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How we collect energy data


AMR systems automatically collect data from energy meters and transmit it to a central server. This method reduces the need for manual meter reading and can provide more accurate and timely data.


AMI systems use two-way communication between the energy meter and the central server. This allows for real-time data collection and can provide more detailed information about energy usage patterns.


BAS systems can be used to monitor and control energy usage in buildings. These systems can collect data from various sources, including energy meters, HVAC systems, lighting systems, and other building systems.


IoT devices can be used to collect energy data from various sources, including energy meters, sensors, and other devices. These devices can be connected to a central server, which can collect and analyze the data.


1.Do I need a Building Management System (BMS) to use Retragreen AI Solution?

No. In cases where there is no BMS, we will collect the data directly from your utilities. Often, Retragreen IoT Device is used as the primary BMS, as many organisations don’t want two systems that both collect energy data.If you already have a BMS with sub-meters and data infrastructure, we can leverage and integrate directly with your BMS.

2. How much does it cost?

In contrast to traditional approaches, we don’t charge an upfront installation or integration fee.We will be happy to let you try our Retragreen AI solution on your buildings and show you the expected value before you decide.

3. What savings can a building owner expect if it chooses Retragreen AI solution?

In the deployment of our solution, our technology has achieved a reduction of HVAC energy costs of up to 25% and lowered HVAC-related carbon emissions by up to 40%. In addition, it has also increased Net Operating Income (NOI) and asset/equity value due to reductions in the HVAC spend.

4. How does Retragreen AI aid in energy savings?

The data collected from IoT sensors and Building Management Systems (BMS) serve as the data points. These points are continuously fed into a machine learning model, which acts as the system's brain. Over time, this model diligently analyses and optimises the data, employing a variety of machine-learning algorithms to propose enhancements. If further optimization is required, we can augment the system with additional IoT sensors to capture more data points, thereby enhancing energy efficiency.At the core, what our technology does with a building’s HVAC system is manage it with deep learning AI tech, reproducing what we’re doing as humans but in a more efficient and more forward-looking way.

5. Can I still use my trusted energy or building manager?

Yes! Our solution is designed to combine the strengths of AI with the experience and skills of human professionals.The goal is to take away the tedious task of looking at thousands of data points every hour and allow them to focus on the important aspects of their work – to reduce your energy and carbon footprint.

6. When do the savings take effect?

Generally, our solution generates savings within three months of deployment.

7. Will the facility manager be able to see the energy consumption data in real-time?

Yes, they can visualise from our BI dashboard of how the energy consumption of each equipment in the building or location and ensure that everything is working at an optimal level. This overview also allows them to react and address major issues. Demonstrating another key value that our solution brings in addition to energy cost savings.

8. If I have solar systems, how does Retragreen AI solution help?

Retragreen AI solution enhances energy efficiency of the HVAC and reduces energy bills.Building owners with Net metering policy allows them to export excess electricity back to the grid. This excess energy is credited to the consumer’s electricity bill, effectively reducing energy costs.

The equipment required for AI energy control in the cold room industry can vary depending on the specific implementation and needs of the business. However, all systems require robust sensors, data processing equipment, machine learning software, control systems, communication equipment, and human interface equipment to effectively manage energy consumption and optimize efficiency.
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