Work

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

Year: 2025

Type: article

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.

Source: Scientific Reports

DOI: 10.1038/s41598-025-33058-8

Authors: Fahman Saeed, Sultan Aldera, Mohammad Alkhatib

Institution: Imam Mohammad ibn Saud Islamic University

Language: English

Topic: Energy Load and Power Forecasting

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure, Climate action

Open Access status: gold

Estimated APC paid: $2,690