Evidence map›Paper›PMID 38057411›Full record

ArticleScientific data2023

High-resolution electric power load data of an industrial park with multiple types of buildings in China.

Kaile Zhou, Dingding Hu, Rong Hu, Jiong Zhou

Abstract readDataset
In one paragraph

Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Kaile ZhouSchool of Management, Hefei University of Technology, Hefei, 230009, China. zhoukaile@hfut.edu.cn.
Dingding HuSchool of Management, Hefei University of Technology, Hefei, 230009, China.ORCID 0000-0002-2341-4922
Rong HuSchool of Management, Hefei University of Technology, Hefei, 230009, China. hurong@mail.hfut.edu.cn.
Jiong ZhouJizhong Energy Technology Services (Shanghai) Company, Shanghai, 200040, China.

Funding

National Natural Science Foundation of China (National Science Foundation of China) 72188101National Natural Science Foundation of China (National Science Foundation of China) 72242103National Natural Science Foundation of China (National Science Foundation of China) 72271071
6 · The paper itself

Abstract

Considering the growing demand for electricity in industrial parks, understanding their electric power load patterns is critical for improving energy efficiency and ensuring the rational utilization of energy resources. However, the detailed electric power load data of various buildings in industrial parks are rarely available and accessible, which hinders the related studies. In this context, we present the electric power load data of 6 years (from January 1, 2016 to December 31, 2021) for various types of buildings in an industrial park in Suzhou, China. The data are obtained from smart meters and have various time resolutions (i.e., 5 minutes, 30 minutes, and 1 hour). This work describes the data collection, processing process, and different imputation methods. The high-resolution electric power load data can be used for various research tasks, including load prediction, load pattern recognition, anomaly detection, and demand response strategy development.

Identifiers

PMID38057411
PMCPMC10700648

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.