How to become Data Scientist

past year, interest in data science has soared.Nate Silver is a household name, companies everywhere are searching for unicorns, and professionals in many different disciplines have begun eyeing the well-salaried profession as a possible career move.

In our recruiting searches here at Burtch Works, we’ve spoken to many analytics professionals who are considering adapting their skills to the growing field of data science, and have questions about how to do so. From my perspective as a recruiter, I wanted to put together a list of technical and non-technical skills that are critical to success in data science, and at the top of hiring managers’ lists.

Every company will value skills and tools a bit differently, and this is by no means an exhaustive list, but if you have experience in these areas you will be making a strong case for yourself as a data science candidate.

Technical Skills: Analytics

1. Education – Data scientists are highly educated – 88% have at least a Master’s degree and 46% have PhDs – and while there are notable exceptions, a very strong educational background is usually required to develop the depth of knowledge necessary to be a data scientist. Their most common fields of study are Mathematics and Statistics (32%), followed by Computer Science (19%) and Engineering (16%).

2. SAS and/or R – In-depth knowledge of at least one of these analytical tools, for data science R is generally preferred.

Technical Skills: Computer Science

3. Python Coding – Python is the most common coding language I typically see required in data science roles, along with Java, Perl, or C/C++.

4. Hadoop Platform – Although this isn’t always a requirement, it is heavily preferred in many cases. Having experience with Hive or Pig is also a strong selling point. Familiarity with cloud tools such asAmazon S3 can also be beneficial.

5. SQL Database/Coding – Even though NoSQL and Hadoop have become a large component of data science, it is still expected that a candidate will be able to write and execute complex queries in SQL.

6. Unstructured data – It is critical that a data scientist be able to work with unstructured data, whether it is from social media, video feeds or audio.

Non-Technical Skills

7. Intellectual curiosity – No doubt you’ve seen this phrase everywhere lately, especially as it relates to data scientists. Frank Lo describes what it means, and talks about other necessary “soft skills” in his guest blog posted a few months ago.

8. Business acumen – To be a data scientist you’ll need a solid understanding of the industry you’re working in, and know what business problems your company is trying to solve. In terms of data science, being able to discern which problems are important to solve for the business is critical, in addition to identifying new ways the business should be leveraging its data.

9. Communication skills – Companies searching for a strong data scientist are looking for someone who can clearly and fluently translate their technical findings to a non-technical team, such as the Marketing or Sales departments. A data scientist must enable the business to make decisions by arming them with quantified insights, in addition to understanding the needs of their non-technical colleagues in order to wrangle the data appropriately. Check out our recent flash survey for more information on communication skills for quantitative professionals.

The next question I always get is, “What can I do to develop these skills?” There are many resources around the web, but I don’t want to give anyone the mistaken impression that the path to data science is as simple as taking a few MOOCs. Unless you already have a strong quantitative background, the road to becoming a data scientist will be challenging – but not impossible.

However, if it’s something you’re sincerely interested in, and have a passion for data and lifelong learning, don’t let your background discourage you from pursuing data science as a career. Here are a few of the resources we’ve found to be helpful:

Resources

1. Advanced Degree – More Data Science programs are popping up to serve the current demand, but there are also many Mathematics, Statistics, and Computer Science programs.
2. MOOCs –Coursera, Udacity, and codeacademy are good places to start.
3. Certifications – KDnuggets has compiled an extensive list.
4. Bootcamps – For more information about how this approach compares to degree programs or MOOCs, 
5. Kaggle – Kaggle hosts data science competitions where you can practice, hone your skills with messy, real world data, and tackle actual business problems. Employers take Kaggle rankings seriously, as they can be seen as relevant, hands-on project work.
6. LinkedIn Groups – Join relevant groups to interact with other members of the data science community.
7. Data Science Central and KDnuggets – Data Science Central and KDnuggets are good resources for staying at the forefront of industry trends in data science.
8. The Burtch Works Study: Salaries of Data Scientists – If you’re looking for more information about the salaries and demographics of current data scientists be sure to download our data scientist salary study.

