Top 10 Data Science Use Cases in Energy and Utilities
The energy sector is under constant development, and more of significant inventions and innovations are yet to come. The energy use has always been involved in other industries like agriculture, manufacturing, transportation, and many others. Thus these industries tend to enlarge the amount of energy they consume every day. Energy seems to be very demanding in terms of new technologies application and development of new energy sources.
The rapid development of the energy sector and utilities directly influences social development. People are now facing challenges of smart energy management and consuming, application of renewable energy sources and environmental protection. Smart technologies play a crucial role in the resolution on these matters. In this article, we will consider the most vivid data science use cases in the industry of energy and utilities.
Failure probability modeling
Failure probability modeling has won its place in the energy industry. The efficiency of the machine learning algorithms in the failure prediction is undoubtful.
Active application of probability modeling helps to increase performance, predict occasional failures in the functioning and as a result to reduce maintenance costs. The energy companies invest vast amounts of money into maintenance and proper functioning of their machines and devices. Unexpected failures in their operations result in considerable financial losses. Moreover, for people who rely on these companies as their energy source the situation gets critical. As a result, general reliability and image of the energy provider may suffer.
The output of the failure probability model application is an essential part of the decision making process for the companies. It gives a marvelous opportunity to be one step ahead for the company management.
Outage detection and prediction
Despite the efforts made by the companies belonging to the energy industry, the power outage still takes place, leaving a considerable number of people without power. In this respect, people tend to regard the blackouts as a failure of the electric grids. However, the blackout is a preventive measure, a result of the automatic protection system operation.
In previous years, the energy systems engineers used static algorithms and models rather than real-time solutions. Nowadays, numerous companies dealing with energy and utilities are actively upgrading their systems to improve outage detection and prediction. Modern smart power outage communication systems are capable of:
predicting the influence of weather conditions on the power grid
predicting the impact of the near-term asset values on the power grid
detecting possible outages by smart meter events
detecting outages in the specified areas
real-time filtering of outage inputs and recognition of the outage type
confirmation of the outage and communicating on this matter.
Outage detection and prediction starts with the identification of the right metrics and the threshold value for it. Every single outage event should be carefully analyzed to identify the root cause. Only after that, predictive algorithms may be applied to model the future likelihood of an outage. The application of the smart energy outage ecosystems allows providing accurate real-time outage statuses to improve general customer experience and satisfaction.
Dynamic energy management
Dynamic energy management systems belong to the innovative approach to managing the load. This type of management covers all the conventional energy management principles concerning demand, distributed energy sources, and demand-side management along with modern energy challenges like energy saving, temporary load, and demand reduction. Therefore, smart energy management systems have developed abilities to combine smart end-use devices, distributed energy resources, and advanced control and communication.
Big data analytics plays a leading part here as it empowers dynamic management systems in Smart Grids. This largely contributes to the optimization of the energy flows between the providers and consumers. The efficiency of the energy management system, in its turn, depends on the load forecasting and renewable energy sources.
Dynamic energy management component usually comprises the smart energy end-use devices, smartly distributed energy sources, advanced control systems, and integrated communication architecture.
Dynamic energy management systems process vast quantities of data attained by practical methods and solutions. Applying big data analytics to this data helps to make performance estimation and provide smart recommendations for energy management.
Smart Grid security and theft detection
Energy theft may be regarded as one of the most expensive types of theft. Therefore, energy companies make great efforts to prevent it. Energy theft with smart grids often happens via a direct tap into the distribution cable.
To predict and prevent energy theft and as a result money loss, big energy companies and corporations monitor energy flows to react immediately to some suspicious matters. With this purpose, the companies owners tend to shift to Advanced Metering Infrastructures, which are capable of reporting on the energy use instances and remote controlling.
Smart Grid security solutions are gaining extreme popularity. These solutions may be behavior-based, thus they constantly track the users’ behavior to detect hackers and disclose their intended actions.
Preventive equipment maintenance
Preventive equipment maintenance relies on the monitoring of the current equipment condition and performance level under normal operating conditions. This monitoring is called upon to prevent equipment failure by predicting possible failure occurrence on the basis of specific metrics.
