Empirical evaluation of feature processing strategies in deep residual networks for short-term load forecasting: a Malaysian tropical power system case study.
Liu; Ahmad; Samsudin; Hashim; Abidin Ab Kadir · 2026 · Scientific reports
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract (excerpt)
Short-term load forecasting (STLF) plays a critical role in ensuring the reliable and economical operation of power systems, particularly under complex and dynamic meteorological conditions. While Deep Residual Networks (DRNs) have…
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Metadata source: PubMed · DOI 10.1038/s41598-026-60033-8