Smart Cities in India

Smart City  offers economic activities and employment opportunities to a wide section of its residents, regardless of their level of education, skills or income levels


By 2050, the world will witness a mass exodus of people into cities. 2 out of every 3 people will be living in urban areas which translates into 6.3 Billion urban dwellers. . By 2050, Asia and Africa will account for 86% the world’s urban population. India alone will add more than 400 Million people to its cities – that is twice the population of Brazil today. The consequence of this migration - especially in a fast growing economy like India - is a significant increase in the demand and consumption of resources. The coal reserves in the existing coal mines in India are likely to get exhausted in little more than 50 years at the current rate of consumption. India’s oil imports account for the biggest share in the Current Account Deficit. Cities contribute to 70% of India’s GDP. The growth of cities therefore is inevitable.

However, unplanned growth and distribution of resources could prove to be catastrophic to the economy and impede progress. Building sustainable and Smart cities from scratch and retrofitting sustainability features in already existing cities is the only way out.
Smart Cities are the only perceivable solution to urbanization of this scale.

A Smart city includes a structure that is resource efficient and has a minimal impact on the environment. Among other things, a smart city reduces the energy and water requirement by employing technology and smart construction & design techniques, reduces the generation of solid waste and uses renewable sources to meet energy requirements.

Additionally, to promote a more convenient way of life, a smart city incorporates a sophisticated Information and Communication Infrastructure. A robust transport network to move people is also established.
While retrofitting smart city-like features into an already existing city is a possible solution, it comes with its limitations. Retrofitting is more expensive and inconvenient primarily because of high replacement costs and limitations of the existing structures. The market for retrofitting is still in its nascent stages and therefore not fully understood. While areas like lighting, air conditioning, etc. have seen some technological innovation, other areas like disaster resistance are untouched. Permits and legal requirements are additional challenges.

Although building smart cities from ground up is a more feasible solution, it is a daunting task. The smart city concept has been experimented with several times in the past in different parts of the world. However, some have fallen victim to failure because of various reasons. Many unsuccessful attempts in the past were characterized by ambitious sizes of these cities, bold investments, technological misfits, poor urban planning, etc.


The Indian smart city landscaped must be engineered to encourage developing smaller and more realizable cities with technologies adapted to the Indian ecosystem and innovative financing. Building a strong support infrastructure, recreation options and promoting thriving businesses to flourish will make the city more habitable and desirable. Formation of communities must be allowed to follow a natural path.
Building smart cities is investment heavy and time consuming. However, India has to embrace sustainable methods soon to avoid eventual chaotic circumstances. The success of smart cities cannot be attributed to technologies alone. People must be educated about the importance and necessity of sustainable practices and the advantages of investing in sustainable settlements.

SMART CITY , GIS and FIVE PILLARS

What is smart  city

People migrate to cities primarily in search of employment and economic activities

beside better quality of life. Therefore, a Smart City for its sustainability needs to offer

economic activities and employment opportunities to a wide section of its residents,

regardless of their level of education, skills or income levels. In doing so, a Smart City

needs to identify its comparative or unique advantage and core competence in

specific areas of economic activities and promote such activities aggressively, by

developing the required institutional,physical, social and economic

infrastructures for it and attracting investors and professionals to take up

such activities. It also needs to support the required skill development for such

activities in a big way. This would help a Smart City in developing the required

environment for creation of economic activities and employment opportunities.

Smart City  GIS and Five pllars

GIS  five “pillars” namely Power, Water, Transport, Solid Waste Management and Safeguarding (Public Safety) and identifies enablers to better utilize Information and Communications Technologies (ICT). Governance, Planning, Infrastructure & networks, Data analytics, Geographic Information Systems (GIS) and Cyber Security have been identified as enablers. In reality GIS is similar to any other IT enterprise component, but for some reason GIS has been identified as a separate enabler. Ideally a “secure” GIS based IT enterprise should have been considered which - offers capability for “analytics”, can be used for “planning” and thus support in effective and efficient “governance”.

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Geographic Information Systems

The report refers to GIS as a “…system that involves superimposition of several layers of geo-data and information systems in a specific sequence to create a comprehensive geospatial / geographic information system”. Technically this statement still holds good, but gone are the days when GIS was used for viewing thematic maps and little bit of spatial analysis. Today GIS systems offers much more than that. A GIS can be integrated with – non-spatial data, multiple databases, multiple systems, real-time sensors and devices and so on and can be made available on cloud, web, mobile or desktop environments. I would prefer calling it “A system / solution that can capture, store, manipulate, analyze, manage, and present all types of data in a geographical context”.

