Enhancing peak demand forecasting with adaptive FastICA-transformer and entropy-based model customization.

Fahman Saeed, Sultan Aldera, Mohammad Alkhatib

Abstract read

In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Fahman SaeedComputer Science Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia. faesaeed@imamu.edu.sa.

Sultan AlderaComputer Science Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia.

Mohammad AlkhatibComputer Science Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia.

Funding

Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University (IMSIU) IMSIU-DDRSP2503

6 · The paper itself

Abstract

Modern energy systems necessitate accurate peak demand predictions to minimize costs. This study presents an adaptive FastICA-Transformer that improves feature extraction and forecasting precision using entropy-guided component selection and a redesigned transformer architecture. The model modifies transformer depth according to the forecast horizon and initializes attention heads using statistically independent FastICA components, resulting in interpretable and efficient networks. In comparison to PCA-Transformer and sophisticated neural architectures (LSTM, TCN, N-BEATS, TFT), the FastICA-Transformer demonstrates enhanced MAE, RMSE, and MAPE in forecasting for Next-Hour, Next-Day, Next-Week, and Next-Quarter intervals. The results indicate that architectural adaptation, informed by temporal and statistical characteristics, efficiently tackles peak demand forecasts in practical power systems.

Indexed as

Adaptive transformerEntropy-guided feature selectionFastICA (Independent component analysis)Peak demand forecastingSmart grid operationsTime series forecasting

Identifiers

PMID41453991

DOI10.1038/s41598-025-33058-8

PMCPMC12830735

What OpenQuestion holds

Textmetadata

LicenceCC BY-NC-ND

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.