To maximize the efficiency of energy operation, AI automatically controls energy and continuously collects and analyzes the data of operational mode for continuous optimization and adjustment via machine learning to ensure no deviation from normal electricity usage. ElectriSwift AI-based energy saving system for smart buildings empowers companies to overcome energy saving bottlenecks to fulfill the goal of minimizing energy consumption and environmental sustainability.
The exclusive AI model collects the data, predicts and automatically controls the equipment to minimize power consumption while maintaining the same level of environmental comfort.
Introduce an AI-powered automatic energy saving system according to the building type to save the expenses of replacing AC units and equipment.
By monitoring equipment status 24/7, this AI-based system builds an alert system of anomalous power consumption for quick troubleshooting as well as prevention of energy waste.
The visualized middle platform allows the user to understand the power consumption and energy flow at a glance, as well as helps companies find the equipment consuming the most energy and make the most appropriate business decisions.
Show the data such as power consumption, temperature, humidity and CO2 concentrations by day/week/month. The user can export the report when needed.
When the power consumption exceeds the contracted capacity, the system will send alerts via emails or LINE messages to help companies understand the trend of power consumption accurately.
Integrate the power generation data of a self-owned site and analyze the trend of power consumption to accurately calculate the most suitable green energy wheeling capacity model.
Enter the power consumption data of multiple buildings and develop the best green power supply strategy through data analysis and AI-based modeling.
Automatically update Taipower electricity rates regularly. Adjust the contracted capacity via the optimized electricity costs calculation model for cost savings
The original system recorded over 40 million CDR call records per day. A traditional RDB database was not capable of handling data management and aggregation efficiently and required excessive time and manpower to monitor huge databases. The customer also had to pay a high license fee of RDB(Oracle) annually. By adopting MongoDB Cluster system, the efficiency of data handling was upgraded by 5 times. digiMars automatic real-time monitoring also improved management efficiency.
The scope of factory energy management covers air-conditioning, power, lighting, heater, air compressor, etc. Among these, air-conditioning units account for the highest energy consumption. AI-powered smart energy management system allows the user to monitor remotely, check equipment operational status in real time and run automatic energy saving controls based on the outdoor temperature. The system automatically enters energy saving mode during non-operational hours to avoid potential energy waste and achieve power optimization in 24-hour operating factories.
By connecting the outdoor air regulator to the independent air-conditioning unit inside building, the visitor count data is accurately analyzed by the smart energy saving system for traffic load prediction. The system then responds quickly based on the data collected in real time to adjust air-conditioning settings to accommodate mall traffic changes and outdoor weather and ensures efficient equipment operation and a pleasant shopping experience for all visitors.
Managed carbon data service and solution
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