 Morning folks, thanks to the yoga instructor. Is it too loud? Can we adjust the volume a bit? Thanks to the yoga instructor for giving us a great start. I really enjoyed it. So today I'm going to be talking about bits and jewels, data-driven energy systems. So like when I was young, right, this is how the family entertainment used to be. Like, you know, there will be a black and white TV. You just go sit in the hall, watch a TV program together. And there used to be a thing called Chitrahaar or something. I forget the exact name. There used to be movie songs playing there from 8 to 8.30 p.m. So we will wait for it. Thinking about in the school that we are going to watch it, etc. Right? So now when you see the media has completely changed, there are so many options, your cell phones, YouTube, iPad, Facebook, what not, right? So what has happened is the media has come, you know, moved from a centralized paradigm, you know, somebody generating content and sending it to people who passively consume to more like Facebook, anybody can become a blogger, you know, vlogger, whatever, right? Does anyone know who this one is? This lady is? Her name is Lily Singh. She is from Canada and she's a YouTube star. She made like more than $7.5 million in producing content for YouTube and she has, you know, millions of subscribers on the channel, right? Think about the days where, you know, there used to be a Doodharshan and everything central to now anyone can be a content producer. The same thing is happening in the power grid, you know, from the centralized electricity generation to completely decentralized and more democratized. So I'm going to talk about how IoT and data technologies are enabling such kind of a transition, right? When you look at a conventional power grid, you know, so it's somewhere, you know, far away the energy gets generated and, you know, and then the energy flows through the transmission network, comes to the distribution network, and people passively consuming their homes or in their office buildings. So it's a centralized energy generation and, you know, supply always follows the demand. You know, the conference says it has geeks, so you must have all seen like, if you wrote your first C program, you will have a static array of 1000 elements. You know, your program will never take more than 1000 elements. It's fixed and if you have more things, you know, you put more items into the array. Whereas now the energy grid has become like malloc, you know, as the consumer determines how much of energy they want and how do they work with the generators to adjust their demand so the peak does not exceed too much, right? And finally, you know, there is no involvement of consumers in the current or the older form of grids. In the newer grids, they are called the prosimers. They can generate energy, put it into the grid. They can reduce their energy consumption, shift their energy consumption and so on and so forth, right? So why such a paradigm shift is required? It's because of three reasons. First, when you see there is so much of energy shortage, currently more than 1 billion people across the world don't have access to electric grid. Even in India, about 200 million people don't have access to electric grid, right? And second thing is when you look at the power consumption pattern, this is from New York, so on a daily basis, it can fluctuate anywhere between 5000 megawatt to 30,000 megawatt. So the consumption variations is too much, right? So that is a big problem in the grid because they have to generate and the consumption has to happen instantaneously, right? So you have to have this kind of capacity to handle all the fluctuations. And finally, the environmental concerns. More than 70% of the greenhouse gas emissions from the world come from energy generation and energy consumption activities. So given these three important reasons, people are talking about decentralized grids. This is already happening, you know? So there are four main aspects of the decentralized grid. One is energy efficiency, you know? How do you actually use anything from LED lights, more energy efficient refrigerators and air conditioners? How do you save your energy? In fact, you must have heard that even in the data centers, they have something called PUE, power utilization efficiency. Basically, the amount of energy that goes into computing versus the amount of energy that goes into cooling. They want to reduce the amount of energy that goes into cooling. I've been to a data center in Sweden. Facebook has set up a data center in Sweden, in the northern part of Sweden, because it's so close to Arctic and the wind is so cold, so you don't need to have any separate cooling or air conditioner there. So they save energy on that. Google pays, like, several tens of millions of dollars in just-in-electricity bills, right? So then the load shifting. I mean, this is something that we do in Bangalore every day, right? We want to get away from the traffic. Zainab yesterday told me, you know, don't come to this area after 8 o'clock. The traffic will be so bad, you know, come during the off-peak hour, you know, cross the KR Purim bridge before 7.30. That's what is the load shifting. Move away from the peak hours. And then more distributed generation. You must have seen, even in Bangalore, there are a lot of places that are rooftop solar, solar water heaters, et cetera. And finally, energy injection. You know, you can generate energy and put it into the grid. This is what I was kind of comparing with Facebook and Twitter. Anybody can become a generator of energy, like how anybody can become a