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Data Analytics and Its Decisive Role in the Energy and Utilities sector

Data has become the new currency across the globe. The analysis of the accumulated data, be it any sector, is a tedious process, and once done right can be beneficial for the growth of the manufacturer, and subsequently, the market involved. The method of assessing data for inspection, transformation, so as to arrive at useful insights through the aid of a specialized system and software, is known as data analytics. It entails multiple facets and approaches, and encompasses diverse techniques, playing a crucial role in making logical decisions, along with aiding in the efficient operation of businesses. It also helps in augmenting business revenues and improving customer service efforts.

Data Analytics in Energy and Utilities
The anticipated growth prospects and the increasing demands in the energy and utilities sector present the challenges of energy management, consumption, environmental impacts, and application of renewable energy sources. Data analytics can help utilities in curbing these challenges by providing solutions to consumer energy consumption habits. They aid in forecasting the energy consumption and use the piling data for efficient operations and energy demand management, along with ensuring accuracy in the information provided pertaining to energy usage and power outages. They further identify data quality requirements in key areas like product, tariff, industry flows, customer, etc. along with exploring opportunities for revenue optimization of the enterprise. One of the primary benefits of the data analytics in the energy and utilities sector is the estimations and billing routines it ensures, accounting for an accurate billing process for the consumers.

The Mechanism of the Data Analytics
The utilities are struggling to meet the escalating demands for sustainable, affordable and safer services, for which they require optimized operation and business practices. The data analytics and modern analytics solutions are said to be of help in achieving such standards within a short span. The segmentation given below includes workings, the frame of reference, application and the benefits of data analytics in the energy and utilities sector.

  1. Meter Operation
    The utilities sector has undergone a significant transition, with regard to meter operation, from meter readings taking place once a month to every fifteen minutes. The phenomenal transformation is credited to technological advancements and smart revolution. Consequently, a massive chunk of data is generated that contains all the aspects of the consumer and their consumption pattern. The data analytics leverages integrated meter reading, customer billing data, and meter inventory, and thus enhances the revenue management of the company or enterprise. The data that also includes interval readings, register readings, meter-related problems, data quality information, etc. are analyzed through analytics for a clear perception of customer behavior. The analysis would help an enterprise or a company to develop better insights for new rate plans and services for the consumer.
  2. Load Forecasting
    Load forecasting plays a vital role in maintaining the stability and security of the power grid operation, thereby resulting in reduced generation costs. Load forecasting has always been the go-to planning approach of power systems, since it is impactful in making decisions, whether it’s automatic generating control, energy commercialization, or economic dispatching. The accurate forecasting can have a considerable impact on the power system generator both in economic and reasonable terms. The development of smart grid technology and the advanced metering infrastructure (AMI) aids in generating large amounts of data by the monitoring systems. The conventional forecasts consisted of considerable errors, since they were based on historical data. The risks associated were staggering, and with a comprehensive approach, data analytics can address the rising complexities of energy generation and distribution, concerning load forecasting.
  3. Demand Response
    The supply and demand of electricity need to be met in order to achieve a balanced system. Predictive analytics aids in the precise forecasting of such demands. The advanced metering infrastructure (AMI) has helped the utilities to understand the electricity usage and bi-directional communication between the utilities and consumers. The availability of high-dimensional electricity consumption data not only offers opportunities for the developing forecasting models, but also has resulted in a vast repository of data. The data analytics can further leverage these data for balancing supply and demand. It can also help the energy and utilities providers in activating targeted and location-aware demand response, thereby instructing the consumers to reduce power usage during high electricity demands.
  4. Distribution Planning
    Power outage due to bad weather is one of the toughest challenges faced by any utility company. The conventional distribution planning was static based, and worst-case analyses of the system conditions. The advanced data analytics can aid the utilities in identifying the service breakpoints in the distribution system. The data analytics of the real-time and near-real-tie data can locate and identify glitches and send alerts, thereby helping the system operators in taking immediate remedial measures. It also helps the utilities in regulating the impact of distributed energy resources and in better planning of its grid operations. The data analytics help the utilities in identifying the underutilized assets, thereby improving the return on asset loading continuously.

Inference
The energy sector is projected to attract lucrative growth aspects and continue to do so for a good number of years, all the while anticipating innovations and technological advancements. There is an increasing demand for energy across the globe. The rising population and economic development are the reasons attributed to these rising demands. The findings put forth by the International Energy Agency (IEA) estimates the energy needs to propel by 55% between 2005 and 2030. The data analytics can thus improve customer interactions as well as develop new incentives and pricing plans. The utilities can better target the consumers using highly-targeted customer information and the analytics-based interpretation of customer data, along with effective sales, educational programs and marketing.

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Author: Akhil Nair

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