CLC number: TM714
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2008-11-10
Cited: 9
Clicked: 6784
S. Sp. PAPPAS, L. EKONOMOU, V. C. MOUSSAS, P. KARAMPELAS, S. K. KATSIKAS. Adaptive load forecasting of the Hellenic electric grid[J]. Journal of Zhejiang University Science A, 2008, 9(12): 1724-1730.
@article{title="Adaptive load forecasting of the Hellenic electric grid",
author="S. Sp. PAPPAS, L. EKONOMOU, V. C. MOUSSAS, P. KARAMPELAS, S. K. KATSIKAS",
journal="Journal of Zhejiang University Science A",
volume="9",
number="12",
pages="1724-1730",
year="2008",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A0820042"
}
%0 Journal Article
%T Adaptive load forecasting of the Hellenic electric grid
%A S. Sp. PAPPAS
%A L. EKONOMOU
%A V. C. MOUSSAS
%A P. KARAMPELAS
%A S. K. KATSIKAS
%J Journal of Zhejiang University SCIENCE A
%V 9
%N 12
%P 1724-1730
%@ 1673-565X
%D 2008
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A0820042
TY - JOUR
T1 - Adaptive load forecasting of the Hellenic electric grid
A1 - S. Sp. PAPPAS
A1 - L. EKONOMOU
A1 - V. C. MOUSSAS
A1 - P. KARAMPELAS
A1 - S. K. KATSIKAS
J0 - Journal of Zhejiang University Science A
VL - 9
IS - 12
SP - 1724
EP - 1730
%@ 1673-565X
Y1 - 2008
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A0820042
Abstract: Designers are required to plan for future expansion and also to estimate the grid’s future utilization. This means that an effective modeling and forecasting technique, which will use efficiently the information contained in the available data, is required, so that important data properties can be extracted and projected into the future. This study proposes an adaptive method based on the multi-model partitioning algorithm (MMPA), for short-term electricity load forecasting using real data. The grid’s utilization is initially modeled using a multiplicative seasonal ARIMA (autoregressive integrated moving average) model. The proposed method uses past data to learn and model the normal periodic behavior of the electric grid. Either ARMA (autoregressive moving average) or state-space models can be used for the load pattern modeling. Load anomalies such as unexpected peaks that may appear during the summer or unexpected faults (blackouts) are also modeled. If the load pattern does not match the normal behavior of the load, an anomaly is detected and, furthermore, when the pattern matches a known case of anomaly, the type of anomaly is identified. Real data were used and real cases were tested based on the measurement loads of the Hellenic Public Power Cooperation S.A., Athens, Greece. The applied adaptive multi-model filtering algorithm identifies successfully both normal periodic behavior and any unusual activity of the electric grid. The performance of the proposed method is also compared to that produced by the ARIMA model.
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