What went behind developing Insights-as-a-Service?

The Energy Blog

Tag: Data Analytics

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CAN- Compressed Air Network Optimisation Algorithm! #EnergyAnalytics #EEHub

Compressed Air is one of the forms of energy that finds use in most industries, in some industries like Textiles it contributes to significant portion of the overall energy consumption.

Compressed Air Networks i.e. multiple number of compressors operating in tandem, offer potential for optimisation and improve energy productivity CFM/KW by around 5%-7% (in some cases going as high as ~12%)

Multiple factors must be looked at while considering optimising the generation and distribution of Compressed Air and the overall network.

CAN- Compressed Air Network Optimisation Algorithm

Our Algorithm works on the operational data I.e. data from each of the compressor, operating behaviour and over a period suggests optimisation possibilities resulting in better utilisation of the network, correct operation settings, resulting in energy productivity gains.

Factors/Aspects considered by the algorithm:-

  1. Weather Conditions
  2. Set Points for each compressor
  3. Operational behaviour of each compressor under the influence of the network.
  4. Maintenance

Algorithm would be available on EE Hub, users could either purchase API calls or use the algorithm as DIY tool through our web based application EnView.

 

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How can governments do more with (tons of) Industrial #EnergyEfficiency related Data?

Earlier this month we hosted India’s first Energy Data Analytics Summit in Delhi, where one of the discussions led to some people asking “What can governments do with (tons of) Industrial #EnergyEfficiency related Data?

This question to me has a lot of significance. Just as the way all Governments use Health, Economy, Inflation, Education related data for greater good (social and economical), why treat Industrial #EnergyEfficiency related data differently? While one may argue that there is no precedence, but given the age in which we are living, we are seeing use cases evolve every day.

In this blog i intend to initiate dialogue on two of the use cases for greater use of Industrial #EnergyEfficiency related data and who stands to gain and how?

  • Bench-marking

Hard to believe that most industries/sectors do not yet have correct bench-marking methodology/tools available.  I am using the term “tool” here because industrial processes have a lot of variables and hence one cannot have a “number”, but there is no excuse to have not been able to develop a “tool” yet.

Will the government make the tool? NO. But they can open up the data for other solution/service providers, let them develop applications on it. Innovation can foster.

Contrary to what many believe “opening up data” doesn’t mean that anonymity can’t be maintained, there is no need to share raw data, connected insights/indicators could do a lot of good straight away. Mix of qualitative and quantitative data (derived output from raw data) allows enough room for innovation.

  • Investment Trend & Success

Fact that many governments have been collecting data on Industrial #EnergyEfficiency for over a couple of decades, allows them to keep track of investment trends, returns being yielded, % increase in efficiency YoY, which sector does well and which is lagging etc?

Governance can be simplified drastically if data is put to use. Its happening everywhere, there is no reason why Industrial #EnergyEfficiency has to be ignored. Designing Industrial #EnergyEfficiency policy w/o data points is not worth it.

Some of the other use cases could be:

  • AI driven assistance in Measurement & Verification (Example: PAT Scheme in India currently relies on 100% human driven verification process)
  • Predicting performance, forecasting demand supply situations instruments linked to Energy Efficiency Projects ( White Certificates/ Energy Saving Certificates/ ESCerts (PAT Scheme))

What’s your take on this? Would love to hear from you on “How your Government could do more with Industrial #EnergyEfficiency related Data?”

Regards,

Umesh

 

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Discussions, Deliberations and Engagement at India’s First Energy Data Analytics Summit

The Energy Data Analytics Summit 2017 hosted by EnergyTech Ventures at The Claridges, New Delhi, on 8th September 2017 was a grand success. The event saw around 90+ delegates and 15 speakers from reputed organisations take part and have engaging discussions on the Future of Energy Data Analytics in today’s emerging economies.

There were high ranking officials from Businesses like – Coca-Cola, Aditya Birla Group, Welspun, United Phosphorus Limited, Vardhaman; from IoT/IIoT companies like – Bosch, Zenatix, Loudcell; from Development Agencies like – SIDBI, World Bank, IFC, GiZ, Shakti Foundation and from Think Tanks like – TERI, WRI; and NGOs like – Carbon Disclosure Project and The Climate Group; present at the Summit as delegates as well as speakers. TERI, The Climate Group and Inc42 were Supporting Partners to the Summit.

