IBMer and MIT TR35 honoree is making electricity accessible and available
Tanuja Ganu grew up in a small town in India about 400 kilometers south of Mumbai, where – like much of the country – energy outages happen all the time.
“The voltage was often so low that the lights were dim and the refrigerator would burn out.
“I studied for exams by candlelight, and endured summers without working fans. To deal with this as children, we learned to time-shift critical things we needed electricity for – like cooking and cleaning,” Tanuja said.
Now an engineer at IBM Research, MIT recognized her as a “2014 Innovator Under 35” for building solutions that begin to solve these challenges. Her collaboration with the University of Brunei Darussalam led to the inventions of SocketWatch, nPlug, and iPlug.
Q&A With Tanuja Ganu: Experience to expertise
How did the experience of dealing with electrical outages influence your decision to work in this field?
Knowing the inconvenience of time-shifting, I was particularly fascinated with the idea of democratizing the Demand Side Management (DSM) of energy. It’s something that average citizens can make a difference doing by simply reducing their consumption during peak hours and avoiding other energy wastage (like leaving the TV and other appliances on standby).
But you studied computer science and machine learning at university. How did you connect that expertise with energy and utilities – and eventually your solutions of nPlug, SocketWatch and iPlug?
I first learned practical engineering from my father (also an engineer) when we had to fix appliances at home. These projects got me interested in engineering and particularly influenced my thinking about inventing and applying knowledge to solve real-world problems.
Though, I graduated with a degree in computer science, and completed graduate studies in data mining and machine learning, I looked for domains where I could address real societal problems using data insights and technological change. And during campus interviews, I came to know about the Smarter Energy group at IBM Research-India. It was the perfect combination of computer science and electrical engineering techniques specifically addressing energy issues. After an internship with them, I joined as an engineer in 2011.
My first project was nPlug, or “Smarter Planet in a Plug.” It is aimed at alleviating peak usage loads through inexpensive autonomous DSM. Working with a team of engineers with backgrounds in embedded systems and power optimization, we developed a device that fits between appliances such as hot water heaters and even electric vehicles, and wall sockets. nPlug senses line voltage and line frequency (how much energy the device uses and how often), and then uses machine learning techniques to infer peak periods as well as supply-demand imbalance conditions. It then schedules usage for the attached appliances in a decentralized manner to alleviate peaks whenever possible – without violating the requirements of consumers.
SocketWatch is another device that fits between an appliance and the wall socket. It autonomously monitors the appliance’s usage – and based on the appliance’s power consumption, SocketWatch alerts the end consumer of the device’s proper usage (preventing energy waste). For example, it can switch off a TV if it is on standby mode, or alert the consumer about the energy “leaking” from a refrigerator due to a malfunction, like a leaking gasket.
Our most-recent project, iPlug, will help distributed energy sources such as a home’s rooftop solar panels. It – like our other devices – autonomously decides how to route electricity from solar panels back to the grid (on the most loaded phase during peak times), or to store or use the energy locally, based on the home’s usage needs.
How do machine learning, data mining and analytics play a role in these energy projects?
Thanks to advances in embedded systems and sensor technologies, a lot of high frequency data related to energy parameters, such as line voltage, frequency, active power, and reactive power is available for analysis – like finding irregularities in the operations of energy systems. My skills in machine learning and data mining help analyze and bring insight from the data by writing learning pattern algorithms.
Once the patterns are analyzed, optimization skills help in coming up with optimal strategies to solve specific issues at hand. For example, in the case of nPlugs, we apply machine learning techniques to line voltage and frequency data to understand the times of peak demand and supply-demand mismatches. Then we apply optimization techniques to determine preferred times to schedule an appliance in a decentralized manner such that they follow user-defined deadlines, but do not over-load the grid.
Though we have not evaluated these devices in large scale pilots yet,we have evaluated prototypes of nPlugs and SocketWatches in real-life settings.
We’re able to show that nPlugs correctly defer loads such as storage water heaters to off-peak hours without inconveniencing their owners. We have also studied the collective behaviors of thousands of nPlugs using simulations. They are able to reduce peak loads by up to 45 percent with a realistic mix of deferrable loads.
And we can show that SocketWatches are able to accurately pinpoint malfunctions in appliances, such as air conditioners (blocked air filters and obstructed fans) and refrigerators (gasket leakage).
How do you envision these devices being used in the future?
I think there are multiple ways these devices could roll out to consumers and the industry. Utility companies can subsidize nPlugs for high consuming deferrable loads, like electric vehicle charging, to alleviate peak demand.
In the case of SocketWatch, since it provides alerts for reducing electricity waste, helps in preventive maintenance, and lowers a home’s electric bill, it could be directly commercialized to end consumers. And we could also partner with appliance manufacturers since these devices could be integrated within an appliance.
Read more about Tanuja and her work in MIT Technology Review’s 2014 Innovators Under 35.