@misc{9231434,
  abstract     = {{The increasing demand for electric vehicle charging stations creates new challenges for electric grid planning. This thesis proposes a framework for forecasting realistic 24-hour peak load profiles for new direct current fast-charging stations without historical observations. The method combines $K$-means clustering of existing stations’ hourly load patterns and random forest models that predict both the daily load shape and peak load magnitude of a new station. The resulting profile is scaled to create a low-risk predicted load curve and evaluated using leave-one-out cross-validation. The results show that the proposed profiles cover the majority of observed peak loads while remaining below theoretical maximum capacity for much of the day. Small charging stations are more difficult to predict, as they can more easily approach full capacity. The framework is designed to support grid planners in making more efficient and risk-aware decisions when assessing new charging station connections.}},
  author       = {{Gerholm, Markus}},
  issn         = {{1404-6342}},
  language     = {{eng}},
  note         = {{Student Paper}},
  series       = {{Master’s Theses in Mathematical Sciences}},
  title        = {{Forecasting Peak Load Profiles for New High-Power Charging Stations: A K-Means Clustering and Random Forest Approach}},
  year         = {{2026}},
}