content creator now. So I'm going to talk about two things, right? One is about the distributed generation, and secondly, it's about load shifting, right? This topic is so huge and so vast, and, you know, it'll take hours and days to complete it. So I'll just give two examples of how data-driven systems are enabling this thing, right? The first one I will talk about is the solar plants. Solar is a very beautiful energy source. First of all, it is clean. It doesn't have any moving parts. Since we are all software people, it's extremely modular and linearly scalable. You can put a 1-watt or, you know, 0.5-watt solar panel. You must have seen solar-powered calculators to even, like, you know, 50 megawatt, 100 megawatt of solar plants, right? In fact, in PowerGuarda, which is about four hours' drive from here, government is setting up like 2 gigawatt of solar plants, right? So they're extremely scalable. It's quiet. When you look at wind turbines, they make a lot of noise, disturb the birds, et cetera, whereas solar is quiet, solar is scalable. And the thing is what has happened when you go to places in China, the pollution is so bad, so much of fog, and people celebrate if they see a blue sky there, right? As if it is Diwali or Christmas they celebrate. Oh, today is clean. So the Chinese government put a mandate that, you know, we have to go towards solar. They gave a lot of subsidies to solar manufacturers, and the solar panels have become so cheap and so efficient. Now the Indian panel manufacturers are pushing the government to take protectionist measures. They are asking the government to put extra duty on the Chinese panel manufacturer. They don't want the panels to come in. I think that is pathetic. We have to improve the engineering quality instead of asking the government to put extra duty on the competitors so that you don't have competition, right? But what Chinese have done, it has done a great thing to the world. It reduces pollution and gives us more energy. So when you look at managing the solar plants, it's quite complicated because one megawatt of plant takes about five acres of land, okay? And usually they are in very remote places because that's where the land prices are low, and, you know, they usually take the fallow lands and it's extremely hot, right? There are no restrooms, nothing. I've been to these plants to set up our things. You have to schedule your restroom, is it? It's like four or five kilometers away. That's how bad it is, right? So it's very hard to find the site engineers to come and work there and, you know, to manage these plants. And there are a lot of things that can go wrong. Birds can come and, you know, drop on the panels. You know, your, you know, circuit breakers can burn because of the extreme heat. And, you know, so you can imagine whatever the problem that can be that can happen in this area. So what we said is how do you take data from these kind of environments and how do you help them manage their plants efficiently so you get maximum amount of energy from those plants? So this is our, I mean, I'm one of the co-founders of the company called Dataglin, which is focused on solar plant management or renewable energy management in general. This is the overall architecture, right? We go to a solar plant and we put a data logger, right? This is the point where the IoT technology starts. We collect data from the weather stations. We collect data from the energy meters and inverters. So inverters are the ones that take the solar energy, which is in DC form, converts into AC and puts it into the grid actually. When you look at the solar construction, what will happen is the solar panels will be connected in series like a string, so that is called a string, like a string of poles is a string of panels and those strings are connected in parallel to each other like an array. I mean, it's called an array of solar panels and that goes to a SMU and the repeater and then inverter and it goes into the grid. So we collect data from all these components when the strings from the inverters and all these things and send the data to our cloud over HTTP REST platform, sorry, HTTP REST protocol or MQTT and we also collect data from other services like weather forecasting services and, you know, do various analytics and provide these kind of interfaces like solar dashboards, we have smartphone apps, you know, do we do various analytics for improving the operation and maintenance efficiency and we give real-time alerts to site engineers so that they can take corrective actions. So this is our solar dashboard. I will not go into details of this, but it gives you a very quick overview. Like there are companies like Greenco, Wari, they have multiple plants, they want to see what is happening at their plants in different parts of India. They can come to this dashboard and see what is happening, how much energy is generating, how many places are down and so on and so forth. So I just want to talk about little bit about the various analysis that we do. So one is a spatiotemporal analysis. As I said, you know, the solar plants are huge and all the equipment are distributed over vast spaces. So we do spatiotemporal analysis. How is each string generating? How does it compare with the other strings? How does the performance compare with the other panels? Is there any substantial reduction in output? Is there variations in the output, et cetera? The analysis we found was one particular string was producing less at, you know, 3 o'clock in the evening and again around 9.30 in the morning, actually, every day consistently for the first six months of the year. So