Opening & Key Note Address

Mr. Abhishek Rungta, (Founder & Advisor at EnergyTech Ventures) welcomed the delegates and was followed by the Keynote Address from Mr. Umesh Bhutoria (Founder & CEO at EnergyTech Ventures) who set the tone for the day by speaking about the ‘Why and How of Energy Data Analytics’. He touched upon the importance of Energy Data Analytics in today’s Industrial age and why there is immense potential in the field of IIoT.

Numerous future possibilities like better Resource Productivity, Open markets in terms of data sharing/ value creation, better Governance in framing enabling policies were addressed by him along with some immediate challenges like the need for a single platform, need for collaboration among the various members of the entire IIoT ecosystem.

There were 3 Thematic Sessions and a Fire-Side Chat Session which saw lively discussions from the participants and the audience.

During the first thematic session, the topic addressed was – What would inspire Businesses to invest in an Energy Data Analytics Strategy.

Mr. Jarnail Singh (India-Head, The Climate Group) touched upon the fact the Businesses need to prepare for Climate Change and not treat Climate Change as a Business. He also said that more and more companies are demanding tools that could allow them to understand their data better and hence take decisions towards Energy Productivity improvement targets.

Mr. Nandakumar Sankar (Head-Sales, Energy Solutions and Analytics, Bosch) talked about Industry 4.0 and what kind ROI can Data Analytics bring about for organisation. He also said that they came across a lot of organisations that have either no data or very low data and he feels that can be changed once there is a better understanding of how data can be used and we see an evolution of the use cases.

The second thematic session talked about – How can Data and Technology be leveraged to scale Supply Chain Programmes and reach out to more SMEs.

Mr. Shubhashis Dey (Program Manager, Energy Efficiency, Shakti Foundation) mentioned that Manufacturing SMEs have a multiplier effect on Energy Efficiency Programs if proven to work positively.

This was concurred by Mr. Deepak Krishnan (Manager, Energy program, World Resources Institute) and he added that the trust factor is also very important for SMEs as they need to truly believe that their Data cannot be used by anyone else for their benefit.

Mr. Rajiv Kumar (CEO, ISTSL) also agreed with these views and explained how Data Digitization is helping bringing about easy sharing of this data in a trusted manner. MSMEs can are now being able to adopt and implement Energy Conservation Measures in an easier manner due to various initiatives taken up by various Organizations.

The third session was taken via video call by Mr. Steven Fawkes (Founder of EnergyPro, London). He spoke about – Business Model Innovation in context of Industrial Energy Efficiency and Energy Productivity. One of the most significant message from his presentation was

“There is no market for Energy Efficiency, there is only market for Stuffs and Services”. This is where data and technology play a role in making the conventional markets work in an unconventional way.

EnergyTech Ventures also then announced the launch of the Energy Efficiency Mirco-Services Hub at the end of these 3 sessions. Boost EE is a hub of sector specific Algorithms/APIs that allows Technology players /IoT Platforms to leverage sector specific algorithms, enhance offerings and thus create more value for all Stakeholders.

Currently 20 APIs from across 4 sectors and 6 categories have been hosted on the Hub, EnergyTech Ventures aspires to take the API count to 100 by the end of 2018. 

The final session for the day was the Fire Side Chat which was panelled by Mr. Girsh Sethi (Senior Director, TERI), Dr. Satish Kumar (Ex-Executive Chairperson, AEEE), Mr. Damandeep Singh (Director, CDP-India), Mr. Prabir Niyogi (Chief Executive, RP-Sanjiv Goenka Group), Dr. G C Datta Roy (Founder & CEO, DESL), and Ms. Vandana Gombar (Editor, Bloomberg New Energy Finance).

Mr. Singh spoke about the importance of disclosure when it comes to Energy Efficiency and Clean Energy as large funds companies to document, estimate and make public any environmental risks. Around 6000 companies and 600 cities have disclosed to CDP and how this Data is used for the betterment of the economy as a whole.

Dr. Roy, with over 50 years of experience in the field of Energy spoke about need to take orbital approach in mainstreaming Energy Efficiency as against to the incremental approach being taken currently.

Dr. Kumar postulated that Data is required from the consumption side as well as the production side and Data cannot work in isolation. According to him, Data brings Accountability, Responsibility and finally Action.

