India faced one of the major blackouts in July 2012. The entire northern half of the country was devoid of electricity for two full days. More than half a decade later, where India now ranks as the sixth largest economy and third largest producer of energy after US and China, things still haven’t changed much. Energy infrastructure is still vulnerable and a major portion of the energy produced by systems is either lost in transmission or stolen.

The above mentioned problem is common to the Energy and Utilities Industry, but thankfully in the age of Big Data and Analytics, solutions exist that would not only counter the loss of revenue, but would also leverage optimal consumption of the underlying assets of the Energy and Utilities Industry.

The lifecycle of energy generation starts from the power plant, where energy is supplied from primarily thermal, hydro, wind, and solar power plants. Assuming a power plant is supplied with 100MW of energy, which is to be transferred to cities for consumption. A step-up transformer elevates the voltage and transmits the energy using transmission lines. These transmission lines run throughout the length and breadth of the country, through hundreds of kilometers. Substations receive this transmitted energy and use a step-down transformer for distribution and end-user consumption.

In the above high-level scenario, data generated per minute by power plants, transformers, transmission lines, substations, distribution and consumption units would be of phenomenal volume. It’s overwhelming to imagine that power supply to a small area of a small city in the country generates petabyte-level data; leave alone the data generated for power supply to an entire state or for that matter, the entire nation. Governments and organizations need to understand the importance of this phenomenal volume of data and how Big Data and analytics need to be leveraged to be fed into analytical algorithms to:

• Monitor vast volumes of real-time data to find the generation and consumption patterns.
• Predict shortage in energy supply and future demand by studying those patterns.
• Help optimize the energy supply and demand cycle.
• Estimate if there is surplus energy being produced in some area and optimally redistribute the same to an area of energy shortage.

Big Data Answers “How” and “Why”

Imagine, collecting energy consumption data for one of the cities in India. By validating it against the power supplied to the legitimate number of meters in that area, we can determine the amount of energy being stolen and lost through illegal connections. We can prepare the data for a set of cities in the country and determine a pattern of “how” and “why” the energy is being lost across the nation.

Big Data Answers “Where”

In line with Green India initiatives, India has cut down immensely on thermal energy production, which primarily uses coal as their primary source and is investing heavily on solar energy production. Smart meters can be installed on various location of the country to capture data points, like the intensity of sun’s rays for that particular area, wind speed, and gauge the intensity of various natural resources, like tidal waves based on demographics. Data captured from such smart meters can be subjected to high-end analytics to answer “where” production units to generate power from solar, tidal, and wind sources could be placed to generate the optimal amount of energy. This would be a big leap in cutting down emissions and relying more on natural sources to produce energy.

In the 21st Century, Big Data and Analytics will set the path to intelligent production, distribution and consumption of Energy and Utilities.

Hopefully, organizations and governments will soon realize the importance of Big Data and analytics to address these core issues of the energy infrastructure and utilities sector and would implement the same. In the 21^st century, Big Data and analytics is all set to show mankind the path to intelligent production, distribution, and consumption of energy and utility.