what we found out is that there is a handrail because it's a roof mounted solar plant. So there is a handrail to go up and clean the panels, et cetera, that handrail was casting a shadow in the morning on this side of the panels and then in the evening on the other side of the panels. So that was causing the heat and the panels were getting damaged. So using the spatiotemporal analysis, we could find out, you know, what is the problem? And we could not exactly say that because of the handrail, but we could identify what the problem was. The site engineer can go and inspect at a particular point. Otherwise, they would have never caught that this is happening until they see the data. So in also different root causes, as I said, in a solar plant, there are various factors that affect the performance of the plant. It could be the amount of sunlight that is falling there, the amount of humidity, whether modules are getting too hot, or, you know, the ambient heat is too much, so on and so forth. So we do, like, a correlation analysis to find out what are the failure. So when you look at it, you know, there is a lot of fluctuations in the demand, as I already told you, right? So one of the problems that happens is how do you maintain the equipment to handle the peak loads, right? So when you see this 1980 in New England, the top capacity, like, let's say if you have a six-lane highway in your freeway, the one lane gets used for, like, one hour of the year or, you know, two hours of the year, but you have to put enough money to construct such huge freeways. So this is getting worse and worse, you know, because the peak-to-average ratio is increasing like crazy, right? So that is what is happening in the 36 years. It has happened like this. So many of the network operators, especially in India, the utilities are already operating at loss. They are not able to maintain such infrastructure. And also the electricity gets traded in real time. If you can look up, you know, the India Energy Exchange where the producers and consumers trade energy. So the market prices also fluctuate like crazy and the wind speed, right? Also fluctuates and your generation fluctuates. All these factors means you need to have better peak load alleviation mechanisms. So when you look at a conventional power balancing thing, there are like plans that cater to the base load, right? Only like, you know, just like one lane highway or something like that. That is always available. They are very expensive to construct and very cheap to operate. So otherwise the peak loads, they are very cheap to construct and very expensive to operate. So that is what this curve is showing. If I can reduce some of the peak load, right? Peak demand to here, I can save this much of money for serving the peak load, right? So what have been the historic approaches? So this is one of the reasons why the daylight saving time came up. During the Indochina war, they said, you know, they are going to ship the daylight a little bit so you can use more of the sunlight. So 6 a.m. became 5 a.m. so that you burn less of electricity, right? So in Tokyo, they could not serve electricity. They started doing brownouts. Brownouts is instead of 230 volts, you give it the energy at a little less voltage like 220 volts or something like that. In California, they started doing rolling blackout during the Enron scandal. You know, they had so much of energy shortage. They would say this area, you know, the Hebal area is going to have a blackout for next two hours and so on and so forth. But this is very common to us. At least when I grew up in Tamil Nadu, we used to have 8 hours of blackout and 6 hours of blackout and you know, I've donated so much of blood to mosquitoes. I've donated blood, I mean, I've donated blood and saved wildlife so much, right? So how do you do this, right? There is something called the demand response. As I said, you know, this is something we do in Bangalore every day. You know, you try to avoid the peak traffic. Similarly, you try to avoid the peak load, right? So when you look at it, you know, like let's say this is how the demand is. What I do is if I can shift some load to before and some load to after, so the peak to average ratio can come down. That is what is the essential concept of demand response. So there are a lot of things that can be peak shifted, right? You know, I don't know why the cursor is moving separately here. Anyway, so there is a washer and dryer. You know, you don't have to run it at that time. If you have power storage, what can you do? Your rice cooker, your water heater is important. You know, every water heater consumes about 2 watts. Thank you. So, yeah, the water heater consumes about 2 kilowatts and everyone starts a water heater at the same time to go to work, etc. So that increases the peak load. When you look at the Bangalore, it's a bimodal power distribution for power consumption. The morning when people go to work and when they come back in the evening and start using various equipment, so you see two huge peaks that is happening, right? So what we said is in the western countries, especially Europe and the US, they said we'll create a demand response program. We will send a message to our consumers saying that, you know, reduce your consumption at this point or increase your consumption at this point. The problem with that approach is in India, especially, there are 800 million people have access to electric grid and about 150 million people have access to internet, you know? So even like when they have the smartphone, I mean, even I don't try to turn on the, you know, cellular data