Examples of Data Science in the Utilities Industry

Utilities is a complex sector with many moving parts, all of which need to be in sync in order to maintain reliable and safe services. That complexity is only expected to grow with the expansion  of the Internet of Things and an increase in demand for sustainable, fossil fuel alternatives. Smart sensor networks and IoT technologies generate billions of raw data points that contain information that is highly useful to utility management professionals, such as real-time asset performance and resource use. Data science in the utilities industry is the key to helping unlock that information.

Data science for utilities enables the processing of vast amounts of structured and unstructured big data, which is exploding in volume, variety, and velocity. This data can then be further analyzed to drive insights and improve decision making throughout every level of the utilities and energy sector. Read on to learn how the top 10 data science use cases in energy and utilities are transforming how utilities providers are optimizing the industry.

‍

Failure Probability Modeling

A prevalent example of data science in utilities industry is the use of the failure probability model. Failures in utilities machines and devices are inevitable, but they don’t have to be unpredictable. The data science failure probability model is an essential part of the decision making process for utility and energy companies, and a surefire way to stay ahead in the game and predict when failures will occur. Using Machine Learning to anticipate failures and planning accordingly helpy managers warn potentially affected customers so that they can prepare and so that the company can protect their reputation.

Outage Detection & Prediction

Outages are bound to happen and, while frustrating to the customer, outages are not, in fact, failures. They are often intentional, preventative measures built into the automatic protection system operation. Modern smart power outage communication systems are real-time solutions that can predict and detect outages with the use of predictive algorithms, which rely on the identification of the right metrics and the threshold value for it. These modern data science solutions can do things like: predict the influence of weather conditions and the impact of the near-term asset values on the power grid; detect possible outages by smart meter events; filter outage inputs and identify outage types in real time; and communicate confirmation of the outage.

Customer Usage Tracking & Demand Prediction

Customer usage data, as well as data related to season, temperature, location, and time of year, power demand forecasting, which helps modern utility companies make data-driven decisions regarding operations, supply, finances, and more. Demand forecasting is a major component in smart energy management, which uses real-time management applications and solutions to find the most efficient balance between demand and supply. Metrics of energy use are gathered and used to adjust the energy flow to the current demand rate. This data can even be used in demand response management programs, which encourages consumers to use energy at a specific time to save money.

Preventative Maintenance

Being able to predict the point of failure for each machine and device in the utilities sector enables managers to set up maintenance and replacement schedules, preventing costly disruptions in operations and services. Maintenance predictions require feeding an algorithm constant streams of real-time data related to current equipment conditions and performance levels under normal operating conditions. Using the algorithm output, utilities smart systems can alert decision makers of poor functioning of the mechanisms so that they can act quickly.

Vegetation Management

A combination of utility power grid maps, vegetation imagery via GIS, and weather forecasting data is collected in order to help utility providers determine: the likelihood that a weather event will impact utilities services, the scale of vegetation management that is required to avoid costly damages to equipment, back-up power sources and services blueprints, when and where to schedule outages to avoid wildfires. Data science tools facilitate the ingestion of historical and current data in real-time to help utilities managers detect patterns and gain insight into instances of services disrupted by vegetation-related incidents.

Real-Time Customer Billing

The most unpleasant aspect of utilities for customers is often not the price nor the service, but rather the customer service they are provided when they submit a concern. Data science plays a part in customer-facing operations as well. Modern utilities providers leverage myriad applications and software to bring visibility into the service provision, billing, and payment process; improve the handling of misunderstandings or disputable issues; and improve quality of service and eliminate delays. Operational activity and transactions can be tracked in real-time by both internal and customer users, facilitating immediate action when needed.

Smart Grid Security & Theft Detection

A highly valuable data science example is the use of Machine Learning to detect security breaches. Theft in the utilities and energy industry can be extremely costly. Smart grid security solutions use algorithms that constantly track and study user customer behavior to detect patterns and anomalies and identify hackers and their attempts to breach security. Real-time data and anomaly alerts help utility companies immediately detect and act on suspicious behavior. 

Advanced Metering Infrastructure (AMI), an integrated system of smart meters, communications networks, and data management systems, enables utility companies to perform activities remotely, such as disconnecting service, detecting tampering, identifying and isolating outages, and monitoring voltage. Data science tools facilitate the ingestion and processing of these vast and various real-time data streams.

Asset Performance Optimization

A combination of real-time asset health data and supply and demand analysis can help managers determine how to optimize the performance of different utility assets. Examples of inefficiencies include equipment failures or delays in energy supply, and unplanned service interruptions or complications. Data science and utility analytics solutions are used to monitor asset conditions, costs, and performance, as well as to define methods for scoring and issues of critical priority, enabling managers to enhance utility assets’ reliability, capacity, and availability and minimize costs.

Dynamic Energy Management

Dynamic energy management involves the integration of energy efficiency and load management from a dynamic, whole-systems perspective that simultaneously addresses permanent energy savings, permanent demand reductions, and temporary peak load reductions. This type of management combines conventional energy management principles regarding demand, distributed energy sources, and demand-side management with modern energy challenges like energy efficiency, temporary load, and demand reduction. Smart energy management systems depend on our ability to perform load forecasting, which data science tools perform with the use of an algorithm.

Improving Operational Efficiency

There are a wide variety of different factors that can be tweaked here and there to improve a process’s overall operational efficiency. Data science in utilities can help us determine which factors need adjusting and how much. Smart data application and software determine which operations should be optimized by gathering data related to time, activity rate, and the state of some operations, combined with external factors to measure a function’s efficiency. Data science is used for modeling various situations and predicting the possible efficiency rates under different circumstances.

Mitigating Fleet Costs

Fleet management – knowing where individual utility vehicles are, how they’re performing, and how each driver is behaving – helps lower the risk of liability while delivering the best possible customer service. With vehicle telematics data, utility fleet managers can analyze and regulate fleet driving behavior, track fleet movements, improve efficiencies, and reduce idle time. Collecting these vast and varied data streams in real-time helps utility managers make correlations between productivity and certain fleet behaviors.

The HEAVY.AI Advantage

Utilities is an extremely large and time-sensitive sector with enormous potential for optimization, and only the most advanced big data analytics tools can truly harness the vast data streams that utility assets generate. HEAVY.AI’s hardware accelerated analytics delivers greater speed and performance than any legacy system could handle. Instant interactivity coupled with dynamic visualizations of big data drives faster action and better decisions. 

All of these advantages mean decreased time to value and an improved ability to: ask the right questions and define objectives, fix inconsistencies and gaps in data, analyze data and form hypotheses about problems, conduct effective demand forecasting, perform fast data discovery, train accurate predictive models, monitor ML models, and create interactive data visualizations that reveal hidden insights and clearly communicate findings to the right people – all at lightning speeds. 

When HEAVY.AI gives data scientists and utility managers the power to explore big data at the speed of thought, the questions you can answer are limitless.