Adaptive Clustering Long Short-Term Memory Network for Short-Term Power Load Forecasting Article Swipe
Yuanhang Qi
,
Haoyu Luo
,
Yuhui Luo
,
Rixu Liao
,
Liwei Ye
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/en16176230
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/en16176230
Short-term load forecasting (STLF) plays an important role in facilitating efficient and reliable operations of power systems and optimizing energy planning in the electricity market. To improve the accuracy of power load prediction, an adaptive clustering long short-term memory network is proposed to effectively combine the clustering process and prediction process. More specifically, the clustering process adopts the maximum deviation similarity criterion clustering algorithm (MDSC) as the clustering framework. A bee-foraging learning particle swarm optimization is further applied to realize the adaptive optimization of its hyperparameters. The prediction process consists of three parts: (i) a 9-dimensional load feature vector is proposed as the classification feature of SVM to obtain the load similarity cluster of the predicted days; (ii) the same kind of data are used as the training data of long short-term memory network; (iii) the trained network is used to predict the power load curve of the predicted day. Finally, experimental results are presented to show that the proposed scheme achieves an advantage in the prediction accuracy, where the mean absolute percentage error between predicted value and real value is only 8.05% for the first day.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/en16176230
- https://www.mdpi.com/1996-1073/16/17/6230/pdf?version=1693204463
- OA Status
- gold
- Cited By
- 6
- References
- 33
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4386220083
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4386220083Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/en16176230Digital Object Identifier
- Title
-
Adaptive Clustering Long Short-Term Memory Network for Short-Term Power Load ForecastingWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-08-28Full publication date if available
- Authors
-
Yuanhang Qi, Haoyu Luo, Yuhui Luo, Rixu Liao, Liwei YeList of authors in order
- Landing page
-
https://doi.org/10.3390/en16176230Publisher landing page
- PDF URL
-
https://www.mdpi.com/1996-1073/16/17/6230/pdf?version=1693204463Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/1996-1073/16/17/6230/pdf?version=1693204463Direct OA link when available
- Concepts
-
Cluster analysis, Particle swarm optimization, Computer science, Term (time), Similarity (geometry), Process (computing), Data mining, Artificial intelligence, Support vector machine, Machine learning, Quantum mechanics, Image (mathematics), Operating system, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
6Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 3, 2024: 3Per-year citation counts (last 5 years)
- References (count)
-
33Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4386220083 |
|---|---|
| doi | https://doi.org/10.3390/en16176230 |
| ids.doi | https://doi.org/10.3390/en16176230 |
| ids.openalex | https://openalex.org/W4386220083 |
| fwci | 0.99530339 |
| type | article |
| title | Adaptive Clustering Long Short-Term Memory Network for Short-Term Power Load Forecasting |
| awards[0].id | https://openalex.org/G5771903518 |
| awards[0].funder_id | https://openalex.org/F4320337111 |
| awards[0].display_name | |
| awards[0].funder_award_id | 2022A1515240058 |
| awards[0].funder_display_name | Basic and Applied Basic Research Foundation of Guangdong Province |
| biblio.issue | 17 |
| biblio.volume | 16 |
| biblio.last_page | 6230 |
| biblio.first_page | 6230 |
| topics[0].id | https://openalex.org/T11052 |
| topics[0].field.id | https://openalex.org/fields/22 |
| topics[0].field.display_name | Engineering |
| topics[0].score | 1.0 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2208 |
| topics[0].subfield.display_name | Electrical and Electronic Engineering |
| topics[0].display_name | Energy Load and Power Forecasting |
| topics[1].id | https://openalex.org/T10688 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9937000274658203 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1707 |
| topics[1].subfield.display_name | Computer Vision and Pattern Recognition |
| topics[1].display_name | Image and Signal Denoising Methods |
| topics[2].id | https://openalex.org/T12368 |
| topics[2].field.id | https://openalex.org/fields/18 |
| topics[2].field.display_name | Decision Sciences |
| topics[2].score | 0.9898999929428101 |