To get the maximum return on investments and to use complex machines and equipment at the peak of their efficiency, the companies dealing with energy distribution and utilities have been applying preventive equipment maintenance for decades. Smart data solutions, sensors, and trackers are used to collect the defined metrics, process and analyze the data. On the basis of the output, the smart systems alert the energy outage, the poor functioning of the mechanisms and urge people to take right and immediate decisions.
Virtual Machines for data science
Demand response management
Under conditions of a constant search for renewable energy sources and the need to use energy efficiently, smart energy management is at the peak of its popularity. A key to successful energy management lays in the balance between demand and supply. Both high and low demand rates cause a lot of problems and costs for both energy providers and consumers.
Therefore, demand response is a strategy that has proved its efficiency over time. Specific real-time management applications and solutions allow monitoring metrics of energy use, define the activity pick and adjust the energy flow to the current demand rate. Moreover, there exist response management programs encouraging consumers to use energy at a specific time and save money. Thus, consumers get a chance to shift to a better pricing program and providers get an opportunity to achieve the desired balance in energy provision.
Real-time customer billing
There is nothing strange in the companies desire to improve their customer service and increase the customers’ satisfaction rates. Energy and utility companies do not lack behind the others. They strive to bring visibility into the service provision process, billing and payment operations, improve quality and eliminate delays, misunderstanding or disputable issues. Companies use a whole bunch of applications and software to manage numerous customers, billing, payment, invoicing. Customers, in their turn, have an opportunity to monitor the transaction as well.
The operational management software tracks operational activity and transactions in real-time and takes immediate actions in terms of billing, payment, prepaid and postpaid services, and communication services.
Improving operational efficiency
Efficiency by its very meaning presupposes completion of particular tasks in a shorter period of time than before. The fast pace of modern life and daily matters makes people desire efficiency in everything.
Energy and utility companies use smart data application and software to detect the matters, operations and functions worth of optimization. Real-time monitoring provides data concerning time, activity rate, state of some operations. The data is processed in combination with the external factors to define the average efficiency. Data science here is used for modeling of various situations and prediction of possible efficiency rates under various circumstances.
Optimizing asset performance
All possible failures or delays in energy supply, unplanned service interruption or complications result in inefficiency. This inefficiency may be prevented or at least taken under control by monitoring of performance and assets.
Real-time data concerning assets health, supply and demand analysis helps to improve asset performance. Data-driven and business analytics tools and software are used to monitor conditions, costs, and performance, as well as to define scoring methods and the areas of critical priority. Data-driven and business analytics tools and software are used to enhance the reliability, capacity, and availability of the assets and minimize costs. The more data you have, the more you can do to manage the assets better.
Enhancing customer experience
There exist two prioritized dimensions of work for energy and utility companies directly related to general brand reputation. These are operational excellence and customer experience, which by nature are interdependent. The rapid development of smart technologies and the growing popularity of smart home provide new opportunities to users. Due to this fact, customers become more sophisticated in their choice of company or service. Thus, the demand for high-quality services increases.
All the companies are doing their best to adhere to the customers’ needs and desires. First of all, the application of multiple communication channels should be applied for this purpose. Omni channels provide the company with valuable insights for further processing. With the help of accurate analysis, the companies can effectively reveal the information about the customers’ demographics, behavior, and sentiment. As a result, they can tailor personalized recommendations, suggestions, and services.
The energy and utility companies are under constant pressure to provide high-quality services without delays and failures at an affordable price 24/7. People rely on energy sources in their daily dealings and work. Due to the fast development and improvement of technologies, the industry stands before new opportunities and new challenges every day.
Machine learning algorithms, analytic models, and big data solutions help companies to manage and effectively use their resources, control energy flows, regulate grid, optimize work, and avoid mistakes that may cost a lot.
The use of real-time and predictive analytics and data science solutions requires significant investment and readiness to face the challenges, learn and introduce new complex operations. However, the benefits of data science application in energy and utility sphere are numerous.
Improve your skills with Data Science School
Big data engineering services, data cleaning, transformation, storage. API design and development
Data Science Applications
Building end-to-end data pipelines, machine learning application, AI consulting
Machine learning solutions
We apply machine learning algorithms and models to solve business challenges from prototyping phase to integration of models into production systems.