How Big Data Can Shape the Future of Energy for Developing Countries


Big data continues to create a lot of buzz across many different industries, from healthcare to manufacturing. For the utilities sector, it has opened new doors for products and capabilities that can address some of the most important and unsolved energy challenges, including how and where energy is being used. With the possibility of saving millions of dollars, while also making energy more accessible and reliable for more people, companies are turning to big data to improve decision-making.

This concept isn’t new – in fact, 70 percent of IT decision-makers consider their organization’s ability to extract intelligence from big data critical to their future success. Once utilities learn how to use big data properly, benefits will begin to flow, including predictive cost management, fraud detection, customer experience quality or service operations efficiency.

Understanding big data’s impact on utilities in developing countries

In developed countries such as the U.S., there is an opportunity to use this data to revamp existing programs and modernize infrastructures. Many companies in the energy market are working hard to figure out how to best execute these types of programs. Yet, the potential benefits of big data for utilities in developing countries are even larger and more widespread.

One of the largest undeveloped regions in the world is Africa, which has around 22 million of its people living without electricity. The majority of those that do have it are using biomass for cooking – oftentimes a fatal practice. Regions like these are looking for modern solutions to power their communities – both for electricity purposes and to support a better quality of life for residents.

Regions like these also suffer from frequent power outages, which put a constant stress on the community. Harnessing big data enables real-time awareness, which can address outages directly. For example, if smart insights can pinpoint where power seems to be most vulnerable, that utility can spend less time and resources – of which are likely manual and extremely limited – looking for the area that needs to be corrected. Furthermore, as developing countries continue transitioning many rural communities to peri-urban environments, having the tools to correctly analyze and use energy usage data allows utilities to create a broader functioning grid that can expedite the entire country’s development and success.

Another region primed for benefiting from big data is the Caribbean. Utilities in countries like Puerto Rico are struggling to manage debilitating issues that range from providing residents with consistent access to energy, as well as preventing energy theft, an issue Katherine Tweed has also explored on Greentech Media.

Challenges with analytic techniques for established power grids

One of the greatest barriers these utilities will face in developing modern infrastructures is effectively managing and determining which data is useful. A modern electric utility receives an avalanche of data from metering, monitoring, and instrumentation systems that can overwhelm the utility’s ability to process and understand that data. Each of these data sets can provide valuable insight into how well equipment is operating, how to utilize the equipment most efficiently, how to react to equipment failures or natural catastrophes, etc. Sophisticated analytic techniques have been developed that can help utilities understand more about what is happening within each particular system. However, the isolation between these systems makes it difficult to combine data and recognize more complex relationships.  Thus, it is challenging to achieve an overall understanding of a utility’s complete operations.

One problem is that moving data from one system to another, using conventional methods, is difficult and expensive to implement. Keep in mind that this has proven to be challenging for modern utilities in developed countries – imagine a cash-strapped government and utility trying to integrate these solutions. In addition, differences in data formats and measurements make combining data from different systems challenging. Modern IT architectures such as Enterprise Service Bus (ESB) designs provide a partial answer, but bring considerable complexity of their own. This is an impediment to analytic methods that depend on a combination of data sources. This barrier appears both as an increased cost in operation, but also as an increased difficulty in the general exploration of methods and techniques. Simply put, some analytics never get explored because they are too hard to try.

Applying the cloud as a universal solution

One of the most promising solutions is the cloud. The cloud is feasible to developing regions because of recent developments in the delivery of sophisticated SaaS platforms, making it affordable and simple enough, technically speaking, to integrate. By using Hadoop technology, a free, Java-based programming framework that supports the processing of large data sets in a distributed computing environment, data analysis tasks can be spread over many separate processors to deliver a tool that is quick and easy to use. Cloud data storage can be scaled to suit the volume of data and the expected lifetime of the data, so the utility doesn’t need to worry about having to update in a few years.

Users of analytic tools throughout the utility can simultaneously access shared datasets and analytic methods, either in open-ended analytic exploration or as production analysis methods.  The cloud also enables advanced data visualization to be delivered, supporting a deeper understanding of the data relationships from system to system within an electric utility. This is especially important in rural areas where complex transportation logistics lend to systems with easily visualized geographical data representation.

Lastly, a key factor in delivering maximum value from an analytic tool for developing regions is the ease of use. Modern software designs can provide a clean, easy-to-understand presentation of complex data sets and relationships. This makes possible the use of analytic tools by occasional users, which is critical for developing regions due to manpower limitations. Unlike in the U.S. or other areas, local utilities in developing areas must rely upon a smaller pool of technically skilled employees.

Of critical note is the capability in many developing regions to support cloud-based solutions. Many of these regions have developed baseline cellular infrastructures that allow them to communicate via mobile phones. For example, Kenyan farmers have adopted the M-PESA phone credit system into their workflow to provide transparency on average crop price, trade goods, and more. As such, these regions benefit from having simple, basic infrastructures that provide a rudimentary starting point for establishing cloud based data architectures and will ultimately aid in the deployment of Big Data networks.

Data analytics have the potential to shape the future of energy everywhere…if uncovered correctly

Big data is an essential piece in modernizing the systems of developed and developed regions. However, we believe the benefits for each type of region are different. A more efficient system in developing regions has the potential to improve daily life for inhabitants. The benefits are much more far-reaching, and thus, technology companies should be aggressive about applying their understanding of the cloud and big data to contribute to the growth in this market. The availability of modern data analytics tools will usher in a new era for both developed and developing regions, and allow them to fully embrace the data capabilities of future smart grid developments.

By Scott Foster, CEO of Delta Energy & Communications


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