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  • Writer's pictureAnantaya Pornwichianwong

5 Data Analytics Applications in the Energy Industry



Data analytics is another indispensable power that drives the growth of energy industries in the rapidly-changing world while generating more profits at the same time.


Many other industries have succeeded in implementing and driving more profits using data analytics, for example, retail giant Amazon and the most successful streaming platform Netflix. The energy sector also significantly benefits from implementing AI as well.


With the ever-growing market with more competitors, the coming of renewable and alternative energy, and changing consumer needs, the energy industry has to work hard to cope with these changes in the new era.


Data analytics is therefore an essential tool to help the energy industry successfully transition through all the changes. There are already some data analytics applications in the energy industry, and new data-driven energy services are coming out to meet the needs of modern consumer demands.


Sertis invites all readers to take a look at 5 examples of data analytics in the energy industry that pave the way to a better future.


1. Analyze electricity usage data


We can apply data analytics to the energy industry by collecting customer electricity usage data such as electricity bills and meter data from households. Then, we can analyze and better understand the customer's behaviors in using electricity. We can also collect situational data to analyze market volatility and monitor other external factors in order to deal with unexpected changes.


With this approach, service providers will be able to design and adjust their operational strategies in accordance with their needs and changes in resource allocation and marketing planning to reduce the costs of errors. For example, we may offer tailored packages with usages and prices that are suitable for each type of customer in a personalized way to attract more consumers and match the current needs in the market.


2. Perform predictive maintenance


To maintain the electrical grid system and ensure efficient operation at all times, we can use data analytics to perform predictive maintenance using the data obtained from the performance tracking of various machines and systems. Data analytics can constantly predict which parts of the systems or machines require maintenance or repair and when, and arrange repairs on time to prevent damages that may interrupt the operations.


For example, an energy service provider revealed that using data analytics technology for predictive maintenance saved its maintenance and repair costs by more than 130 million USD.



3. Supervise the network operation


Real-time data analytics allow us to see the performance of our electrical grid system and constantly monitor it to ensure that the power grid is operating at its full output. Also, when there is a problem or a malfunction in the system, the system will be able to detect and fix it immediately.


Furthermore, the system will be able to assess various failure risk factors such as transformer overheating, dramatic electricity usage spikes, or a tree falling on an electric pole. Assessing and monitoring these factors will prevent interruption of the supply chain system or outages that cause an impact on consumers.


4. Improve DERs


DERs (Distributed energy resources) are small-scale electricity supplies for the electrical grid. It feeds the electricity into the electrical loads within the grid. DERs are small-scall supplies that are interconnected throughout the grid. What is necessary for DERs to function effectively is to ensure that it is well-integrated into the broader grid.


Data analytics is indispensable for implementing effective and well-functioned DERs, as it helps connect DERs to the network. Data analytics allows us to track the performance of DERs at any time, and simultaneously monitor the overall grid performance, DERs, and the real-time performance of the whole system. Data analytics also allow us to monitor the demand and supply of electricity, measure the electricity supplied by each DERs to the grid, and calculate the electric load at any time.


Implementing data analytics will help ensure that the entire network and all subsystems work efficiently while also providing us with the data to support decision-making and unexpected problem-solving. This increases the grid's stability and saves more cost of errors.


5. Smart Energy Platform


At Sertis, we have continually developed data analytics technology for the energy industry. We collaborated with our partners to develop a smart energy platform, a digitalized energy management system for solar power trading. Its main goal is to facilitate energy trading between producers and users. We leverage data analytics applications and AI technology to forecast the demand for electricity and production capacity from all manufacturers. So, we are able to match buyers and sellers with more accuracy and analyze operational data to maintain optimum performance.


The smart energy platform from Sertis also utilizes Blockchain technology to make energy trading management more convenient by eliminating the verification process. At the same time, Blockchain enables us to maximize transparency and reliability and maintain the platform's best performance. We also further aim to promote the use of alternative energy as an important resource for a better future.


Learn more about the smart energy platform from Sertis at: https://www.sertiscorp.com/smart-energy-platform


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