Some observations specific to the way forward :

Power – Smart Grid does not find any reference to GIS. GIS is critical component of a smart grid facilitating effective and efficient - network management, asset management, consumer Information management, workforce management and outage management. Integration of call centres, billing, payments and other sensors from SCADA with GIS in a real-time scenario can offer actionable intelligence for plugging pilferage's, outage management and restoration and so on.

Solid Waste - Long term proposal recommends applying of GIS and GPS solutions to enable route optimization and process improvement. With GIS deployment and GPS enablement proposed as a medium term plan, ideally route optimization and process improvement can be accomplished at this stage itself. Most of the GIS softwares come with route planning and optimization tools now a days.

Water– While it addresses GIS integration, the report misses on pipeline distribution management, asset management, water quality monitoring and management etc. which are key components of water systems. With percentage of Non-revenue water (NRW) high in the Indian context, GIS can offer actionable intelligence to bring down the NRW.

Traffic – Smart traffic management could be accomplished on a GIS based platform. The smart surveillance can be integrated with such system and graduated to Intelligent Traffic Management System. In addition such system can also be used for planning, monitoring and maintenance of transport networks, asset management etc. which are critical components of traffic management.

Safeguarding (Public Safety) – City Surveillance, Command Control and CAD are addressed as separate entities. Ideally this should be an integrated system. It describes “CAD vehicles”, “GIS & GPS enabled vehicles” - in reality CAD is a software solution / system. On command control, a GIS based CAD system can be scaled up by integrating video feeds and multiple sensor data to offer enhanced locational awareness of the incident location. This can further be graduated to City Surveillance systems.

Data Scientist in 8 easy steps

Data Scientist in 8 easy steps

Six essential skills for Big data

Analytical Skills

Analytics involves the ability to determine which data is relevant to the question that you are hoping to answer, and interpreting the data in order to derive those answers.

If you have a knack for spotting patterns, and establishing links between cause and effect, then these skills will prove invaluable if you’re tasked with turning a business’s data into actionable plans of operation.

Creativity

There are no hard and fast rules about what a company should use big data for. It is an emerging science, which means the ability to come up with new methods of gathering, interpreting, analysing and – finally – profiting from – a data strategy, is a very valuable skill.

The corporate data superstars of the future will be people who can come up with new methods of applying data analytics in innovative ways. Often they will be solving problems that companies don’t even know they have – as their insights highlight bottlenecks or inefficiencies in the production, marketing or delivery processes. In particular, creativity is important for anyone hoping to make sense of unstructured data – data which does not fit comfortably into tables and charts, such as human speech and writing.

Mathematics and Statistics

Good old fashioned number crunching. Despite the growing amount of unstructured data being incorporated into data strategies, much of the information being gathered and stored, ready for analysis, still takes the form of numbers.

And even when dealing exclusively with unstructured data, the objective of the exercise is often to reduce elements of the data – emails, social media messages etc – to figures which can be quantified, in order for definite conclusions to be drawn from them. This means candidates with a strong background in maths or statistics are ideally placed to make the leap into big data enterprise.

Computer Science

Computers are the workhorses behind every big data strategy, and programmers will always be needed to come up with the algorithms that process data into insights. This is a very broad category which covers a whole range of subfields, such as machine learning, databases or cloud computing, which will be great additions to any budding data scientist’s arsenal. In particular you should be familiar with the range of open-source technologies – Hadoop, Python, Pig etc. – which make up the foundations of most big data enterprises.

Business skills

An understanding of business objectives, and the underlying processes which drive profit and business growth are also essential. The idea that a company will hire an “egg head” data scientist who will be locked away in a basement lab, to work their magic on data fed to them through a slot in their door, is dangerous and wrong. They should have a firm grasp of the company’s business goals and objectives as well as an understanding of the indicators which let them know if they are heading in the right direction.

