The power of AI to optimize energy bills: case study of a UK household

Dynamic tariffs are revolutionizing energy markets, allowing consumers to save by adjusting their energy consumption based on real-time prices. They improve energy efficiency, support the adoption of renewable energy and stabilize the grid, while empowering users and promoting sustainability. However, navigating this complex pricing landscape remains a challenge. How can we simplify dynamic pricing and provide even greater benefits to consumers?

SigenStor from Sigenergy, combined with the self-developed mySigen app, offers an ideal solution. This advanced, AI-driven energy storage system turns dynamic pricing challenges into valuable opportunities.

In this article, we explore how Sigenergy delivers on this promise through a real-world UK residential case study. With mySigen, the industry’s smartest energy app, users across Europe can fully exploit the potential of dynamic tariffs. Combined with Sigen AI, it allows users to maximize savings while making energy management simpler than ever.

System Overview: UK Residential Installation

• Solar photovoltaic capacity: 10.08 kW
• Energy storage capacity: 19.34 kWh
• AI mode operating time: 10 months

Monthly savings: AI in action

The table below compares monthly household energy savings with and without AI optimization. The results show a remarkable improvement, with Sigen AI achieving up to 44.56% additional savings on certain dates.

The observation period was extended to analyze returns over a longer period. As shown in the table below, despite minor variations, the overall yield remains consistently stable and exceeds 20%, highlighting the reliable performance of AI technology in delivering high yields to residential users.

How does Sigen AI optimize energy consumption?

Sigen AI uses cutting-edge data analytics and machine learning to not only optimize energy consumption, but also predict and adapt to energy needs in real time, enabling substantial savings, improved efficiency and performance unrivaled operational capabilities. The following real-world example illustrates the dynamic capabilities of Sigen AI in action.

In the chart below, four key curves are shown:

• The blue curve represents the actual electricity production from solar panels.
• The green curve shows the AI’s forecasts for solar energy production, taking into account geographic, weather and environmental data.
• The yellow curve displays the actual power consumption.
• The red curve represents Sigen AI’s predictions of future electricity consumption, based on models drawn from cumulative data.

The close alignment of predicted and actual curves demonstrates AI’s ability to accurately predict and optimize energy production and consumption. While there may be minor fluctuations, the overall trends are remarkably aligned. This level of accuracy in prediction, despite inherent variations in user behavior, highlights the power of AI to ensure reliable and efficient energy management.

As Sigen AI continually learns from user behavior, its ability to predict and optimize electricity consumption will only become more accurate and sophisticated. This advanced predictive capability is the cornerstone of next-generation AI-based energy management systems.

Let’s look at two key scenarios to fully capture the transformative potential of AI in reducing energy costs and maximizing profitability.

Scenario 1: Optimize energy consumption with time-of-use pricing

On October 30, 2024, electricity prices experienced significant fluctuations, with peaks in the morning and evening. Sigen AI managed energy flows intelligently, as shown below:

• 3:00 – 5:00: the system charged the battery at the lowest prices on the network.
• 7:30 a.m. – 8:00 a.m.: Rising prices and solar generation have been offset by the use of solar and battery power for household charges.
• 9:00 a.m. – 3:00 p.m.: Solar energy charged the battery, while household loads were powered by the low-cost grid.
• From 3:00 p.m.: During periods of peak electricity prices, battery-powered households charge.

This optimization reduced reliance on high-cost grid electricity. In AI mode, daily savings reached £3.28, compared to just £1.13 in self-consumption mode, an increase of 190%.

Scenario 2: Arbitrage Price Fluctuations for Profit

On October 21, 2024, network prices varied widely, with costs near zero in the early morning hours. Sigen AI capitalized on these fluctuations to generate profits:

• 00:00 – 02:00: Excess energy was sold to the grid at £0.15/kWh.
• 2:00 – 4:00: The battery recharged at prices close to zero.
• 4:00 – 5:00: Energy was again sold at £0.15/kWh.
• 5:00 a.m. – 6:30 a.m.: Recharged again at near-zero prices.
• 4:00 p.m. – 8:00 p.m.: Solar and battery power power home loads during a peak price period.

As a result of these actions, the AI-powered system generated a net daily profit of £3.83, compared to just £0.38 without AI – a staggering 900% improvement.

Conclusion: AI-based energy management

These case scenarios illustrate the transformative potential of Sigen AI in reducing energy costs and maximizing profits. By leveraging AI to intelligently respond to dynamic rate structures, the system delivers substantial financial benefits, unparalleled efficiency and effortless energy management.

The combination of SigenStor and Sigen AI is not just a tool: it is a powerful platform for smarter and more sustainable energy use. Whether optimizing energy usage time or capitalizing on price arbitrage, Sigenergy’s AI integration sets a new standard in the energy sector.