most citedExploring the Interpretability of Forecasting Models for Energy Balancing Market

7 citations · 7 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2026

Urban Power Grid Topology and Hierarchy Identification from Open Data

Shiliang Zhang, Sabita Maharjan, Alexey Vinel +1

Understanding the complex topology and hierarchy of urban power grid is crucial for energy prognosis, power flow management, and system resilience analysis. However, detailed grid…

cs.AI2026

AI and Open-data Driven Scalable Solar Power Profiling

Shiliang Zhang, Sabita Maharjan, Damla Turgut

Solar photovoltaic (PV) deployment is expanding rapidly, yet detailed, up-to-date information on the spatial distribution and capacity of rooftop PV remains limited. This paper pre…

cs.DL2026

Cenergy3: An Open Software Package for City Energy 3D Modeling

Shiliang Zhang, Sabita Maharjan

The efficient management and planning of urban energy systems require integrated three-dimensional (3D) models that accurately represent both consumption nodes and distribution net…

cs.CR2026

Security, privacy, and agentic AI in a regulatory view: From definitions and distinctions to provisions and reflections

Shiliang Zhang, Sabita Maharjan

The rapid proliferation of artificial intelligence (AI) technologies has led to a dynamic regulatory landscape, where legislative frameworks strive to keep pace with technical adva…

q-fin.ST2026★ 7 cited

Exploring the Interpretability of Forecasting Models for Energy Balancing Market

Oskar Våle, Shiliang Zhang, Sabita Maharjan +1

The balancing market in the energy sector plays a critical role in physically and financially balancing the supply and demand. Modeling dynamics in the balancing market can provide…