Communication ability

Both inter-personal and written – an essential part of a data scientist skillset is the ability to communicate the results of the analysis to other members of their team as well as to the key decision-makers who need to be able to quickly understand the key messages and insights.

This also includes the skills of visualising and reporting data in the most effective manner. You can have the best analytical skills in the world, but unless you are able to make your findings understandable to everyone else you work with, and demonstrate how they will help to improve performance and drive success, they will be of little use to any business.

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Smart Grid Analytics

World Market for $1 trillion is being invested this decade in upgrading the power infrastructure globally to make the devices in the power grid remotely machine addressable. These devices include meters, thermostats, home appliances and HVAC equipment, factory equipment and machinery, and transformers, substations, distribution feeders, and power generation and control componentry.

Till Now 310 million smart meters have been installed globally. That number will more than triple by 2022, reaching nearly 1.1 billion according to Navigant Research. While representing only a fraction of the sensors on the grid infrastructure, the smart meter installation numbers provide a good indication of the penetration and rate of growth of the smart grid. These developments are occurring worldwide.

Collectively, these devices generate massive amounts of information. With recent developments in information technology, including elastic cloud computing and the sciences of big data, machine learning, and emerging social human-computer interaction models, we are able to realize the economic, social, and environmental value of the smart grid by aggregating the sum of these data to correlate and scientifically analyze all of the information generated by the smart grid infrastructure in real time.

By holistically correlating and analyzing all of the dynamics and interactions associated with the end-to-end power infrastructure—including current and predicted demand, consumption, electrical vehicle load, distributed generation capacity, technical and non-technical losses, weather, and generation capacity— across the entire value chain, we can realize dramatic advances in energy efficiency.

Smart grid analytics enables us to provide real-time pricing signals to energy consumers, manage sophisticated energy efficiency and demand response programs, conserve energy use, reduce the fuel necessary to power the grid, reconfigure the power network around points of failure, recover instantly from power interruptions, accurately predict load and distributed generation capacity, rapidly recover from damage inflicted by weather events and system failures, and reduce adverse environmental impact.

The advent of smart grid analytics represents a major advance in the development of energy efficiency technology. Many leading utilities including Enel, GDF Suez, Exelon and PG&E work with us to drive innovation by applying the science of smart grid analytics to the benefit of their communities, consumers, and stakeholders.

DATA SCIENTIST

Step 1: Graduate from a top tier university in a quantitative discipline

. Education makes a huge difference in your prospects to start in this industry. Most of the companies who do fresher hiring, pick out people from best colleges directly. So, by entering into a top tier university, you give yourself a very strong chance to enter data science world.

Ideally I would take up Computer Science as the subject of study. If I didn’t get a seat in Computer Science batch, I’ll take up a subject which has close ties with computational field - e.g. computational nueroscience, Computational Fluid Dynamics etc.

Step 2: Take up a lot of MOOCs on the subject – but do them one at a time

This is probably the biggest change, which would happen in the journey, if I was passing out now. If you spend even a year studying the subject by participating in these open courses, you will be in far better shape vs. other people vying to enter the industry. It took me 5+ years of experience to relate to the power R or Python bring to the table. You can do this today by various courses running on various platforms.

One word of caution here is to be selective on the courses you choose. I would focus on learning one stack – R or Python. I would recommend Python over R today – but that is a personal choice. You can find my detailed views about how the eco-systems compare here.

You can choose your path – but this is probably what I would do:

• Python:
• Introduction to Computer Science and Programming using Python – eDX.org
• Intro to Data Science – Udacity
• Workshop videos from Pycon and SciPy – some of them are mentioned here
• Selectively pick from the vast tutorials available on the net in form of iPython notebooks
• R:
• The Analytics Edge – eDX.org
• Pick out a few courses from Data Science specialization to complement Analytics Edge
• Other courses (applicable for both the stacks):
• Machine Learning from Andrew Ng – Coursera
• Statistics course on Udacity
• Introduction to Hadoop and MapReduce on Udacity

Step 3: Take a couple of internships / freelancing jobs

This is to get some real world experience before you actually venture out. This should also provide you an understanding of the work which happens in the real world. You would get a lot of exposure to real world challenges on data collection and cleaning here. 