connectivity, we want to save them, you know, money as much as possible. So we said like, you know, how can we create a tool that can, you know, do this demand response without depending on the internet? So that's what we called Nplug. I'm sure, you know, most of you must be familiar with Unix, right? Unix has a nice command that reduces the priority of your job. So that's what is a nice plug is what we created here. So what you do is you take your appliance and plug it into this guy and take this guy and plug it into the wall socket, right? So this guy will sense the condition of the grid, right? So I'll go into the condition, the details later, but it's about $20 in large volumes. And, you know, it's very simple to install and operate, like how we install the power strip, even though it was a little bit complicated today. But generally, you know, it's as simple as that, right? So when you look at the voltage levels in your grid, right, you see the fluctuation. So we measured the voltage level in Bangalore for seven days. And, you know, that is a fluctuation. Each color is for a different day, and this is how it fluctuates throughout the day. So voltage actually indicates a load level on the grid. You know, the grid that is closest to your home or the transformer that is closest to your home are the measuring point. And frequency indicates the imbalance between the generation and load, right? So that is what is the two conditions. So using the two indicators, without any signal from the utility, you can decide whether to shift your load or not shift your load. Okay, this is the peak hour, so I will shift my load. This is not an off-peak. This is an off-peak hour I can consume right now, right? So essentially, this is what is the algorithm, right? Fundamentally, if the, you know, the current voltage is less than the lower voltage level, you know, the threshold, then you don't consume it. And if the current voltage level is greater than the upper threshold, that means the grid is less loaded and you do it, you consume it. And otherwise, you compute a probability as a proportion of this distribution, VC minus VL and VU minus VL, right? So it works beautifully and, you know, this particular work was given an award, MIT TR35 Award to my colleague who led the work. Her name is Tanuja. So the TR35 Award is given to 35 innovators under the age of 35, like the past winners include, like Google founder and Tesla founder, et cetera, because it's extremely powerful to help the grid stability and it, as you can see, it involves IoT and sensing and it's local analytics. It's edge computing. You know, it's not like you're transferring data to some other place doing the analytics and you can do this. So at this point, I just want to digress and say what we have been thinking about is, like, how do you do hierarchical analytics systems, right? When you look at the architecture of the brain, there is the lowest part of the brain which is called the amygdala, which is the reptilian brain. That is mostly the stimulus and response. You know, when you touch something hot, you immediately take your hand away. So that is very quick, but it cannot do any signal processing, any simple processing, et cetera, right? So that is the lowest part of the brain and the middle part of the brain that does, you know, a little bit of language and everything and the slowest part of brain in the neocortex, which can do, like, think with a future planning, you can, you know, plan how do you make a strategy? How do you go and hunt an animal kind of thing? So we want, we propose this kind of thing for the energy systems also, for the low latency and avoiding network thing, do computing and analytics at the edge and do something at the substation level or the transformer level, and the highest level is the cloud level. You get the data from all the places and do computing, which is slow, but you can do more sophisticated analytics. So this is what is basically in simulation we showed how this can be done, how this N plug can be effective. As you can see, the two peaks here completely gets distributed over multiple areas. There are, there is a concept called rebound effect. Like, let's say everybody shifts from the peak cover to the off peak cover. You create a new peak cover, but that is a, that is another, you know, detailed topic, which I don't want to discuss, but, you know, something that we are doing this demand response, you need to look into that, right? So there are two discuss, right? I mean, so there is this paper, you know, if you can look at, search for David Smalley, he's a Nobel Prize winner, and he took like 10 important problems that are, that we have, like, you know, water, terrorism, poverty and everything. And he says energy is the most important problem. If we can get access to clean and cheap energy, every other problem can be solved. So to give you an example, right, there's a water scarcity, but if we find, you know, very cheap and, you know, clean energy for desalinating the seawater, we have so much of seawater, you know, we can solve the water scarcity problem. So he, you know, he goes through all the other nine problems and says how it can be solved with this. So Indian government is pushing for 100 gigawatt solar and 75 gigawatt of wind and all that stuff, basically 200 gigawatt of energy from clean energy sources by 2022. And also government has said by 2030, all the transportation, all the consumer transportation will be electric vehicles. They moved Ashok Jhinjanwala, a professor from IIT Madras to the central government to lead this effort. So there will be no internal