Smart Meters: The Role of Synthetic Data

This data opens up new possibilities for understanding energy use, fine-tuning demand predictions, managing the grid, and subsequently, moving towards a more sustainable future. 

However, working with smart meter data isn’t straightforward. Privacy concerns and data sharing barriers remain significant challenges, which synthetic data aims to address by offering a privacy-preserving alternative. While synthetic data mitigates these risks, ensuring its accuracy and utility introduces new complexities.  

UK energy companies have also hit the headlines recently around smart meter performance – with a distinction between the reliability of smart meters in the north and south of the country. Let’s explore these challenges and what’s being done to leverage the full potential of smart meter data. 

Why smart meter data matters 

Smart meters provide real-time insights into how households and businesses consume energy, with measurements taken every 30 minutes. This granular data enables providers to predict demand more accurately to balance the grid and reduce strain on infrastructure. Understanding consumption patterns helps to decarbonise heating, optimise renewable energy usage, and build a more adaptable grid. 

Despite this, challenges like accessing accurate, timely data go beyond privacy concerns. Technical limitations also play a role, such as the need for robust infrastructure to enable smart meters to upload data every 30 minutes and relay it efficiently to utility companies. Additionally, latency – the time it takes for data to travel and be processed – can hinder timely analysis and predictions. 

The UK’s smart meter rollout has faced significant delays, making it unlikely that the original target of universal installation by 2025 will be met. As of June 2024, 64% of domestic meters were smart, with 57% operating in smart mode (ie the operational state where the meter communicates data automatically and in real-time with energy suppliers or utility companies via a secure network). The government has since adjusted its goals, now aiming for 74.5% of homes and nearly 69% of small businesses to have smart meters by the end of 2025. 

Synthetic data: Balancing privacy and access 

One major obstacle in smart meter data utilisation is privacy. This data is deeply personal, detailing household behaviours and energy use. Sharing real data for research or commercial purposes introduces risks, including potential misuse and breaches of consumer trust.  

Enter synthetic data: synthetic data generation, using AI models, allows organisations to create datasets that mimic real-world patterns while protecting privacy. This artificial data is highly realistic, but because it doesn’t belong to actual individuals, it can be shared more freely to support research and innovation. Opensource community initiatives in synthetic smart meter data, for example OpenSynth set up by the Centre for Net Zero (CNZ) and LF Energy, offer promising paths forward by allowing researchers and developers across sectors to access synthetic datasets. Another way in which data can be anonymised is through differential privacy; this works by adding “noise” so no one’s exact data can be revealed, and fake “outliers” (unusual data points) can be added to test if these stood out or got leaked.  

This data democratisation approach enables wider participation in energy solutions across startups, universities and regulatory bodies, without risking privacy. 

Creating a global energy data community 

Even with synthetic data, global differences in smart meter standards remain a barrier. Each country has unique data collection protocols with varying intervals, sampling methods and privacy requirements. In the UK, data is gathered at 30-minute intervals, whereas other countries collect data more frequently. This is where an opensource community for synthetic smart meter data presents an opportunity to harmonise these standards – making it easier for providers to develop universally beneficial solutions. 

By pooling insights globally, energy providers and researchers can better predict and manage regional and global demand patterns. This kind of collaboration has the potential to scale renewable energy integration globally, amplifying efforts to combat climate change. 

Next steps: Smart meter data research and adoption 

To fully reach the potential of smart meter data, further research is needed to ensure synthetic data can reliably replicate real-world patterns and provide accurate insights for practical applications. Addressing scepticism about its use as a substitute for real data in real-world scenarios can help accelerate its adoption, and demonstrating its effectiveness in critical areas like energy demand forecasting, grid optimisation and strategic planning will strengthen its “trust factor” and encourage wider use. 

Equally important is building trust among stakeholders by ensuring data is handled responsibly. Transparent communication about privacy measures and the broader community benefits will be key to building confidence. As more organisations explore the potential of synthetic data, this trust will play a vital role in driving meaningful, data-driven renewable energy solutions. 

Our recent Hypercube Podcast guests from the Centre for Net Zero, Sheng Chai and Gus Chadney, explore the use of synthetic smart meter data globally – check it out on YouTube.