Step 4: Participate in data science competitions

You should aim to get at least a top 10% finish on Kaggle before you are out of your university. This should bring you in eyes of the recruiters quickly and would give you a strong launchpad. Beware, this sounds lot easier than what it actually is. It can take multiple competitions for even the smartest people to make it to the top 10% on Kaggle.

Here is an additional tip to amplify the results from your efforts – share your work on Github. You don’t know which employer might find you from your work!

Step 5: Take up the right job which provides awesome experience

I would take up a job in a start-up, which is doing awesome work in analytics / machine learning. The amount of learning you can gain for the slight risk can be amazing. There are start-ups working on deep learning, re-inforcement learning – choose the one which fits you right (taking culture into account)

If you are not the start-up kinds, join a analytics consultancy, which works on tools and problems across the spectrum. Ask for projects in different domains, work on different algorithms, try out new approaches. If you can’t find a role in a consultancy – take up a role in captive units, but seek a role change every 12 – 18 months. Again this is a general guideline – adapt it depending on the learning you are having in the role.

Finally a few bonus tips:

• Try learning new tools once you are comfortable with ones you are already using. Different tools are good for different types of problem solving. For e.g. Learning Vowpal Wabbit can add significant advantage to your Python coding.
• You can try a shot at creating a few web apps – this adds significant knowledge about data flow on the web and I personally enjoy satisfying the hacker in me at times!

Few modifications to these tips, in case you are already out of college or hold work experience:

• In case you can still go back to college, consider getting a Masters or a Ph.D. Nothing beats the improvement in probability of getting the right job compared to undergoing a good programme from top notch University.
• In case full time education is not possible, take up a part time programme from a good institute / University. But be prepared to put in extra efforts outside these certifications / programmes.
• If you are already in a job and your company has an advanced analytics setup, try to get an internal shift by demonstrating your learning.
• I have kept the focus on R or Python, because they are open source in nature. If you have resources to get access to SAS – you can also get a SAS certification for predictive modeler. Remember, SAS still holds the majority of jobs in analytics!

Big Data for Industries

4 key layers of a big data system - i.e. the different stages the data itself has to pass through on its journey from raw statistic or snippet of unstructured data (for example, social media post) to actionable insight.

Data sources layer

This is where the data is arrives at your organization. It includes everything from your sales records, customer database, feedback, social media channels, marketing list, email archives and any data gleaned from monitoring or measuring aspects of your operations. One of the first steps in setting up a data strategy is assessing what you have here, and measuring it against what you need to answer the critical questions you want help with. You might have everything you need already, or you might need to establish new sources.

Database storage layer

This is where your Big Data lives, once it is gathered from your sources. As the volume of data generated and stored by companies has started to explode, sophisticated but accessible systems and tools have been developed – such as Apache Hadoop DFS (distributed file system), – or Google File System, to help with this task. A computer with a big hard disk might be all that is needed for smaller data sets, but when you start to deal with storing (and analyzing) truly big data, a more sophisticated, distributed system is called for. As well as a system for storing data that your computer system will understand (the file system) you will need a system for organizing and categorizing it in a way that people will understand – the database. Hadoop has its own, known as HBase, but others including Amazon’s DynamoDB, MongoDB and Cassandra (used by Facebook), all based on the NoSQL architecture, are popular too. This is where you might find the Government taking an interest in your activities – depending on the sort of data you are storing, there may well be security and privacy regulations to follow.

Database  processing/ analysis layer

When you want to use the data you have stored to find out something useful, you will need to process and analyze it. A common method is by using a MapReduce tool. Essentially, this is used to select the elements of the data that you want to analyze, and putting it into a format from which insights can be gleaned. If you are a large organization which has invested in its own data analytics team, they will form a part of this layer, too. They will employ tools such as Apache PIG or HIVE to query the data, and might use automated pattern recognition tools to determine trends, as well as drawing their conclusions from manual analysis.

Database  output layer

This is how the insights gleaned through the analysis is passed on to the people who can take action to benefit from them. Clear and concise communication (particularly if your decision-makers don’t have a background in statistics) is essential, and this output can take the form of reports, charts, figures and key recommendations. Ultimately, your Big Data system’s main task is to show, at this stage of the process, how measurable improvement in at least one KPI that can be achieved by taking action based on the analysis you have carried out.