combustion engines, no fossil fuels, no pollution, right? And what is happening is there are like the storage costs, especially you must have heard about the Tesla, Elon Musk, he has started something called the Giga factory in Las Vegas. So the lithium ion battery prices are coming down like really drastically, right? And for instance, recently the government is working on shifting the Andaman Nicobar islands, the only source of electricity there is diesel. They're completely converting that into a solar plus storage, storage for the night, whatever energy gets generated in the morning and use it in the evening, right? And there is the concerns about the climate change and the Paris Accord. And like Trump cancelling the US commitments to the Paris Accord. But the good thing is 40 of the 50 US states have committed to the Paris Accord and they are saying that they are going to stick to the same levels of clean energy and pollution reduction and environmental protection, et cetera. So a lot of interesting things are happening, right? And Zainab told me that, you know, the key thing is I need to give a few take away items. You know, I can talk all the academic and theory things or whatever, but what is it for a practitioner to look at, right? So I want to recommend, you know, a few books, right? I'm a dilettante in machine learning and data science. I'm a more embedded guy. So this book like from the UK Open University and the time series analysis, I found it very useful, very easy to understand. And this is a very nice book by Cambridge University physicist, Sustainable Energy Without Hot Air. It is completely available for free. And he is also a data scientist. He looks at like brilliant data and comes up with very interesting insights. I would highly recommend looking up his site. There are public data sets for you to look at, you know, red data set. They have taken data from multiple homes at minute level granularity. And Nivea is a National Institute of Wind Energy. They have published a lot of data and Indian Meteorological Department publishes a lot of weather data. So you can look at all these things. You can do analytics. You can do what you can do. And finally, if you want to do any kind of do it yourself, you know, you can buy energy meters to find out what your consumption is. You can collect data from your home, from your research lab. You can do various kind of analysis. You can build your own equipment. Nowadays, all also these micro inverters are coming up. Maybe you can set up solar on your home. You can try it out. And finally, we are hiring data engineers and data scientists. We also take interns. So if anyone interested, please contact me offline. So thank you. We take questions. Regarding the problem of... Regarding the problem of solving the peak hours load problem, do you think dynamic pricing of the consumption will solve that problem? Yes and no. So the dynamic pricing is already there, right? There are two issues, actually. There are government regulations. They don't want to expose the consumers to the fluctuation in the real-time price markets. And second thing is, by the time you take a decision for the price, the condition could have already changed because, as I said, right, things are changing at the speed of light. So either you have to have a time of use pricing, which is already implemented. Say for instance in Tamil Nadu, the peak hours between 6 p.m. and 10 p.m. the electricity price is 1 rupee per kilowatt hour more. That is what is at 20% more for that time. So that they do it, but still you don't get this kind of smooth level of peak reduction. So there are companies, what they do is there is a company called NRNOC, Entelios. What they do is they talk to the industries and they say that, okay, if we send you a signal, you reduce your energy consumption at that time. We will give you financial incentives for that. So that kind of mechanisms are also existing. Peak load reduction credits, CPP, which is a critical peak pricing and so on and so forth. This side. Hi, thanks. There's two questions. One is that a lot of these problems, of course, in terms of load balancing or load generation, have existed for decades. In the past, I'm assuming, you know, largely... I'm not able to hear you well, sorry. Can you hear me now? Yeah. I think a lot of the use cases you discussed, of course, have existed in the energy networks for decades. In the past, I imagine a lot of it was done with regression modeling. The question is... What modeling, sorry? Regression modeling, I suspect. How has machine learning changed, you know, the ability to predict or deliver some of these use cases in the last 15 years? That's question one. The second is that when you look at the value, you mentioned that you can attend to use cases that are at the edge or at the substations or things that you deliver from central cloud processing. How much value do each of those stages typically give in terms of energy efficiency or better management of the load? Do you see it's at the edge that ends up giving a lot of the value or is it stuff that you do at the center that gives maximum value? Okay. So the first question about, like, how machine learning improved load balancing, right? Is that your first question, actually? So what happened is, first of all, as I was saying, right, they used to predict only how much is going to be the load in the area. Now the generation is also unpredictable because of fluctuation in the solar and everything. Second thing is, let's say if you want to participate in a demand response program. So we even published a paper, you know, user sensitive