Mr. Neogi spoke from the Utility point-of-view and agreed that the economics are changing and utilities have to adapt to the same. The emergence of renewable energy is bringing about a shift in how Utilities conduct their business and what has to be done to keep abreast of the same.

Ms. Gombar from Bloomberg New Energy Finance presented some interesting statistics on how for some development banks spend on Energy Efficiency had surpassed spend on Clean/Renewable Energy.

The end of this session was followed by the final announcement of the next Energy Data Analytics Summit in September 2018 in Jaipur, it’s going to be a 2 day event and we are hoping to have some International flavour to it as well!

Also announced conducting Sector Specific Workshops on Energy Data Analytics under our “AIR Series” Workshops.

For more details one can visit https://haveyouaired.com/ .

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Retrospective assessments before moving to predictive alerts #DataAnalytics #EnergyAnalytics

Greetings!

I was recently onsite for a data discovery exercise, unit has one of the largest single location manufacturing capacity in its sector. Of a lot of data sets we looked at, one of the interesting case that came in front of us was that of a vibration of a Fan, one that is very important in the entire process, lowering of the operational RPM could result in significant production loss.

We took past data sets and wanted to understand how the retrospective assessment is done by the team, as expected a lot of time went into fact finding and was dependent on a lot of people. Besides taking time, no one could point out exactly when the issue started building up and when would have been the right time to respond to it?

What did our algorithms (series of logics, no ML really) find out?

1.       Total of 1953 peaks happened, where in the rise in vibration % was such that if continued it could have mean an X% increase over 24 hours.

2.       1606 cases where the peaks where in consecutive points the vibration increased by 50% of X%, we have called them as Alerts. (In the current scheme of things alarm only goes when things are out of control)

3.       823 out of 1606 cases had consecutive alerts, in quite of a few of them 4-5 alerts came in successively. (Remember these alerts are not simply a>b “raise alarm”, it tracks the tendency and past pattern)

4.       There were 7 occasions (exact date and time pointed out) when plant had an unplanned shutdown (over 8 hours) and the problem could have been addressed. (Next time when that happens an maintenance team already has a ticket to address the issue)

5.       Algorithm automatically pointed out how maintenance activity in one of the cases could normalize the increase in vibration %, while in the other they couldn’t or perhaps no action was taken. (So if a ticket is marked resolved and technically the problem stays, the algorithms points out it close to real time)

6.       Because of last two tickets going un attended the unit lost out 7% production over a stretch of 5 weeks and had to wait for another unplanned shutdown to address the issue!

Point 1 to 6 happened even after people were looking at the screens 24X7! Time to have people taking actions and not looking at screens, real time monitoring is a thing of past, but to move to future the team needs to of adequate tools to do retrospective assessments and eventually go on to work on systems/tools that predict an anomaly building up well in advance!

Well that’s a real case study! Liked it? Would love to hear your views/thoughts!

Best Regards,

Umesh Bhutoria

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Investing in Data Management Application? Have you considered these 3 points?

Greetings!

Over the last few months we have seen organisations investing or deciding to invest in suite of web and mobile applications to manage data and automate part of reporting process when it comes for scaled #EnergyEfficiency or #CleanerProduction focused programmes.  3 things that one must consider before deciding in selecting the right vendor:

1. Thought Leadership

Use of #AI or simply put series of logics to automate certain processes is evolving, there is a lot of noise when it comes to people talking about it. One must decide to work with partners that have worked on similar applications before and have had the habit of innovating in the domain.

2. It’s not about IT

Developing an application or designing a form is not the important part. What the system does and how it helps is important? Some of the potential benefits that must come from such a system is reduced project management costs, standardisation etc? So if your vendor has not delivered it before there are chances that it they might fall short again.

3. Business Model Innovation

Such applications have to evolve every day so that they can last for 4-5 years, hence it is important to consider innovative business models before embarking on the “product” development. Vendor with core interest in such applications is best suited as against to a conventional “Developer”

EnergyTech Ventures is an emerging company that has the largest portfolio in the #DataHub Space with it’s application in the #EnergyEfficiency and #CleanerProduction programmes being used by 100+ factories in 6+ countries. We have helped organisations reduce the operation costs by around 30% when it comes to data crunching, validation and reporting.

To know more about our work please visit us at www.entechventures.com