2 citations · 2 across the 4 of their papers we have counts for
5 papers
The Living Forecast: Evolving Day-Ahead Predictions into Intraday Reality
Kutay Bölat, Peter Palensky, Simon Tindemans
Accurate intraday forecasts are essential for power system operations, complementing day-ahead forecasts that gradually lose relevance as new information becomes available. This pa…
Clustering Rooftop PV Systems via Probabilistic Embeddings
Kutay Bölat, Tarek Alskaif, Peter Palensky +1
As the number of rooftop photovoltaic (PV) installations increases, aggregators and system operators are required to monitor and analyze these systems, raising the challenge of int…
Impact of Lead Time on Aggregate EV Flexibility for Congestion Management Services
Nanda Kishor Panda, Peter Palensky, Simon H. Tindemans
Increased electrification of energy end-usage can lead to network congestion during periods of high consumption. Flexibility of loads, such as aggregate smart charging of Electric…
Transformer-based few-shot learning for modeling Electricity Consumption Profiles with minimal data across thousands of domains
Weijie Xia, Gao Peng, Chenguang Wang +3
Electricity Consumption Profiles (ECPs) are crucial for operating and planning power distribution systems, especially with the increasing number of low-carbon technologies such as…
Stable Training of Probabilistic Models Using the Leave-One-Out Maximum Log-Likelihood Objective
Kutay Bölat, Simon H. Tindemans, Peter Palensky
Probabilistic modelling of power systems operation and planning processes depends on data-driven methods, which require sufficiently large datasets. When historical data lacks this…