Researchers in Spain have developed a conditional diffusion-based model to improve the visibility of behind-the-meter PV systems using low-resolution smart meter data. The probabilistic approach enables accurate PV reconstruction with uncertainty estimates, helping distribution system operators better manage grid planning and flexibility.
The rapid adoption of behind-the-meter (BTM) distributed energy resources, such as photovoltaic (PV) systems and heat pumps (HPs), is creating new challenges for distribution system operators (DSOs). Since these assets are located behind customer meters, their individual generation and consumption profiles are not directly observable from conventional smart meter measurements. This lack of visibility limits DSOs’ ability to accurately assess network conditions, forecast demand, and manage emerging flexibility opportunities.
Addressing this challenge, a research team from Spain’s Universitat Politècnica de Catalunya (UPC) has introduced a generative diffusion-based methodology for probabilistic BTM PV disaggregation. The method extracts the probabilistic characteristics of PV generation from low-resolution smart meter data, enabling reconstruction of solar production profiles from measurements available at 30-minute intervals.
