The minds behind the algorithms that contribute to energy efficiency
Mitra and Raghunath are both data scientists with a focus on machine learning and data analysis. They started at Egain about a year ago and have since then worked on several projects connected to Data Science and Machine Learning, such as the projects Peak Control 2.0, Performance of AI-steering, creating a Data Science Platform, and Predictive Maintenance. One valuable result is for example a new control algorithm that makes it possible to see how energy consumption varies at different temperatures in real-time.
With a new data science platform, property owners can see the difference in the building’s performance and consumption if they use Egain’s products or not, based on the current year’s data. Previously, analysis and comparison were made of energy consumption based on reference year data.
– With the new algorithms, our energy experts can show customers directly what savings they can achieve by using our services, says Raghunath.
Customer insights
Mitra and Raghunath have created a Data Science platform to collect relevant data based on customers’ needs for information and visual reports. This data will also be used to create machine learning algorithms to adjust the indoor climate and temperatures automatically based on how the weather varies. The project is called Peak Control and is a method that can calculate how much customers save in money by letting Egain control the heating of a building and manage power peaks.
– Since January, we have started collecting more weather-related data points to support our Machine Learning-models to improve our control algorithm so that we help customers identify energy peaks on a large scale and proactively warn energy companies days in advance, says Raghunath.
Visual and interactive reports
Mitra says that she has conducted interviews to find out what data customers want to see, are interested in and why this information is important to them. The data generated will be used to create new Machine Learning algorithms which makes it possible to create better, intuitive, and visual Business Intelligence reports that also can be tailored for each customer.
– The reports will provide a quick and visual overview and make it possible to click and filter different data from the graphs in an interactive way. This will make it easier for the user to recommend different actions. And also make it easier for the customers to make the right decisions to reduce environmental impact, energy consumption, and costs, Mitra says.
Increase customer understanding of important data
In addition to the above Mitra and Raghunath also have started a project on Predictive maintenance where data is used to analyze the heating central performance. Through advanced analysis and by understanding the amount of heat that enters and leaves the heating system, it is possible to detect leaks or other things that deviate in relation to how it usually is. This makes it possible to implement preventive measures such as correctly adjust the system and to understand the efficiency and performance of your heating central in order to streamline your energy usage and doing long-term maintenance planning.
– We need to help customers understand the value of collecting correct data from their building portfolio and what type of data is valuable to be able to optimize consumption. That is when it becomes a win-win-win from an environmental, customer, and cost perspective, Mitra concludes.