| topics[2].domain.id | https://openalex.org/domains/2 |
| topics[2].domain.display_name | Social Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1803 |
| topics[2].subfield.display_name | Management Science and Operations Research |
| topics[2].display_name | Grey System Theory Applications |
| funders[0].id | https://openalex.org/F4320337111 |
| funders[0].ror | |
| funders[0].display_name | Basic and Applied Basic Research Foundation of Guangdong Province |
| is_xpac | False |
| apc_list.value | 2200 |
| apc_list.currency | CHF |
| apc_list.value_usd | 2382 |
| apc_paid.value | 2200 |
| apc_paid.currency | CHF |
| apc_paid.value_usd | 2382 |
| concepts[0].id | https://openalex.org/C73555534 |
| concepts[0].level | 2 |
| concepts[0].score | 0.8109225034713745 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q622825 |
| concepts[0].display_name | Cluster analysis |
| concepts[1].id | https://openalex.org/C85617194 |
| concepts[1].level | 2 |
| concepts[1].score | 0.676815390586853 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q2072794 |
| concepts[1].display_name | Particle swarm optimization |
| concepts[2].id | https://openalex.org/C41008148 |
| concepts[2].level | 0 |
| concepts[2].score | 0.6327835321426392 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[2].display_name | Computer science |
| concepts[3].id | https://openalex.org/C61797465 |
| concepts[3].level | 2 |
| concepts[3].score | 0.6049378514289856 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q1188986 |
| concepts[3].display_name | Term (time) |
| concepts[4].id | https://openalex.org/C103278499 |
| concepts[4].level | 3 |
| concepts[4].score | 0.5171429514884949 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q254465 |
| concepts[4].display_name | Similarity (geometry) |
| concepts[5].id | https://openalex.org/C98045186 |
| concepts[5].level | 2 |
| concepts[5].score | 0.4515754282474518 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q205663 |
| concepts[5].display_name | Process (computing) |
| concepts[6].id | https://openalex.org/C124101348 |
| concepts[6].level | 1 |
| concepts[6].score | 0.4440884292125702 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q172491 |
| concepts[6].display_name | Data mining |
| concepts[7].id | https://openalex.org/C154945302 |
| concepts[7].level | 1 |
| concepts[7].score | 0.42631950974464417 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[7].display_name | Artificial intelligence |
| concepts[8].id | https://openalex.org/C12267149 |
| concepts[8].level | 2 |
| concepts[8].score | 0.4128965139389038 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q282453 |
| concepts[8].display_name | Support vector machine |
| concepts[9].id | https://openalex.org/C119857082 |
| concepts[9].level | 1 |
| concepts[9].score | 0.2915165424346924 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[9].display_name | Machine learning |
| concepts[10].id | https://openalex.org/C62520636 |
| concepts[10].level | 1 |
| concepts[10].score | 0.0 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q944 |
| concepts[10].display_name | Quantum mechanics |
| concepts[11].id | https://openalex.org/C115961682 |
| concepts[11].level | 2 |
| concepts[11].score | 0.0 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q860623 |
| concepts[11].display_name | Image (mathematics) |
| concepts[12].id | https://openalex.org/C111919701 |
| concepts[12].level | 1 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q9135 |
| concepts[12].display_name | Operating system |
| concepts[13].id | https://openalex.org/C121332964 |
| concepts[13].level | 0 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q413 |
| concepts[13].display_name | Physics |
| keywords[0].id | https://openalex.org/keywords/cluster-analysis |
| keywords[0].score | 0.8109225034713745 |
| keywords[0].display_name | Cluster analysis |
| keywords[1].id | https://openalex.org/keywords/particle-swarm-optimization |
| keywords[1].score | 0.676815390586853 |
| keywords[1].display_name | Particle swarm optimization |
| keywords[2].id | https://openalex.org/keywords/computer-science |
| keywords[2].score | 0.6327835321426392 |
| keywords[2].display_name | Computer science |
| keywords[3].id | https://openalex.org/keywords/term |
| keywords[3].score | 0.6049378514289856 |
| keywords[3].display_name | Term (time) |
| keywords[4].id | https://openalex.org/keywords/similarity |
| keywords[4].score | 0.5171429514884949 |
| keywords[4].display_name | Similarity (geometry) |
| keywords[5].id | https://openalex.org/keywords/process |
| keywords[5].score | 0.4515754282474518 |
| keywords[5].display_name | Process (computing) |
