04 October 2024

Ruben Van den Bossche, co-founder & CEO, Gorilla

Energy providers are tasked with translating these complex variables into clear, fairly priced products and services for consumers. And with the rise of renewables introducing intermittency to the energy grid — where fluctuations from sources like wind and solar can result in instances of negative pricing — this task is more crucial than ever. Many UK small businesses are not equipped with the tools needed to manage this volatility effectively. As of March 2023, only 57% of all electricity and gas meters in the UK were smart, lagging behind the US’s 80%, France’s 93%, and Spain’s 100%.

But lacking insight at the meter isn’t just a problem for consumers. Energy providers face even tougher back-end challenges with outdated systems that require manual data input, hindering real-time analysis critical for today’s dynamic market. Massive data volumes, like forecasting the availability of renewable energy, further complicate swift responses. By the time data is processed into actionable models, market conditions may have already shifted drastically.

To address these challenges, AI-driven digital platforms can automate data collection and convert it into actionable insights. This approach can improve competitiveness, enhance the profitability of renewables, and ensure a smoother experience for customers.

Forget the spreadsheets

Energy markets are becoming increasingly granular. The era of day-to-day price and weather analysis is ending, paving the way for second-to-second measurement, particularly for intermittent renewable sources.

Digital platforms enable the integration of various data sources, boosting operational efficiency through automation and predictive insights. These technologies help energy companies streamline pricing by connecting with customer relationship systems. Such improved pricing strategies enhance customer engagement and enable renewable tariffs.

Energy producers can use big data to help price their products and services amid market, political, and weather fluctuations. By transforming complex datasets into actionable insights, digital tools can improve demand forecasting and portfolio management. With specialised applications for the energy and utilities sectors, data processing platforms enhance transparency and efficiency, addressing areas where spreadsheets fall short.

A seamless solution

Third party platforms should focus on creating an end-to-end data strategy that benefits both consumers and energy providers. For providers, this starts with precise and transparent pricing based on the latest data, assessing lifetime value and risk accurately. Once customers are onboarded, meter data analytics can be used to enhance forecasting and portfolio analysis.

Multiple forecasting models advance energy risk management, while portfolio analysis provides insights into the revenue, profits, and losses. The next step is aligning this integrated process with the energy retailer’s data ecosystem for seamless operation, while ramping up processing speed from days to hours or minutes.

An automated platform can automatically re-price an entire portfolio in a matter of hours. What were once unexplainable variances in data are backed by insights at the individual meter level. Errors are flagged, and the data is cleaned and ready for data scientists. Predictive analytics, machine learning and other AI methods could push the speed and efficiency of this process even further. These tools identify trends, clusters and other patterns in data, generating models for optimisation, forecasting and personalisation.

For instance, a retailer facing inefficiencies with its manual, spreadsheet-based pricing models could integrate a flexible and scalable customer relationship management (CRM) system, automating their complex pricing models. This transition would free sales reps to focus on customer relationships and would minimise data entry errors.

The centralised platform would also streamline compliance with varying regional regulations, significantly improving risk management. A phased implementation could lead over time to sales growth and enhanced market responsiveness. Although examples like this exist in the UK, more energy providers could adopt similar strategies to energy producers in the US, where we’re seeing more adoption of third-party digital solutions.

Adapt to thrive

The key to thriving in a volatile market lies in speed. Integrating AI or machine learning techniques into existing infrastructure can have a profound impact. However, incorporating new technologies into or around legacy systems presents challenges such as data fragmentation and a lack of specialised in-house expertise. Energy companies should therefore partner with software providers who can bridge both the data gap and the expertise gap.

Comprehensive and collaborative planning, and a phased approach to implementation, can mitigate the risks and ensure a smooth transition to newer infrastructure. This lets in-house teams and sales reps focus on generating value and getting energy sold at the right price, rather than getting bogged down in endless system tweaks.

By integrating AI techniques, energy providers can significantly boost operational efficiency, reduce costs, and refine decision-making processes. Embracing this digital transformation is not just crucial for strategy, but also essential for sustainability and innovation in the energy sector. As the market evolves, AI-driven solutions will be essential for navigating complexities and ensuring long-term success.