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Time Segmented Deep Forecasting of Power Grid Frequency Deviations

  • Metaadatok
Tartalom: http://hdl.handle.net/10890/66993
Archívum: Műegyetem Digitális Archívum
Gyűjtemény: 1. Tudományos közlemények, publikációk
Konferenciák gyűjteményei
BME MIT PhD Minisymposium
BME MIT PhD Minisymposium, 2026, 33rd
Cím:
Time Segmented Deep Forecasting of Power Grid Frequency Deviations
Létrehozó:
Setianingsih, Casi
Hartmann, Bálint
Dátum:
2026-10-16T10:09:23Z
2026
Tartalmi leírás:
Frequency deviations reflect short-term power imbalance and contain information about system-wide synchronization and time-delayed coupling. We analyze about one month of synchronized transmission-grid frequency measurements from 17 monitoring points using lagged Pearson cross-correlation for lags 1–10, reported for two aggregations: an all-month baseline and a weekday–weekend split. Weekends show higher coherence than weekdays across all evaluated lags (e.g., at lag 1 the global mean correlation is 0.258 on weekends versus 0.174 on weekdays). From the lag sweep, we extract peak correlation and best lag to summarize an empirical delay structure; most pairs peak at small lags, while some corridors require larger lags (≈8–10 samples) for maximum alignment. To connect these coupling patterns to forecasting design, we run a controlled univariate benchmark on a reference station (24,083,128 samples in Feb 2025, 0.1 s sampling) using LSTM, TCN, and PatchTST under the same time split, train-only preprocessing, and early stopping, with window lengths k∈(1,3,10,20). Increasing the window from k=1 to k=20 improves accuracy for all models and reduces architecture sensitivity; at k=20 the three models converge to nearly the same nRMSE (≈11.5%).
Nyelv:
angol
Típus:
könyvfejezet
Formátum:
application/pdf
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