From the 1 of 7 linked papers with an AI index.
7 papers
MxGPS: Multiplex Graph Transformers for a Power Grid Foundation Model
Charilaos Papaioannou, Ioannis Tsantilas, Dimitris Giannakakos +9
The paper presents MxGPS, a multiplex graph transformer that jointly trains on static state estimation and AC power flow to avoid topology overfitting, achieving robust zero‑shot p…
Deep Learning for Electricity Price Forecasting: A Review of Day-Ahead, Intraday, and Balancing Electricity Markets
Runyao Yu, Derek W. Bunn, Julia Lin +8
Electricity price forecasting (EPF) plays a critical role in power system operation and market decision making. While existing review studies have provided valuable insights into f…
OrderFusion: Encoding Orderbook for End-to-End Probabilistic Intraday Electricity Price Forecasting
Runyao Yu, Yuchen Tao, Fabian Leimgruber +6
Probabilistic intraday electricity price forecasting is becoming increasingly important for short-term power-system operation. With increasing renewable generation, demand-side fle…
PriceFM: Foundation Model for Probabilistic Electricity Price Forecasting
Runyao Yu, Chenhui Gu, Jochen Stiasny +4
Electricity price forecasting in Europe presents unique challenges due to increasing renewable generation variability, market integration, and the continent's physically interconne…
Probabilistic Hysteresis Factor Prediction for Electric Vehicle Batteries with Graphite Anodes Containing Silicon
Runyao Yu, Viviana Kleine, Philipp Gromotka +5
Batteries with silicon-graphite-based anodes, which offer higher energy density and improved charging performance, introduce pronounced voltage hysteresis, making state-of-charge (…
Orderbook Feature Learning and Asymmetric Generalization in Intraday Electricity Markets
Runyao Yu, Ruochen Wu, Yongsheng Han +1
Accurate probabilistic forecasting of intraday electricity prices is critical for market participants to inform trading decisions. Existing studies rely on specific domain features…