Solar Energy is helping eliminate Energy Poverty
From only about 30 GW of renewables today, India aims to achieve a capacity of 175 GW by 2022, of which solar is going to account for about 100 GW.
The target by 2030 is 350 GW, of which 250 GW is likely to be solar-based. This translates to Annual Growth of 29% YoY for next 7 Years and 21% growth after that.
Solar and storage together will also act as a tool for lifting 35 crore Indians off-grid —around 50% of India’s rural population — and out of energy poverty.
In this 21st century access to electricity is fundamental right and India should leapfrog implementation by directly going solar in India.
In industrial setups, Machine Learning and AI algorithms can make the prediction of energy generation, any problem in the site or manage the maintenance schedule autonomous without any human intervention.
Households can produce power as low as 1KW and sell it to grid or to their neighbors.
India will reportedly require about $250 billion of investment in the renewable energy sector by 2022 to achieve these Goals.
The big question remains how is India going to achieve this ?
The Only way that is possible is that public-private partnerships that ensures the success of implementation of government programmes without leakage.
People often ask me “Why are Solar Panels Expensive?” and I thought it is best to explain it here once and for all.
Most people think that a Solar Panel is just Silicon Diodes. So let me explain what a Solar Panel is made up of:
- Silicon Diodes
- Aluminum Frame
- Glass Protective Layer
The Silicon Diodes take up about 50% of the total cost. The rest of the expense is in commodity metals like Aluminum. The prices of these other metals are pretty stable over a period of time.
The Silicon Part of the Solar Panel, of course follows Moore’s law.
Also, the problem lies in the efficiency of the Absorption co-efficients. Most diodes in commercials panels are pretty in-efficient.
This means: Only 18 to 21 % of energy is absorbed and converted to electricity at STC, in the field they absorb abysmal <15%
These factors make per-watt cost of Solar Panel more expensive.
If we just double the panel efficiency, we could use 50% less aluminum, 50% less glass and 50% less silicon for the same wattage.
So instead of ‘Why?’, the real question should be..
How can we make Solar Panel less expensive?
Happy 2016 From ThingsCloud Family. 2015 Has been very good for ThingsCloud. Some of the significant achievements are:
1) Got into Top 20 in GMIC
2) Showcased Products in IIMB Bootcamp
3) Became part of IBM Global Entrepreneurship Program
4) Became Part of RevvX Hardware Accelerator
5) Showcased Product at IoTNext Conference
6) Applied for Patents
Looking forward to Big bang 2016.
The Paris Climate Summit was a great success because:
1) It was attended by 195 countries and most of then acknowledged need to control and limit emissions
2) Developed countries openly acknowledged that they had greater roles in causing climate damage
3) Developing countries argument that they need to grow by burning cheaper coal and Developed countries should find solutions for cheaper alternatives found resonance.
So, what did Paris summit achieve
1) Almost all countries, around 180, agreed to limit temperature rise to 1.5 degree C.
2) Developed countries should voluntarily commit $100 Billion to fund Clean Energy projects in poor countries.
3) Almost 180 countries has given their emission target for 2020 and developed countries must adhere to the targets and developing countries are encouraged to
4) There was no penalty for missing the targets, but every party is encouraged to achieve and emission are monitored
Overall it is a win-win for everyone and all countries has pledged to work towards cleaner environment and better earth
When Facebook shows you a list of friends you may know, Google letting you know an ETD to be on time for a meeting, and other e-commerce websites giving you recommendations on things to purchase, these are instances where machine learning is carried out on large volumes of data called Big Data.
According to Gartner, the big data market is worth over $250 billion and surely it is here to stay. Businesses of all sizes that deal with various applications have started to adopt these practices.
Companies are now focused on how to store and manage this voluminous data. How should we architect the business’ technology stack to gain value from Big Data in terms of HDFS, complex event processing, NoSQL and machine learning? Store data on prem or cloud?
By means of advanced analytics, and machine learning, companies tap into their insight-rich vein of experience and mine it to automatically discover and generate predictive models to take advantage of all the data they are capturing. Departing from the traditional style of looking into the past for insights, companies can now predict parameters that they want knowledge about.
The value of machine learning is in finding structures that we have never seen before and precisely modelling to assist in decision making.
At TTC, we are leveraging these to build intelligent models that can serve our customers recommendations about optimising their usage patterns and first hand information about dynamic pricing for compliant infrastructures. We are developing these models in the energy sector where machine learning is hyper critical
It is our please to let you all know that we will be at Indian Institute of Management, Bangalore (IIMB) for the Eximius Annual Bootcamp. We are one of the 20 startups selected to present there and have a booth for all two days. If you are around there please say hi to us and our team. We would love to hear from you all.
Data is becoming one heck of a challenge to solve. Take a example of our Product ThingsHiFi- a 5KW Solar Grid-Tie Inverter. This device send data every 30 seconds to ThingsCloud. On a daily basis we will have
(30/60)*60*24 = 2,880 insertions
Each data packet is around 6K byte, which makes
2880*6k= 17.2 mb/per day
For one month,
17.2 *30 = 516 mb/per device/per day
You can see where I’m going from here, even if we have a modest 1000 devices, then we will be looking around 500 gigs of data per month. Right now we are exploring different architectures to process this data. Also, inserted record has around 10 values.
So, per month per device it is
2880*10*30 = 864,000 values
Now, with 100 devices, it would be 86 million values, which needs to be queried. Suddenly from nowhere we need to work on big data …!!!