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Data · dataset · 2026

XGBoost, LightGBM, and CatBoost.

Listed in ScienceDB

Accurate forecasting of building energy consumption is important for improving energy management and supporting more efficient building operation.

Description

In this study, we investigate a sequential metaheuristic hyperparameter optimization framework for three widely used gradient-boosting models, namely XGBoost, LightGBM, and CatBoost. Two optimization pathways, Grey Wolf Optimizer–Particle Swarm Optimization (GWO–PSO) and Ant Lion Optimizer–Moth-Flame Optimization (ALO–MFO), are evaluated under consistent experimental conditions.

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Where it is published

Catalogue records · 1

Topics

Inferred from text
Building 74%
Provenance · 1 source records, 11 field assertions
SourceKeyLast seenRaw
ScienceDB10.57760/sciencedb.010qw7 d agoJSON v1
FieldAssertionExtractorEvidence
access_levelsource · scidb cnconnector:scidb_cn@1.0.0
concepts[field].anzsrc:group:3302enrichment · scidb cntaxonomy-embedding@1.0.0title+keywords+description (74%)
concepts[field].local:field:earth-environmentalmapping · scidb cnconnector:scidb_cn@1.0.0
concepts[field].local:field:engineeringmapping · scidb cnconnector:scidb_cn@1.0.0
concepts[field].local:field:humanitiesmapping · scidb cnconnector:scidb_cn@1.0.0
concepts[field].local:field:life-sciencesmapping · scidb cnconnector:scidb_cn@1.0.0
concepts[field].local:field:social-sciencemapping · scidb cnconnector:scidb_cn@1.0.0
descriptionsource · scidb cnconnector:scidb_cn@1.0.0/metadata/dc/description
license_textsource · scidb cnconnector:scidb_cn@1.0.0
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