Decentralization of energy supply and deregulation of energy markets, coinciding with exponential increases in the data available from energy users, have created a pressing need for software to understand energy consumers and manage energy resources. Underlying each of those requirements, are challenges that cannot be met by the analytical approaches that have been employed in the past. Predictive Analytics that forecast behavior and faults of individual meters and connected devices meet these challenges with a product suite built on a core set of patented Machine Learning algorithms.

"Grid4C’s edge lies in the ability to squeeze the greatest value from existing, ubiquitous data sources, non-intrusively, without needing to wait for new sensors to reach mass adoption"

"Machine Learning provides a window into homes and businesses using smart meter data that facilitate granular, accurate predictions, enabling more reliable operation of the grid and integration of renewable resources and energy storage,’’ explains Dr. Noa Ruschin- Rimini, Founder and CEO, Grid4C. Over the years, the company has been able to solve some of the industry’s greatest challenges, outperforming the competition in industry benchmarks. Having launched a partnership in APAC with smart grid and smart metering leader Landis+Gyr, Grid4C's software today is deployed for more than one million homes and businesses and analyzes billions of meter reads from four different continents, producing more than 500 million predictions daily in less than an hour.

Energy Providers Adopt Cloud Computing

One of the big changes in the landscape is that utilities recognize that cloud computing is not only an option, but a necessity if they want to take advantage of machine learning and advanced analytics.

Grid4C’s solutions help this market on three fronts: customer-facing applications that help businesses and consumers not only save money but predict problems with the appliances they rely on, predictive customer analytics that facilitate segmentation and micro-targeting and predictive operational analytics that optimize procurement, grid operations and the integration of solar, energy storage and electric vehicles. "Customer demands are just starting to catch up with the capabilities that we can provide," says Dr. Ruschin- Rimini. A solution that is generating a lot of excitement in the market is Fault Prediction, Detection and Diagnostics (FDD) based on smart meter data, which can be enriched with data from smart appliances such as connected thermostats, enabling the same algorithms to deliver deeper diagnostics and insights.

Grid4C draws on smart grid data, weather data, customer data and more to accurately predict demand across the grid at the meter level/sub-meter levels at sub-hour intervals for both short and long terms

As an example, ‘‘with smart meter data, we can not only detect mechanical problems and wasteful settings but can predict problems before they happen,’’ explains Dr. Ruschin-Rimini. This can prevent slight inefficiencies from transcending into larger problems of much more severe consequences. By utilizing a highly modular architecture, Grid4C can easily implement and customize the core analysis and insights derived in the process. ‘‘The core of our products is our proprietary AI self-learning engine, so all you need to do is ‘throw’ any data that may be relevant into it,’’ claims Dr. Ruschin-Rimini.

Benefits of a Machine Learning System

Machine-Learning holds the key for generating new revenue streams, optimizing operational processes, for better balancing the grid, reducing spikes during peak hours, optimizing Demand Response and pricing programs, identifying appliances that are about to break, pinpoint costly behaviors that are easily fixed, and essentially understand homes, businesses and their occupants.

"By monitoring each meter separately and analyzing data like meter and device reads, weather data, customer data and more, Grid4C's engine automatically learns the underlying correlations and hidden patterns and generates predictions in a plug-and-play manner," explains Dr. Ruschin-Rimini. The plug-and-play approach allows predictions to be generated very quickly, which means customers have the added benefit of short time-to-value.

By using information theory-based algorithms, the machine learning engine can decompose the behavior of each meter into sub-series and, combining the analysis of each meter’s data with customer data, develop a deep and detailed understanding of customers’ homes and businesses from both physical and behavioral perspectives.

Behind The Machine

Grid4C’s success stems from its strength in Machine Learning and data science.
With more than 10 years of experience in leading software and startup companies, the Grid4C CEO holds a Ph.D. in Machine Learning and Artificial Intelligence from the Engineering Faculty at Tel Aviv University, specializing in anomaly detection and predictive analytics of big data. Always on a quest to find new challenges and make the world a better place, she is supported by a team that shares her passion and is laser-focused on advancing the current state of the art to bring real value to the Energy industry.

The company’s Research and Development center is a place of continuous collaboration between some of the foremost Machine Learning PhDs and software engineers dedicated to providing a seamless experience to the end customer. ‘‘When we do our job well, our customers don’t need to master anything because they receive predictions and insights—actionable intelligence—not algorithms,’’ affirms Dr. Ruschin-Rimini. Grid4C is always ready to take on the industry’s toughest challenges to ensure they are truly making a difference.

The Next Big Step for Grid4C

With the increasing competition among energy retailers and utilities, Grid4C is well-positioned to meet their need to differentiate and to find new revenue streams to overcome thin commodity margins. Recognized by Greentech Media Research as the #1 Predictive Analytics solution for Utilities in 2016, the company is setting its sights on solving some of the thorniest challenges that the industry faces. By leveraging its advanced machine learning capabilities, the company is developing deeper insights and earlier predictions of the issues and events that customers care about. As the number of IoT devices increases in connected homes and businesses, Grid4C is adding analytics that extract value from the data they provide. There is especially strong interest in solutions for the underserved Small and Medium Business (SMB) segment and the company is leveraging its roots serving the Commercial market with advances in residential IoT to develop a solution for this market’s unique challenges.

Decentralization of the electric grid and electrification of transportation is another focus for the company. "All the systems that exist and are being developed to manage these complex systems share a requirement for granular, accurate forecasts of energy consumption and production," explains Dr. Ruschin-Rimini.

Grid4C’s edge lies in the ability ‘‘to squeeze the greatest value from existing, ubiquitous data sources, non-intrusively, without needing to wait for new sensors to reach mass adoption.’’ At the same time, Grid4C will continue to leverage advanced machine learning capabilities to drive value from the exponential growth in IoT data.