In our latest blog, Luis Castillo, Product Director at Sigenergy (UK&I), explores how AI is transforming energy management in a UK home, optimising consumption and delivering significant savings on energy bills. By predicting demand and adapting in real time, this real-world example highlights the dynamic capabilities of Sigen AI in action.

A Case Study of a UK Household

Dynamic tariffs are revolutionising energy markets, enabling consumers to save by adjusting their energy usage based on real-time prices. They boost energy efficiency, support renewable adoption, and stabilise the grid, all while empowering users and promoting sustainability. However, navigating this complex pricing landscape remains a challenge. How can we simplify dynamic tariffs and unlock even greater benefits for consumers?

Sigenergy’s SigenStor, paired with the self-developed mySigen App, offers an ideal solution. This advanced energy storage system, driven by AI, transforms the challenges of dynamic tariffs into valuable opportunities.

In this article, we’ll explore how Sigenergy delivers on this promise through a real-life UK residential case study. With mySigen, the industry’s most intelligent energy application, users across Europe can fully harness the potential of dynamic tariffs. Paired with Sigen AI, it empowers users to maximise savings while making energy management simpler than ever.

System Overview: UK Residential Installation

• Solar PV Capacity: 10.08 kW
• Energy Storage Capacity: 19.34 kWh
• AI Mode Operation Duration: 10 months

Monthly Savings: AI in Action

The table below compares the household’s monthly energy savings with and without AI optimisation. 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 analyse returns over a longer timeframe. As shown in the table below, despite minor variations, the overall return remains consistently stable and exceeds 20%. This highlights the dependable performance of AI technology in delivering high returns for residential users.

How Does Sigen AI Optimise Energy Use?

Sigen AI uses cutting-edge data analytics and machine learning to not only optimise energy consumption but also to predict and adapt to energy needs in real-time, delivering substantial savings, enhanced efficiency and unmatched operational performance. 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 power generation from solar panels.
• The green curve shows the AI’s prediction of solar power generation, factoring in geographic, weather and environmental data.
• The yellow curve displays actual electricity consumption.
• The red curve represents Sigen AI’s forecast of future electricity consumption, built on patterns learned from cumulative data.

The close alignment of the predicted and actual curves demonstrates the AI's ability to accurately forecast and optimise energy generation and consumption. While there may be minor fluctuations, the overall trends are remarkably aligned. This level of precision in prediction, despite inherent variations in user behaviour, highlights the power of AI in driving reliable and efficient energy management.

As Sigen AI continuously learns from user behaviour, its ability to predict and optimise electricity usage will only become more accurate and sophisticated. This advanced predictive capability is the cornerstone of next-generation AI-driven energy management systems, enabling users to achieve optimal energy savings while contributing to the broader goals of sustainability.

Let’s delve into two key scenarios to fully grasp the transformative potential of AI in reducing energy costs and maximising profitability.

Scenario 1: Optimising Energy Use with Time-of-Use Pricing

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

• 3:00 AM - 5:00 AM: The system charged the battery at the lowest grid prices.
• 7:30 AM - 8:00 AM: Rising prices and solar generation were balanced by using solar and battery power for household loads.
• 9:00 AM - 3:00 PM: Solar energy charged the battery, while household loads were supplied by the low-cost grid.
• 3:00 PM onwards: During peak electricity prices, the battery powered household loads.

This optimisation reduced reliance on high-cost grid electricity. Under AI mode, daily savings reached £3.28, compared to just £1.13 in self-consumption mode—a 190% increase.

Scenario 2: Arbitraging Price Fluctuations for Profit

On October 21th, 2024, grid prices varied widely, with near-zero costs during early morning hours. Sigen AI capitalised on these fluctuations to generate profits:

• 12:00 AM - 2:00 AM: Excess energy was sold to the grid at £0.15/kWh.
• 2:00 AM - 4:00 AM: The battery recharged at near-zero prices.
• 4:00 AM - 5:00 AM: Energy was sold again at £0.15/kWh.
• 5:00 AM - 6:30 AM: Recharged again during near-zero prices.
• 4:00 PM - 8:00 PM: Solar and battery energy supplied household loads during peak prices.

With these actions, the AI-enabled system generated a net daily benefit of £3.83, compared to just £0.38 without AI - a staggering 900% improvement.

Conclusion: AI-Driven Energy Management

These case scenarios illustrate the transformative potential of Sigen AI in reducing energy costs and maximising profits. By leveraging AI to intelligently respond to dynamic tariff 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’s a powerful platform for smarter, more sustainable energy use. Whether optimising time-of-use energy or capitalising on price arbitrage, Sigenergy’s AI integration sets a new standard in the energy sector.