| keywords[6].id | https://openalex.org/keywords/data-mining |
| keywords[6].score | 0.4440884292125702 |
| keywords[6].display_name | Data mining |
| keywords[7].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[7].score | 0.42631950974464417 |
| keywords[7].display_name | Artificial intelligence |
| keywords[8].id | https://openalex.org/keywords/support-vector-machine |
| keywords[8].score | 0.4128965139389038 |
| keywords[8].display_name | Support vector machine |
| keywords[9].id | https://openalex.org/keywords/machine-learning |
| keywords[9].score | 0.2915165424346924 |
| keywords[9].display_name | Machine learning |
| language | en |
| locations[0].id | doi:10.3390/en16176230 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S198098182 |
| locations[0].source.issn | 1996-1073 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 1996-1073 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Energies |
| locations[0].source.host_organization | https://openalex.org/P4310310987 |
| locations[0].source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310310987 |
| locations[0].source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://www.mdpi.com/1996-1073/16/17/6230/pdf?version=1693204463 |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Energies |
| locations[0].landing_page_url | https://doi.org/10.3390/en16176230 |
| locations[1].id | pmh:oai:doaj.org/article:c1e412c4239444a0bfe6143317ffeb8d |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306401280 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[1].source.host_organization | |
| locations[1].source.host_organization_name | |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | submittedVersion |
| locations[1].raw_type | article |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | Energies, Vol 16, Iss 17, p 6230 (2023) |
| locations[1].landing_page_url | https://doaj.org/article/c1e412c4239444a0bfe6143317ffeb8d |
| locations[2].id | pmh:oai:mdpi.com:/1996-1073/16/17/6230/ |
| locations[2].is_oa | True |
| locations[2].source.id | https://openalex.org/S4306400947 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | True |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | MDPI (MDPI AG) |
| locations[2].source.host_organization | https://openalex.org/I4210097602 |
| locations[2].source.host_organization_name | Multidisciplinary Digital Publishing Institute (Switzerland) |
| locations[2].source.host_organization_lineage | https://openalex.org/I4210097602 |
| locations[2].license | cc-by |
| locations[2].pdf_url | |
| locations[2].version | submittedVersion |
| locations[2].raw_type | Text |
| locations[2].license_id | https://openalex.org/licenses/cc-by |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | Energies; Volume 16; Issue 17; Pages: 6230 |
| locations[2].landing_page_url | https://dx.doi.org/10.3390/en16176230 |
| indexed_in | crossref, doaj |
| authorships[0].author.id | https://openalex.org/A5044909340 |
| authorships[0].author.orcid | https://orcid.org/0000-0003-4336-8625 |
| authorships[0].author.display_name | Yuanhang Qi |
| authorships[0].countries | CN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I150229711 |
| authorships[0].affiliations[0].raw_affiliation_string | School of Computer Science, University of Electronic Science and Technology of China, Zhongshan Institute, Zhongshan 528402, China |
| authorships[0].affiliations[1].institution_ids | https://openalex.org/I139024713 |
| authorships[0].affiliations[1].raw_affiliation_string | School of Automation, Guangdong University of Technology, Guangzhou 510006, China |
| authorships[0].institutions[0].id | https://openalex.org/I139024713 |
| authorships[0].institutions[0].ror | https://ror.org/04azbjn80 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I139024713 |
| authorships[0].institutions[0].country_code | CN |
| authorships[0].institutions[0].display_name | Guangdong University of Technology |
| authorships[0].institutions[1].id | https://openalex.org/I150229711 |
| authorships[0].institutions[1].ror | https://ror.org/04qr3zq92 |
| authorships[0].institutions[1].type | education |
| authorships[0].institutions[1].lineage | https://openalex.org/I150229711 |
| authorships[0].institutions[1].country_code | CN |
| authorships[0].institutions[1].display_name | University of Electronic Science and Technology of China |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Yuanhang Qi |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | School of Automation, Guangdong University of Technology, Guangzhou 510006, China, School of Computer Science, University of Electronic Science and Technology of China, Zhongshan Institute, Zhongshan 528402, China |