Analytics to Utility Industry

1.     Analytics can help make the grid safer. 
Utilities need to beef up security when it comes to smart grids. A breach in smart grid security could spell disaster for utilities. Analytics could be used to help detect if someone is in the grid framework, tampering with meters and other tools. They could also see energy being improperly diverted. It’s already begun to happen and will continue. Smart grids are a powerful too, but they’re also highly vulnerable. Analytics could help maintain and beef up security

2. drive innovation and ultimately job growth. 
IT professionals are always in demand, but especially when it comes to the development side of the smart grid.
“Market for IT solutions will be one of--if not the--largest categories among all of the components of a smart grid. “The only larger category is transmission upgrades, which will be necessary for the grids of the future, but aren't 100% tied to actual 'smart' technology.”

3. Better understanding how the grid works allows for better efficiency.
Analytics help understand exactly how the grid works and is able to identify trends. Using this knowledge, utilities can help drive efficiency and better utilize their resources. This is something that must be done, too.
“Growth in smart grid is no longer a luxury for utilities—aging infrastructure, retiring personnel and the proliferation of distributed generation resourceS

4.Forecast severe weather events and other emergencies that could disrupt the grid.
One of the biggest issues with aging grids is the fact that anything can take them down at any given time. With analytics, these events can be better predicted and prepared for. Severe storms are more and more common, making grid outages more common.
“Analytics enable operators to monitor and report the exact times of service interruption at each system endpoint and use these results to measure improvement in restoration time from automated distribution processes,” . “This allows utilities to identify and restore outages more rapidly, without having to rely on customer inquiries.”
This means faster response times and faster repairs.

5. Manage the load balance.
As grids age, properly balancing the power distribution can sometimes be a bit tricky. However, by using smart grid analytics, power can be distributed as needed in an effective manner. .
Transformer load management analytics can utilize smart-meter data and actual  to continuously monitor and analyze distribution transformer loading levels and report on asset health, helping utilities make informed decisions to balance loads,”

Smart Grid – Key to Managing Energy Demand in India

Electricity consumption in  india has been climbing steadily for the past few decades. THE electricity market is growing at an accelerating rate due to higher consumption rates in the private, commercial and industrial sectors. Current domestic energy consuming behaviors pose unescapable fatal consequences that affect both  production and export levels. Therefore, an urgent action is needed to curb the increasing electricity demand and promote energy conservation. Smart grid is a dynamic solution which can bridge the gap between the current supply and increasing demand in india

What is smart grid?

A smart grid network makes for the ideal bridge where the goals of modernization can meet those of a reliable public infrastructure. Smart grid is a computerized technology, based on remote control network, aiming to completely alter the existing electric infrastructure and modernize the national power grid. This is through empowering the demand response which alerts consumers to reduce energy use at peak times. Moreover, demand response prevents blackouts, increases energy efficiency measures and contributes to resource conservation and help consumers to save money on their energy bills. Smart grid technology represents an advanced system enabling two way communications between energy provider and end users to reduce cost save energy and increase efficiency and reliability.

Advantages of smart grid

The beauty of adapting this technology will spread to not only utility but to all utility users including consumers and government.

Active role of consumer.

The beauty of smart grid is that it provides consumers with the ability to play an active role in the country’s electricity grid. This is through a regulated price system where the electricity rate differs according to peak hours and consequently consumers cut down their energy use at those high stress times on the grid. Thus, smart grid offers consumers more choices over their energy use needs. 

Upgrading the Existing Grid

Utilities benefit from improving the grid’s power quality and reliability as mentioned through an integrated communication system with end users with more control over energy use. This is through decreasing services rates and eliminating any unnecessary energy loss in the network. Thus, all these positive advantages will make smart grid technology a smart and efficient tool for utilities.

Contributing to Energy Efficiency

The government of India is in the process of taking bold steps to adapt new energy efficiency standards to reduce domestic energy consumption. For that, adapting and deploying smart grid will enable to modernize the national grid. With the time the government will build efficient and informed consumers as a backbone in its current energy policy. Moreover, this advanced technology will help with electricity reduction targets and contribute to lowering the carbon dioxide emissions. Thus, this is a great opportunity for the kingdom to mitigate with the climate change measures.

