A short-term load forecasting framework for air conditioning system based on model stacking.

Tianjie Liu, Wenling Jiao, Zhiwei Huang, Jianting Yu, Xin Zhang, Luling Li

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.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

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

6 authors.

Tianjie LiuChina Shenzhen Gas Corporation Ltd, Shenzhen, 518040, China.

Wenling JiaoSchool of Architecture and Design, Harbin Institute of Technology, Key Laboratory of Cold Region Urban and Rural Human Settlement Environment Science and Technology, Ministry of Industry and Information Technology, Harbin, China. wljiao@163.com.

Zhiwei HuangChina Shenzhen Gas Corporation Ltd, Shenzhen, 518040, China.

Jianting YuChina Shenzhen Gas Corporation Ltd, Shenzhen, 518040, China.

Xin ZhangSchool of Architecture and Design, Harbin Institute of Technology, Key Laboratory of Cold Region Urban and Rural Human Settlement Environment Science and Technology, Ministry of Industry and Information Technology, Harbin, China.

Luling LiChina Shenzhen Gas Corporation Ltd, Shenzhen, 518040, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate short-term air conditioning load forecasting is very important for controlling air conditioning energy consumption, and is also a requisite for realizing intelligent control of air conditioning units. This paper proposes a short-term load forecasting framework for air conditioning based on the concept of model stacking, which combines six mature machine learning models, including Lasso regression, Ridge regression, Random Forest, Support Vector Regression, eXtreme Gradient Boosting and Long Short-Term Memory, and trains new prediction models through model stacking. In order to realize the short term forecast function of air conditioning load, this paper presents a complete forecast framework, which includes operational procedures for feature screening, algorithm hyperparameters optimization, and cross stacking of prediction models. A real air conditioning system is employed for prediction analysis, the prediction results showed that 28 out of 36 control simulations achieved better prediction accuracy, with an average increase in R

Indexed as

Air conditioning systemHyperparameter optimizationLoad forecastingModel stacking

Identifiers

PMID41125739

DOI10.1038/s41598-025-20929-3

PMCPMC12546722

What OpenQuestion holds

Textmetadata

LicenceCC BY-NC-ND

Read underepoch 390

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.