Machine Learning and IoT Data for the Energy Sector

on August 04, 2020


Lack of Human Capital/Skills Gap:


The energy industry has a high and continuously increasing demand for data-driven talent. In light of recent technological advances and competitive market changes, the overall industry is transforming and using analytics to provide an increasingly high-tech digital experience for end users. Case studies of utility companies suggest that employing analytics not only can increase profits by as much as 10 percent, but also can increase customer satisfaction and improve employee health and safety. Given the hundreds of new and traditional applications of analytics in the energy industry, companies need a focused approach to determine which applications will assist in solving company challenges while having a direct or indirect positive impact on company profitability. Rather than focusing solely on the latest tech trends, companies should also pay close attention to all the value-producing functions of data analytics — including more traditional applications from asset maintenance to transportation to marketing.


Data analytics for energy sector


Data-driven talent in the energy sector is necessary to implement and maintain these data-based innovations. While hiring technology experts is an option, it is often an expensive and time-consuming one, and providing current employees with data analytics training is often a cost-effective strategy. Data-driven skills at a firm can be developed in a period of weeks by focusing on relevant energy-related data skills rather than all the theoretical, non-practical aspects of data science that are usually taught at universities. Additionally, teaching these skills can be more cost-effective than relying on outside analytics companies. Once a firm has the talent to implement technologies using data analytics, it will gain a competitive advantage in this rapidly-changing sector.


Also Read:Hiring Skilled Employees VS Training Employees Internally


Data-Driven Upstream Benefits:


One of the key bottom-line, operational benefits of employees trained in data analytics is the ability to closely monitor machinery performance. With basic machine learning models, energy firms can easily identify systematic breakdowns of machinery through IoT data (data transferred between machines and computers). Utility firms can then perform maintenance that can prevent machinery breakdowns and avoid significant losses in productivity. Because the data-driven firms can prevent issues before they arise, they can surpass their less technological counterparts.


Data monitoring and data analysis


Furthermore, the use of IoT data can be used to monitor a machinery bottleneck. If one machine in a process functions slowly, a data analytics platform, such as Power BI, can be used to monitor the machine in real time and make sure it is operating at peak levels. Analytics can also easily determine whether investing in another machine to alleviate some of the work on the original bottleneck would be beneficial. If new machinery is acquired resolving the bottleneck, real-time data will facilitate identifying the next bottleneck using clear visualizations. PowerBI can easily perform these functions in an expeditious manner, giving executives and workers the ability to focus on other pressing aspects of the business.


Data-Driven Midstream Benefits:


A midstream firm can also enhance its bottom-line performance by using data to make the transitions between different parts of the transportation process more efficient, such as the transition between pipelines and vehicles. With data skills, a midstream firm can easily identify the slowest stages within their transportation process and determine how to increase the efficiency of those stages. For example, it can minimize vehicle waiting times at pipelines by utilizing data to figure out the best arrival times and to create a schedule. If many vehicles are arriving at the pipelines at the same time, then with simple data visualizations, it will be easy to identify and select other times for vehicles to arrive. Fixing such scheduling problems can ease transitions in the process and often decrease overtime required by workers.


Data visualisation


Furthermore, a midstream firm can use historical data to predict times when the demand for energy decreases or surges. Firms can then prepare in advance to alleviate costs when possible. These predictions can come in the form of a number or a visual through Power BI, allowing the viewer to see the temporal trends. Through this technology, firms can easily identify changes in demand and prepare strategies in response to upcoming shifts. With data to illuminate issues and possible solutions, firms with data analytics talent are better situated to identify potential operational failures.


Data-Driven Downstream Benefits:


Customer analytics


As the general population has become more tech-focused, utility customers are showing an increasing expectation of more control over the purchase and use of utilities. For example, some electric customers are desiring a choice of electric sources, rate plans, and other innovations. Downstream energy firms must adapt and focus on attracting and maintaining these customers. Through customer analytics, a firm could discern which energy applications are in demand or which marketing strategies have resulted in customer engagement and success. Once they have identified successful strategies, they can put more resources into successful customer engagement and retention practices. Firms without successful data analytics capabilities may not be able to analyze the data and may adopt business strategies that are not as efficient.


Conclusion:


Develop data science mindset


IoT data, basic machine learning, and visualizations will be pivotal in becoming a leader in the energy industry. Furthermore, due to the significant demand changes seen recently in these industries, such as how COVID-19 caused oil demand to plummet or how solar panels are becoming more and more popular, it is more important than ever to cut costs and maximize efficiency. With organized data that can generate real-time visualization and predictions, energy firms can be ready and prepared to address changes in the market as well as internal processes. Xccelerate can help companies train their employees for these data-driven insights. Furthermore, the benefits listed are just the tip of the iceberg — with the right skills, almost any inefficiency in the energy industry can be addressed.


Also Read:All You Need To Know About RPA & Why You Should Learn It


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