Cirrus AI is an innovative cloud-based platform that uses artificial intelligence to optimize the energy performance of HVAC and refrigeration systems. Its main objective is to reduce energy consumption, maximize efficiency and minimize the environmental impact of these systems.
Main features of Cirrus AI
Real-time monitoring and analysis
Cirrus AI collects real-time data from HVAC and refrigeration systems, such as temperature, humidity, pressure and energy consumption. It uses artificial intelligence algorithms to analyze this data and generate relevant information about the energy performance of the systems.
Automatic optimization
Based on real-time data analysis, Cirrus AI uses optimization algorithms to automatically adjust the parameters of HVAC and refrigeration systems. This includes setting setpoint temperatures, operating schedules, operating modes and other related parameters to maximize energy efficiency.
Machine Learning
Cirrus AI uses machine learning techniques to adapt and continuously improve its performance. As it collects more data and gets feedback on the results of optimizations, the system can adjust and refine its models and algorithms to achieve better results.
Intuitive and easy-to-use interface
The Cirrus AI platform features an intuitive interface that allows users to monitor HVAC and refrigeration systems easily. Users can access real-time data, view reports and performance graphs, and make manual adjustments if necessary.
Integration with existing systems
Cirrus AI can be integrated with existing HVAC and refrigeration systems, allowing it to be deployed in a wide variety of environments. The platform supports different communication protocols and can collect data from multiple sources for comprehensive energy performance analysis.
Energy efficiency and sustainability
The main advantage of Cirrus AI is its ability to improve the energy efficiency of HVAC and refrigeration systems. By optimizing the performance of these systems, energy consumption is reduced, which in turn contributes to sustainability and helps minimize environmental impact.
How do we do energy optimization in air conditioning and refrigeration?
As a general approach, to perform the energy optimization of an industrial refrigeration or HVAC plant using artificial intelligence it is necessary to perform the following steps:
Data collection
The first step is to collect relevant data from the industrial refrigeration plant, such as energy consumption data, operating variables (temperatures, flow rates, pressures, etc.), climatic data and any other relevant information. These data will be used both for the initial training of the model and for its subsequent real-time operation.
Data preparation
Once the data has been collected, data cleaning and preprocessing must be performed to ensure that the data is consistent and in a format suitable for use in the AI model, normalized and free of outliers.
AI model development
Next, an AI model is developed using machine learning algorithms or neural networks. Articae has developed its proprietary model, which while not general purpose, can be easily particularized for any air conditioning or refrigeration plant.
Model training
Once the model has been developed, it is trained using the collected data. During training, the model will learn from the historical data how to optimize the parameters of the industrial refrigeration plant to minimize energy consumption. This involves optimizing the model parameters. For this purpose, Articae has developed its own optimization techniques and methods that can be easily adjusted to any plant.
Model validation and tuning
After training, it is important to validate the model using unseen data to assess its performance and ensure that it generalizes correctly. If necessary, additional adjustments are made to the model to improve its accuracy and performance.
Monitoring and feedback
It is important to continuously monitor the performance of the AI-based control system and collect feedback from the industrial refrigeration plant. This allows the effectiveness of the model in reducing energy consumption to be evaluated and further adjustments to be made if necessary.