In order to meet renewable energy penetration goals without compromising the reliability of the electricity grid, energy storage is a must.
Batteries are one of the most promising technologies for the job. However, in spite of the recent advances in various battery technologies, it is still quite difficult to build economical battery projects. The industry so far has been mainly focused on reducing the cost of the hardware. For example, in the past decade, the cost of lithium-ion batteries has dropped by more than 73%. Reducing the hardware cost, however, is only one aspect of the problem. There are more, and often conflicting, components that go into the equation.
Traditional solutions for dispatching battery projects are typically reactive, sub-optimal, and ignorant of battery life. They either over-cycle the battery to increase the value generated, which compromises the life of the battery, or over-size and under-utilize the battery to preserve its life, which reduces the overall value generated from the project.
WattLearn's technology is the best in class to balance between three pillars of battery economy|: the overall life of the project, the cost associated with the project, and the total value generated from the project.
Using AI, optimization, and cloud-based computing concepts, our PLAAS technology offers proactive and optimal solutions for often-conflicting objectives derived by energy systems constraints, energy market dynamics, economic & operational objectives, and even uncertainties rooted in human behavior.
PLAAS is here to maximize the value of your battery project. PLAAS serves as an intelligence layer on top of the existing battery control system. It sends setpoints to your battery’s management system proactively to maximize a customized set of objectives for your very specific project (including revenue, savings, number of cycles, lifetime, and carbon footprint). Our system is nonintrusive, technology agnostic, and cloud-based.
If you conclude that PLAAS is the right fit for your project, but still prefer to have direct control of your battery’s operation, then this is the solution for you. You receive a series of recommended dispatch setpoints, updated in real time. You can either incorporate these setpoints into your battery control system, or use to benchmark your existing strategies.
If you wonder what our PLAAS solution can do and how it can make your battery project more efficient, this is a service you would benefit from. We will work closely with you to customize our PLAAS solution to your project’s specifications and needs. We will simulate the past operation of your project with PLAAS in different configurations to demonstrate the value that could have been generated for your project.
WattLearn is a startup from Pittsburgh, begun at Carnegie Mellon University (CMU). The founders, Julian Lamy and Matineh Eybpoosh, have decades of combined experience in tackling some of the most challenging problems in the energy space. In 2016, through his studies of energy storage markets, Julian realizes that one very important factor is missing for batteries:: intelligence. Current batteries are simply too dumb to be profitable. Combining their multidisciplinary background, Julian and Matineh designed machine-learning based software to solve this problem and thereby change the future of our energy landscape.
Since then, Matineh has been leading WattLearn's business and technology development, taking the company from an idea to its current state.
WattLearn came out of the AlphaLab accelerator and Plug & Play innovation platform. We have made significant progress in a very short time, and along the way, we have been recognized as one of the front-runners in this domain. WattLearn won first place at the TransTech Energy Business Development Competition at Carnegie Mellon University, was invited to present at the Energy Storage Summit 2017 held by Green Tech Media, won the People’s Choice Award as the best energy startup at Plug & Play, and was a finalist startup presenter at the VERGE 18 conference, just to name a few accolades.
In November 2018, WattLearn was acquired by one of the country’s leading energy investment companies. This partnership has offered WattLearn the unique opportunity to implement its PLAAS solution on some of the country’s largest front of the meter battery projects. We are very excited to have the chance to have an impactful contribution in building a sustainable grid."
Matineh has years of experience and demonstrated leadership in building a variety of machine-learning software solutions for monitoring and controlling energy systems, as well as optimal energy market participation. Her passion is to improve the reliability, efficiency, and accessibility of clean energy across the world. Before WattLearn, she was the Director of Data Science at kWantera, an energy management company where she led the development of the company’s AI platform for energy market intelligence. She completed her Ph.D. at Carnegie Mellon University, where she developed machine-learning algorithms for proactive diagnosis of energy systems.
Lead Data Scientist
Puneeth is a clean-energy professional with demonstrated experience using software and data analysis in the service of reducing carbon emissions. Prior to WattLearn, Puneeth led the data science team at Stem, an early behind the meter energy storage provider. Before that, he was at Plotwatt, an energy efficiency analytics company. Before entering industry, Puneeth was a postdoctoral researcher at the Lawrence Berkeley National Laboratory where he analyzed the impact of global appliance efficiency standards. Puneeth completed his Ph.D. in Experimental High Energy Physics at UC Santa Barbara
Senior Software Engineer
Bo's background is in high energy physics working on recreating moments after the big bang using experiments at CERN. Over the years, Bo has been developing production level analyses which include linear programming for non-profits on behalf of endagnered fish, power grid emulators using transfer functions and AI for hybrid power plants. At WattLearn he is leading the development of cloud-based software infrastructure.
As a Software Engineer, Cristian will help WattLearn scale its data infrastructure to meet the demand of an emerging energy storage market. He completed his Ph.D. in Electrical Engineering (with a concentration in Solar Energy) at UC Davis, where he was also a Business Development Fellow. He patented his Ph.D. research and later founded a company based on the work, Az-tec Power. Professionally as a Data Scientist, he has used global campaign data to drive business development; as a software engineer, he specialized in deploying asynchronous REST APIs into production. Cristian strives to work at the intersection of technology and social responsibility.