| authorships[1].author.id | https://openalex.org/A5024329311 |
| authorships[1].author.orcid | |
| authorships[1].author.display_name | Haoyu Luo |
| authorships[1].countries | CN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I139024713 |
| authorships[1].affiliations[0].raw_affiliation_string | School of Automation, Guangdong University of Technology, Guangzhou 510006, China |
| authorships[1].affiliations[1].institution_ids | https://openalex.org/I150229711 |
| authorships[1].affiliations[1].raw_affiliation_string | School of Computer Science, University of Electronic Science and Technology of China, Zhongshan Institute, Zhongshan 528402, China |
| authorships[1].institutions[0].id | https://openalex.org/I139024713 |
| authorships[1].institutions[0].ror | https://ror.org/04azbjn80 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I139024713 |
| authorships[1].institutions[0].country_code | CN |
| authorships[1].institutions[0].display_name | Guangdong University of Technology |
| authorships[1].institutions[1].id | https://openalex.org/I150229711 |
| authorships[1].institutions[1].ror | https://ror.org/04qr3zq92 |
| authorships[1].institutions[1].type | education |
| authorships[1].institutions[1].lineage | https://openalex.org/I150229711 |
| authorships[1].institutions[1].country_code | CN |
| authorships[1].institutions[1].display_name | University of Electronic Science and Technology of China |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Haoyu Luo |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | School of Automation, Guangdong University of Technology, Guangzhou 510006, China, School of Computer Science, University of Electronic Science and Technology of China, Zhongshan Institute, Zhongshan 528402, China |
| authorships[2].author.id | https://openalex.org/A5102247298 |
| authorships[2].author.orcid | |
| authorships[2].author.display_name | Yuhui Luo |
| authorships[2].countries | CN |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I139024713 |
| authorships[2].affiliations[0].raw_affiliation_string | School of Automation, Guangdong University of Technology, Guangzhou 510006, China |
| authorships[2].institutions[0].id | https://openalex.org/I139024713 |
| authorships[2].institutions[0].ror | https://ror.org/04azbjn80 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I139024713 |
| authorships[2].institutions[0].country_code | CN |
| authorships[2].institutions[0].display_name | Guangdong University of Technology |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Yuhui Luo |
| authorships[2].is_corresponding | True |
| authorships[2].raw_affiliation_strings | School of Automation, Guangdong University of Technology, Guangzhou 510006, China |
| authorships[3].author.id | https://openalex.org/A5108859772 |
| authorships[3].author.orcid | |
| authorships[3].author.display_name | Rixu Liao |
| authorships[3].countries | CN |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I4210164009 |
| authorships[3].affiliations[0].raw_affiliation_string | School of Accountancy, Guangdong Baiyun University, Guangzhou 510550, China |
| authorships[3].institutions[0].id | https://openalex.org/I4210164009 |
| authorships[3].institutions[0].ror | https://ror.org/04wmrj902 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I4210164009 |
| authorships[3].institutions[0].country_code | CN |
| authorships[3].institutions[0].display_name | Guangdong Baiyun University |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Rixu Liao |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | School of Accountancy, Guangdong Baiyun University, Guangzhou 510550, China |
| authorships[4].author.id | https://openalex.org/A5104161611 |
| authorships[4].author.orcid | |
| authorships[4].author.display_name | Liwei Ye |
| authorships[4].countries | CN |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I150229711 |
| authorships[4].affiliations[0].raw_affiliation_string | School of Computer Science, University of Electronic Science and Technology of China, Zhongshan Institute, Zhongshan 528402, China |
| authorships[4].institutions[0].id | https://openalex.org/I150229711 |
| authorships[4].institutions[0].ror | https://ror.org/04qr3zq92 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I150229711 |
| authorships[4].institutions[0].country_code | CN |
| authorships[4].institutions[0].display_name | University of Electronic Science and Technology of China |
| authorships[4].author_position | last |
