Welcome to part 2 of our data science blog series! In part 1, we explored the basics of data science. Now it’s time to examine why implementing data science at a utility can reap endless benefits and aid in adapting the grid for the energy transition.
Let’s get started! What are the advantages of data science for utilities?
Improved Decision-making:
Data analysis provides valuable insights that can be used to make informed decisions. Utilities can predict future outcomes and make data-driven decisions by analyzing historical data and trends. For instance, using data science, utilities can forecast the impact on grid infrastructure from load growth due to electric vehicle adoption.
Increased Efficiency and Productivity:
Data Science can help automate processes, reduce manual labour, increasing efficiency and productivity. For example, utilities can use data and analytics to detect discrepancies in energy usage that may indicate theft. This entails using energy balancing to reduce losses so utilities can increase resources spent on grid modernization.
Better Customer Experience:
By analyzing customer data, utilities have a better understanding of their preferences, behaviours, and needs. An invaluable tool for utilities in improving their customer experience is consumption data. Consumption data can be a powerful tool for helping people save on their monthly utility bills by providing insights into their energy usage patterns and identifying opportunities for reducing consumption.
- Identify high-usage periods: Consumption data can help identify the times of day or days of the week when clients use the most energy, allowing them to adjust their usage patterns to reduce consumption during these high-usage periods.
- Identify appliances that act as energy hogs: Consumption data can help identify the devices in homes that use the most energy.
- Set energy usage goals: Consumption data can help set goals for reducing energy usage. Tracking consumption can help clients stay motivated to reduce their energy usage.
Improved Risk Management:
Data Science can help utilities mitigate potential risks, reduce economic losses and enhance grid reliability with predictive analysis. By analyzing data on things like weather patterns, equipment maintenance history, and customer behaviour, utilities can identify potential issues. Predictive analytics can also help utilities develop better contingency plans for unexpected events. For example, utilities can use situational awareness and set up grid alerts to prevent unnecessary damage or harmful influences in the grid. Another great example is outage detection – analyzing transformers downstream from where a planned outage will occur to identify opportunities for asset upgrades.
Innovation and New Products:
Data Science can help utilities develop innovative products and services. By leveraging data insights, utilities can create programs and tariffs that meet the needs and preferences of customers. A perfect example would be the implementation of EV charging programs – educational programs to teach early adopters when, where and how to charge their EVs efficiently and energy buyback programs. This will ensure the grid can manage these new inputs and demands.
In summary, Data Science can help utilities face the energy transition by improving their ability for decision-making, increasing efficiency and productivity, enhancing customer experience, mitigating risks, and driving innovation!
Part 3 of our data Science blog series will explore the benefits of working with clean data. You can also explore this past blog post, “How Can A Utility Become More Data-Driven?”
Additionally, we are always keen to chat about our Data Science Services. Our team of experienced data scientists can provide your organization with the tools to commence your data science journey.
Contact us directly for more information.
