Page 40 - The 10 Most Innovative Green Energy Startups 2019

igital transformation has become the mainstay for all businesses today and one of the drivers of
this revolution is Artificial Intelligence (AI). There is no denying that AI is destroying seemingly
Dinsurmountable business barriers at an all astounding rate. Today, AI is instrumental in transforming
the way all industries work – from dynamic manufacturing, healthcare industries or the rapidly evolving
automotive and power sector.

All industries, especially heavy equipment industry, are undergoing a rapid digital transformation designed
to meet the two objectives - faster product regeneration and systems optimization. While digital building
blocks (such as DSI & MBE, IIoT platform) form the first part of achieving this transformation, the second
is all about getting deeper insights from collected data, using Statistical, Machine Learning & Deep Learning
techniques. Here is where AI can be a powerful tool to make a difference.
Challenges faced by Indian Power Sector

Despite the encouraging growth trajectory in the power sector over the last few years, the Indian power
sector has still not been able to achieve and sustain the production capacity that matches the ever-growing
power demand of the country. There are various challenges responsible for this shortfall. First among these
are the unavailability of raw materials - thermal capacity addition is plagued by the growing fuel availability
concerns, while gas-based capacity is idle due to non-availability of gas. In addition to this, there are AT&C
losses and operational inefficiencies, coupled with financing and regulatory challenges.
India needs a balanced regulatory intervention that can resolve immediate issues to mitigate these concerns.
A robust and sustainable credit enhancement mechanism for funding should be put in place and an optimal
fuel mix strategy needs to be developed for both conventional and non-conventional forms of energy.
Most importantly, a public private partnership model needs to be encouraged to ensure profitability so that
operational efficiencies are in place.
Top 3 Areas of Power Sector that are transformed by Artificial Intelligence

AI has already been crucial to various aspects of design, development and aftermarket phases for any major
product in the power sector. For product development & services, AI based apps and software act as an
interface between machine and human beings, this works well in both General AI (GAI) and Applied AI
(AAI) functionalities. GAI boasts of machine intelligence that allows intellectual tasks to be performed by a
machine, while AAI is about in-depth machine learning and predictive analysis, to both learn as well as adapt.
The top three segments that are affected –

Low Cost, Customized Products advantage for Equipment Manufacturers:
AI can help designers, developers and product managers to create designs for steam/Gas/Wind/Solar products
that will be more easily acceptable to generation companies and also offers more options in terms of product
performance improvement. The need for customization and localization of products to make power generation
companies comfortable can be met with AAI. For manufacturing products that are designed according to
specific patterned lace with AI are more likely to reduce manufacturing trial and error cost, while ensuring the
process is future ready.

Generation: Reduce Unplanned Downtime via Predictive Maintenance
In the power sector, the most critical aspect of a generation cycle is the downtime that could ensue due to a
breakdown of systems or machinery. The process gets disrupted often without warning and the result is an
exponential cycle that effects the entire supply chain. The resultant losses could run into millions of dollars!
With digital transformation, there is a data collation aspect involved for every part of the industrial process
and its related systems. However, as time passes, the parts and hardware experience wear and tear, and soon
small problems snowball into an exponential breakdown. Pre-empting and identifying this critical breakdown
point using relevant data is a step for predictive maintenance.
AI and ML could create algorithms to predict downtimes and pre-empt the required actions to control the