Big Data Analytics - Making the Energy Sector Smart

The energy sector is the driving force behind our economies. Every establishment relies on the energy industry to function without interruption. Data has always been a crucial part of energy and utilities’ operational processes. However, with the institution of new data feeds and the ensuing increase in the volume of data produced, big data analytics is assuming priority.

Energy and utilities are in charge of accomplishing the power and energy demands of other establishments; they are the lifelines for every other organization. Hence, capitalizing on big data and smart analytics to improve service efficiency is absolutely mandatory.

Today’s energy consumption has reached an all-time high. In response to this increasing demand for energy, the energy industry is looking to develop innovative methods to optimize electricity usage as well as determine potential alternatives for energy generation, and big data analytics is playing a crucial role in this process.

The following are some of the primary roles of big data analytics in the energy sector:

Power Outage Monitoring

A power outage could completely bring your business to a standstill for hours or even days. They can happen due to several unexpected events and leave thousands of people without power. To tackle such outages, the energy and utility industries are building smarter infrastructure and sensors to improve predictability.

Advanced power outage systems apply real-time solutions that function based on live data and smart algorithms to predict and stop any such possible occurrences.

These systems can predict the effect of any near-term asset values on the network grid, including likely outages caused by smart meter events, region-specific outages, and more. This helps energy companies take the necessary measures to keep their energy flow in check and warn people of potential blackouts.

Enhancing Theft Detection and Smart Grid Security

With increasing energy demand, it’s no wonder that some individuals and even establishments may turn to illegitimate means to get electricity. In reality, energy theft has become a nationwide issue for energy companies. Each year, energy companies lose huge amounts due to energy theft.

Nowadays, energy companies are using data science to stop energy theft. Many companies are utilizing advanced metering infrastructures to monitor energy consumption, which enables them to detect energy flows and identify irregularities.

By tracking the behavior of users and comparing it with past occurrences of energy theft, energy companies can detect potential culprits trying to steal energy from energy grids and take appropriate steps to prevent such occurrences.

Power Load Management

To effectively manage energy loads, energy companies need an efficient demand response strategy to balance energy demand with maximum power supply in a given time period.

Having a smart load management system allows energy companies to monitor the metrics of energy usage and adjust the energy supply to meet the demand.

All of the components within the management system produce data. By implementing big data analytics, energy companies can make precise decisions regarding their power planning and generation, energy load, and performance estimation.

Enhancing Customer Experience

As with other sectors, energy companies rely on their customers to make a profit. For every energy company, the demands and requirements of its customers are a priority.

Energy providers can obtain valuable information from their customers concerning their behaviour and energy consumption patterns, which can then be used to customize services and make recommendations for their customers.

5 V’s Of Big Data

In the year 2001, the analytics firm presented the 3Vs of 3D Data, which are volume, velocity, and variety. Over a certain period of time, data analytics as a field saw a rampant transformation in how data is generated and processed. In the course of this advancement, data increased so quickly in size that it came to be termed "big data." With the massive advancement of data, the two latest Vs "Value and Veracity" have been added to the data processing theory.

Velocity: Velocity refers to the speed at which the data is generated, compiled, and assessed. Data constantly flows through several channels, such as computer systems, networks, social media, mobile phones, etc.

Volume: Big data volume describes the amount of data that is generated. The value of data is also dependent on the size of the data that is generated. The value of data is also dependent on its size. If the volume of data is enormous, then it is termed "Big Data." This implies that whether a particular set of data can actually be considered “Big Data” or not is dependent upon its volume.

Variety: “Big data” is categorized as structured, semi-structured, and unstructured data.

Structured data: This is primarily organized data whose format, length, and volume are clearly defined.

Semi-Structured Data: This data is primarily semi-organized. It is usually a form of data that does not conform to the formal structure of data.

Unstructured Data: This data is unorganized and doesn’t conform to the traditional data formats. Data generated via digital and social media (texts, pictures, videos, tweets, etc.) can be categorized as unstructured data.

Veracity: The Veracity of Big Data is an assurance of the quality or credibility of the collected data. Therefore, when processing big data sets, it is important that the validity of the data be checked before proceeding with processing. For example, data in bulk could cause confusion, while a smaller amount of data could convey incomplete information.

Value: Although data is being generated in large volumes, just collecting it is of no use. Instead, data from which business insights are acquired adds value to the organization. Data by itself is of no use, but it has to be transformed into something valuable to extract information.

Contemporary Data Science Has Modified the Energy Sector Permanently

As data analytics have always been present in the energy sector, energy providers have made considerable progress using static models and algorithms for data analytics.

With the latest real-time data analytics solutions, the data science applications for the energy sector are almost infinite.