A Dynamic Approach

Adoption of smart grid systems will help India in increasing the efficiency of utilities as well as improving the ability of consumers to control their daily energy use. Smart grid technology offers a unique engagement that benefits consumers, utilities and government to become part of the solution. In addition, a smart grid technology is a viable option to enhance the value people receive from the national grid system. This smart transition will give the government a policy option to reduce drastically its domestic energy use, leveraging new technology through empowering the role of consumers’ active participants on the country’s grid.

As peak electricity demand grows across the country, it is important for India to make large-scale investment in smart grid solutions to improve energy efficiency and manage increasing energy demand. Undoubtedly, smart grid is more intelligent, versatile, decentralized, secure, resilient and controllable than conventional grid. However, to reap the benefits of smart grid systems, utilities  need to make major changes in their infrastructure and revolutionize the manner in which business is conducted.

Off grid Photovoltaic shelter for streetlightning

Today, some part of the Indians have no access to electricity. During night the  life comes to a halt, affecting  productivity, employment and quality of life.

Off-grid LED street lighting solution has been designed for areas with limited or no access to electricity and use a completely sustainable system.

When the sun shines during the day, the solar panels convert solar energy to electrical energy and stores it in the solar-powered battery. During the night, the battery is discharged, releasing electrical energy to power the LED lamps and lighting hence the road.

The primary advantage of LED street lighting is energy efficiency; in fact the LED technology ensure low power consumption, long lifetime, more accurate color rendering and fewer electrical losses.

Furthermore, by installing efficient and bright street lighting the quality of life get better; the local authorities can improve road safety for drivers and pedestrians, create more attractive conditions for businesses and commercial life  and generates  a  more secure life

Customers respond to fluctuating energy prices in real time

With customers resond to fluctuating prices In real time to encourage the demand side response may reduce the need for investment in grid reinforcement and infrastructure.

pilot rojects are underway by a sample group of households which are installed a smart addition to their electricity meters that collects, analyses and redistributes usage data minute-by-minute. At the same time, the system displays the cost of the electricity, allowing consumers to control their consumption in response to changing prices and target spend thresholds that they have set themselves.

cloud-based information management service, provided by energy management system which willreceive, store, and process consumption data and then present it to customers via browser, tablet or smart phone. It also alerts customers via email or sms when they are approaching their target spend, allowing them to change their behaviour accordingly.

The goal is to see how customers react to energy prices in real time and how they relate to the spend targets that they have set for themselves

Aim of pilot is to create tariffs that contribute to more consistent electricity consumption. Customers will be encouraged to save money by using less electricity during peak periods in the morning and afternoon, which will in turn reduce the load on the grid.

“There is a lot of interest in dynamic pricing and the contribution it can make to demand side response solutions. achieving both simplification and time-of-use tarrifs so that all stakeholders share the benefits. 

Trends in Utilities/Smart Grid and its impact

A mix of smart technologies, customer engagement, and demand response will help bring electricity production and consumption into the precise alignment that the grid requires to function properly. While innovative energy storage approaches may play a future role in managing this exacting dance between power supply and demand, other more proven and more cost-effective options will be required in the near term.

The impressive ability of demand response (DR) to reliably stabilize electric systems under pressure has been on full display in the past year: DR helped keep the lights on during hours of record-breaking summer power demand in New York last July, and also during hours of record-breaking winter power demand in Texas just this month.

It appears that nimble DR mechanisms (e.g. dynamic pricing and real-time customer engagement) will become increasingly valuable assets for utilities as a low-cost strategy to manage not just weather-driven peaks, but also the day-to-day patterns associated with a cleaner and smarter electric grid.

1. Energy efficiency policies worldwide.

More than half of US states have now officially enacted quantitative energy efficiency targets, and around 30 states offer concrete incentives to utilities that drive reductions in energy demand. Yet even more states have instituted a framework for severing the tie between utility energy sales and revenue, thereby removing the disincentive for utilities to help electric and gas customers lower their bills. Mississippi and Louisiana are the latest players to join the energy efficiency policy landscape.

