other environmental parameters. Using machine learning algorithms, it analyzes this data to provide personalized control recommendations to building occupants and automated system operation. The system enables remote monitoring and control through a user-friendly interface, eliminating the need for local server devices.

Source

Cloud-side end collaborative central air conditioning global optimization energy-saving control method and system that enables real-time cloud-based optimization of central air conditioning systems through distributed computing architecture. The system comprises a cloud server, edge nodes, detection devices, and end devices. The cloud server processes complex optimization parameters, while edge nodes and end devices perform local calculations and control operations. This distributed approach optimizes energy savings by leveraging cloud computing while maintaining local control capabilities, enabling efficient and stable operation of central air conditioning systems.

Source

Remote monitoring system for automated air conditioning equipment through the Internet of Things (IoT) that enables remote control and monitoring of air conditioning systems. The system comprises cloud-based modules that collect and transmit equipment parameters to a central server, while a remote terminal connects to the server for real-time monitoring and control. A field monitoring module at the site of the equipment provides environmental data, enabling remote intervention through the on-site terminal. This integrated approach streamlines maintenance operations by eliminating the need for on-site personnel while maintaining operational reliability.

Source

A remote monitoring platform for automated air conditioning equipment that enables real-time monitoring and control through a network of field terminals connected to the equipment. The platform integrates environmental sensors and IoT modules with a cloud-based server, enabling remote monitoring and control through a user-friendly interface. The system also incorporates voice communication capabilities to provide immediate assistance to on-site operators when system alarms occur.

Source