GCA-BULF is a bottom-up STLF framework that filters and groups critical appliances for collaborative forecasting, reporting 20.85-57.88% better hourly accuracy than top-down methods on residential and office data.
Electric vehicle route optimization considering time-of-use electricity price by learnable partheno-genetic algorithm
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
The b-LAHC algorithm sets 9/10 new best-known results on large-scale E-CVRP benchmarks by using a surrogate-guided bilevel framework with fixed parameters.
citing papers explorer
-
GCA-BULF: A Bottom-Up Framework for Short-Term Load Forecasting Using Grouped Critical Appliances
GCA-BULF is a bottom-up STLF framework that filters and groups critical appliances for collaborative forecasting, reporting 20.85-57.88% better hourly accuracy than top-down methods on residential and office data.
-
Bilevel Late Acceptance Hill Climbing for the Electric Capacitated Vehicle Routing Problem
The b-LAHC algorithm sets 9/10 new best-known results on large-scale E-CVRP benchmarks by using a surrogate-guided bilevel framework with fixed parameters.