scheduling of your appliance scheduling. Let's say if you are in a commercial building and you want to participate in a demand response program, you want to find out when people are going to be using particular areas or when people are going to be, you know, say, for instance, right? You know that there is going to be a power cut between 11 a.m. and noon every day. And then you are going to be using diesel generator to do the cooling at that time. When you are using diesel generator, it costs about 20 rupees per kilowatt hour because you get like three, three units of energy per liter of diesel. So each unit becomes about 20 rupees. So what are the building manager can say is I'm going to pre cool the building a little bit before. I know usually this area is going to be more occupied with during the power cut hours. So I'm going to predict the movement on the occupancy and I'm going to pre cool the area and reduce my energy cost. So that is one thing, right? And second thing is like for a consumer, I want to see how do when I when I want to schedule my appliance usage, I don't want to inconvenience myself to participate in the demand response program, especially when I say myself, it can be a building manager who's doing the participation for all the consume all the consumers are occupants in that area. By doing the machine learning, he can learn the preferences and then do the scheduling accordingly. So that are the those are the two examples that I could come up with. And your second question was what was your second? Can you remind me again? Yeah. Yeah, so it depends on the kind of thing. So there's a concept called virtual power plants or, you know, demand aggregation. Let's say there are five buildings right next to each other, right? So let's say one building wants to consume a little bit more extra energy or think of it as like five data centers in the like in a Silicon Valley area kind of thing. Suddenly one one data center has more load. They can say that, you know, I will balance it with the next data center over. So it is a substation level load aggregation or a virtual power plant kind of thing. So you're doing it at the substation level. So at the end level is what I already explained, you know, how do you do that? And the final thing is the cloud thing. You know, I want to decide how much generation I want to do for the entire area or how much is the price going to be or incentive going to be for the entire community kind of thing at the cloud level. I don't know the answer to the question actually because the objectives are a little different, right? I mean, you know, it is not like the, you know, it's not about the energy efficiency, right? I mean, it's each guy is participating with a different objective like the building at the lowest level. I want to operate my local network most safely and optimally and also economically that is at that level. Whereas the utility guy tries to minimize the amount of incentives they want to give to the customers. And whereas in the it is like when the two customers are cooperating and reducing the demand together, it becomes statistical division multiplex us and put it here, right? I mean, the radio clock counts the 60 Hertz in the U.S. or 50 Hertz in India because our frequency fluctuates so much time the clock starts drifting because it it's not able to count like it's every 50th cycle is one second over and I can count. It doesn't happen. So it's very hard to do that. Last question. Nice talk. So one solution which I could see when I've been to UK recently is first they pay for the electricity. I'm not able to hear you. So one solution which could be possible is in UK they pay for the electricity and then they consume so they can actually monitor how much of the money that they're paid is spending and depending on that they can use the electrical appliances in their house. So would that kind of a solution be helpful in estimating the demand and being much more deterministic. Okay. I was not able to hear you well, but I kind of understand what you're saying. So what you're saying is like by looking at the electricity bill you want to you infer prepaid okay. So actually there are prepaid electricity meters in some parts like in places like South Africa. They do have the prepaid electricity meters. I heard even in Maharashtra some places they give the prepaid electricity meter. So yes definitely using the economics to control electricity consumption is very good actually right. When I was growing up if I leave the light on my father would come and give me a slap. Right. I mean I leave the room. I turn the light off and come. But what happens is that is the the person who is using it is also paying for the electricity bill like let's say if you are working in a commercial building. I used to work in money at the tech park people would just come into the conference room and forget to turn on turn the AC off or lights off etc because they are not the ones who are paying for the consumption right. So and second thing is you don't even know where the wastages like we did another work in which we found out what are the anomalies of some of the appliances like there was a refrigerator that was continuously leaking consuming energy. It was never going into an idle mode because the gasket that was around the refrigerator door was broken actually so the hot air was going in and the refrigerator was cooling it cooling it cooling it kind of thing. So there is so much of energy wastage that happens. So those are the things that cannot be cannot be solved just by the economic means. Yeah. Thanks Teva for this presentation. Next.