Customer satisfaction is the next major battleground for Australian utilities. Linking what customers desire with the precise range of possibilities and market conditions for offering those energy services is one of the major promises of integrating AI into the energy markets. Machine learning is ideally suited for determining precisely what customers want and then modifying energy purchase decisions by those findings.
Numerous utilities have developed their unique platform-based customer information systems (CIS), customer management, and building capabilities. The sector is going through a rapid digital change, creating constraints connected to the cost to acquire and fulfill customer experience difficulties as well as the requirement of the low-carbon economy. This shift has forced utility-based services sectors to upgrade their capacities and apply intelligent client management.
Let’s dive deep into how different utility industries try multiple solutions to meet better returns from existing investments and explore the strategies for adopting digital and business transformation.
What are Utilities Analytics?
With this cutting-edge technology, the utilities serviced based companies are a fast-growing area undergoing digital transformation. Analytics enables the utility industries to capture and analyze a wide range of business variables. Utility analysis is composing software and services that harness the value of big data resources.
Data analytics drives business intelligence for utilities to convert this information into actionable insights that utility providers can use to improve operations, minimize issues and recover more efficiently from outages.
For utility industry technology expertise,
- In the next two to three years, 35% of power business executives will explore cloud computing, and 42% will explore analytics, AI, and ML.
- By 2022, 55% of utilities will have automated, optimized, and orchestrated assets, operations, customers, and employees using a core digital platform.
- By 2023, utilities will have connected 75% of their key assets digitally, enabling them to predict and stop equipment breakdown and extend asset life cycles.
Utility businesses use their enormous data sets to improve operations and performance. By utilizing valuable data-driven insights, the utilities-based service sector operates more strategically and intelligently to enhance outcomes and increase income. The collection of data is regarded as both an internal and external process. Using sensors and meters to monitor energy use and equipment performance is necessary for data collection internally. Whereas externally, it involves tracking the weather or the other elements that impact the utility operation. Data analytics transforms this data into valuable insights that utility companies can utilize to streamline operations, reduce problems, and recover from outages more quickly. The firm can make more data-informed decisions thanks to integrating this information across all decision-making processes.
Business benefits of utility analytics
The business advantages of energy and utility analytics are increasingly compelling and broadly based.
- Data analytics for utilities helps utility companies recover from outages as quickly as possible to boost efficiency and avoid revenue loss. Utility firms use data to identify customer requirements, improve customer service, and better understand consumer behavior.
- Utility companies use data analytics and IoT to operate and maintain a predictive grid.
- It has enormous benefits in forecasting weather events and preparing for climate change.
- Many utilities view data analytics as an affordable way to identify and stop revenue leakage caused by energy theft.
- Better estimate the energy saving and offer appropriate demand management program.
- The power distribution sector improved, and the best outcomes can be achieved using data-driven insights to reduce outages.
- With additional consumer data, businesses can customize billing to user segments based on rate class, program participation, interests, and conservation efforts.
- Customer loyalty was a vital issue for the utilities business sector, but the ability to stream the billing process and innovation on the services has improved the return on customer loyalty and led to a more successful business.
Ways to Approach Analytics in Utilities Industries
The Executive Roundtable for Australia Utilities, organized by Google and Wipro, came up with an essential aspect of innovation that, at present, utility companies are using AI platforms and Google data to get more transformative and productive results.
This approach helps to overcome the digital transformation roadblocks by reducing its tracks by a heap of paper-based processes. Almost all internal processes and workflows were manually tracked using labor-intensive, error-prone paperwork with less time-consuming.
Use Case 1: Trouble Transformer Tracker
Linemen now use this app to track transformers that are brought in and returned when a failing or damaged transformer is replaced. They swipe a bar code rather than carefully inputting serial numbers. The transformer might be tracked more quickly and efficiently thanks to the quick, error-free process.
Use Case 2: Circuit Inspection Tracker
This app’s overall plus point is that it helps eliminate free-form entries and collects GPS information. To increase speed and accuracy without sacrificing data quality, users in the field can annotate images with additional context.
This approach helps to empower the frontline workers. As a result of the integration of machine learning and artificial intelligence into Google Glass Enterprise Edition, frontline workers now receive instructions while performing activities on the ground using a light, line-of-sight display. Glass connects workers to remote experts so they can “see what they see,” promoting in-person collaboration and problem-solving. Glass also gives workers access to training resources, including films, instructions, and checklists.
Use Case 1: Smart Analytics
Smart analytics is used for market bidding to make more wise and better decisions. It overall takes forward the aspects of when and what to buy at appropriate pricing. Wholesale pricing is one of the significant aspects of this approach to smart analytics. It also includes-
- Future and weekly market circumstances
- Competitor tactics
- Best practice for price and quantity bidding
Use Case 2: Stream Analytics
Stream analytics is used to assess turbine performance. Using stream analytics, utility-based organizations could control and process large amounts of telemetry data in real-time earlier with their AI strategy. But in the current scenario, the approach has been changed as it has become more assessable to identify and predicts the turbine gearbox. It also gives an idea about the generator failures while using the dashboard.
Data and analytics have become increasingly important for utilities in recent years. By leveraging data and analytics, utilities can improve their operations, increase efficiency, and provide better customer service. Some of the key use cases for data and analytics in utilities include grid management, demand forecasting, and customer engagement. Grid management uses data to optimize electricity distribution, while demand forecasting helps utilities predict future energy usage and plan accordingly. Customer engagement is also important, as utilities can use data to better understand their customers’ needs and preferences, and to develop targeted marketing campaigns. Overall, data and analytics have the potential to significantly improve the way utilities operate and enhance the customer experience. By investing in data and analytics technologies and strategies, utilities can stay ahead of the curve and continue to provide valuable services to their customers.