Across the pond, European member states recently formalized their action plans to achieve an EU-wide 20 percent reduction in energy consumption by 2020, as part of a sweeping Energy Efficiency Directive.

And in Asia — where it’s forecasted that more than half of annual global energy consumption will be consumed with a few decades by 2035 — several countries are becoming more aggressive with efficiency policies. Japan, Singapore, China, and many developing nations in the region are finding efficiency to be one of thecheapest and cleanest energy resources at their disposal.

2. Natural gas and renewable energy keep chipping away at coal

America’s energy portfolio is changing. Natural gas — along with clean power — is persisting in chipping away at coal’s segment of the US energy generation mix.

Much of this shift is due to the expansion of oil and natural gas production here at home. Domestic natural gas production is projected to grow 56 percent between 2012 and 2040. And by 2040 — if not earlier — natural gas will displace coal as the primary fuel for US electricity generation. The shift is already under way: in November, the Tennessee Valley Authority — located in one of the top coal-burning states — announced its plans to shutter eight coal plants representing 3,300 megawatts of capacity.

At the same time, the share of renewable energy in the US generation mix continues to grow rapidly.

3. Innovative utilities are exploring ways to thrive in a distributed-generation world

How should a bakery respond when, each year, more and more of its customers want to start baking their own cookies?

Electric utilities will confront a similar situation in 2014, as tens of thousands of additional homes and businesses will start buying less electricity from traditional retailers, instead opting to produce power from their own solar panels. Rooftop solar installations have reached a furious pace in the US: a new system is now brought online every four minutes. And all other things equal, an increase in behind-the-meter distributed generation (DG) means a decrease in sales and revenue for utilities.

This DG-driven revenue curtailment could produce a frightening cycle for the power industry: reduced sales revenues could lead to less system-wide investment, which could lead to a less cost-effective electric grid, which could in turn lead to an increase in rates for consumers, and that could drive more high-value consumers opting to produce their own power . . . which could all lead to further reduced sales revenue for utilities. You get the picture. Rinse and repeat, until the days of a centralized utility give way to a distributed generation world.

The challenge for utilities in the coming year and beyond will hinge on ensuring that they can constructively participate in this trend, rather than sit on the sidelines. An innovative pack of utilities are already seizing upon such opportunities, which include utilities’ leasing solar panels to ratepayers and creating subsidiaries that install rooftop solar outside their regulated service territory.

In parallel to finding an optimal role in distributed generation, utilities are naturally suited to further unlock the potential of large-scale solar. It still accounts for the majority of installed solar electric capacity in the US, and it is set to take off in a big way in the next few years.

4. A critical mass of smart meter infrastructure is paving the way for dynamic pricing programs

In the last 6 years, the number of smart meters in the US has grown more than sixfold. There are now more than 46 million smart meters installed nationwide — enabling real-time communication of energy data between customers and their service providers.

This trend isn’t about to slow down. Worldwide, the installed base of smart meters will triple from 313 million in 2013 to nearly 1.1 billion within ten years, according to a November report by Navigant Research.

But while smart meter deployments are becoming widespread, the use of dynamic pricing — which better matches energy supply and demand through real-time price changes — is not as prevalent. However, some utilities are emerging as leaders in applying dynamic pricing to better engage their customers and ensure system reliability — just as the concept has picked up steam in the transportation sector.

Programs like Pacific Gas and Electric’s SmartRate and Baltimore Gas and Electric’s Smart Energy Rewards are at the forefront of the utility industry’s adoption of dynamic pricing mechanisms. Their focus is on using time-varying energy prices to keep a grid-friendly balance between electricity supply and electricity demand, and on designing easy-to-understand rates and rebates that help customers manage their consumption in a personalized and energy efficient way.

5. Demand response will aid the grid’s transition toward renewable energy supply

Fundamental changes in the electric grid’s supply and demand profile are requiring utilities to think creatively about how to manage this transition.

On the supply side, deep investments in utility-scale renewables like solar and wind are bringing into focus the intermittency of these sources. It’s no secret that solar electricity production grinds to a halt in the evening, and that wind speeds often pick up after electricity consumers have gone to sleep. And on the demand side, the rise in electric vehicles — the most energy-intensive appliances in the history of the home — could put substantial pressure on the grid at certain times of day.