| authorships[4].raw_author_name | Liwei Ye |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | School of Computer Science, University of Electronic Science and Technology of China, Zhongshan Institute, Zhongshan 528402, China |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.mdpi.com/1996-1073/16/17/6230/pdf?version=1693204463 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Adaptive Clustering Long Short-Term Memory Network for Short-Term Power Load Forecasting |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T11052 |
| primary_topic.field.id | https://openalex.org/fields/22 |
| primary_topic.field.display_name | Engineering |
| primary_topic.score | 1.0 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2208 |
| primary_topic.subfield.display_name | Electrical and Electronic Engineering |
| primary_topic.display_name | Energy Load and Power Forecasting |
| related_works | https://openalex.org/W2090763504, https://openalex.org/W148178222, https://openalex.org/W2104657898, https://openalex.org/W1948992892, https://openalex.org/W1886884218, https://openalex.org/W2804364458, https://openalex.org/W1910826599, https://openalex.org/W42295635, https://openalex.org/W1980100242, https://openalex.org/W2530420969 |
| cited_by_count | 6 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 3 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 3 |
| locations_count | 3 |
| best_oa_location.id | doi:10.3390/en16176230 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S198098182 |
| best_oa_location.source.issn | 1996-1073 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 1996-1073 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Energies |
| best_oa_location.source.host_organization | https://openalex.org/P4310310987 |
| best_oa_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| best_oa_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://www.mdpi.com/1996-1073/16/17/6230/pdf?version=1693204463 |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Energies |
| best_oa_location.landing_page_url | https://doi.org/10.3390/en16176230 |
| primary_location.id | doi:10.3390/en16176230 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S198098182 |
| primary_location.source.issn | 1996-1073 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 1996-1073 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Energies |
| primary_location.source.host_organization | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://www.mdpi.com/1996-1073/16/17/6230/pdf?version=1693204463 |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Energies |
| primary_location.landing_page_url | https://doi.org/10.3390/en16176230 |
| publication_date | 2023-08-28 |
| publication_year | 2023 |
| referenced_works | https://openalex.org/W2107025615, https://openalex.org/W6851738588, https://openalex.org/W6851626264, https://openalex.org/W2601171548, https://openalex.org/W1720804347, https://openalex.org/W2288083718, https://openalex.org/W2080937097, https://openalex.org/W1982660753, https://openalex.org/W2081060955, https://openalex.org/W2064675550, https://openalex.org/W2754252319, https://openalex.org/W2909670596, https://openalex.org/W3033406500, https://openalex.org/W2893915728, https://openalex.org/W2064604455, https://openalex.org/W2126751256, https://openalex.org/W3116475140, https://openalex.org/W3198485590, https://openalex.org/W6850350628, https://openalex.org/W4321236319, https://openalex.org/W3212175142, https://openalex.org/W1984779822, https://openalex.org/W2941542684, https://openalex.org/W4283448690, https://openalex.org/W3122970619, https://openalex.org/W2118840131, https://openalex.org/W2320438893, https://openalex.org/W3015280312, https://openalex.org/W4311955642, https://openalex.org/W4310387075, https://openalex.org/W4365815040, https://openalex.org/W4365814803, https://openalex.org/W4323767819 |
| referenced_works_count | 33 |
| abstract_inverted_index.A | 69 |
| abstract_inverted_index.a | 94 |
| abstract_inverted_index.To | 25 |
| abstract_inverted_index.an | 5, 33, 162 |
| abstract_inverted_index.as | 65, 101, 125 |
| abstract_inverted_index.in | 8, 21, 164 |
| abstract_inverted_index.is | 40, 75, 99, 138, 180 |
| abstract_inverted_index.of | 14, 29, 83, 90, 105, 113, 121, 129, 146 |
| abstract_inverted_index.to | 42, 78, 107, 140, 155 |
| abstract_inverted_index.(i) | 93 |
| abstract_inverted_index.SVM | 106 |
| abstract_inverted_index.The | 86 |
| abstract_inverted_index.and | 11, 17, 48, 177 |
| abstract_inverted_index.are | 123, 153 |
| abstract_inverted_index.for | 183 |
| abstract_inverted_index.its | 84 |
| abstract_inverted_index.the | 22, 27, 45, 53, 57, 66, 80, 102, 109, 114, 118, 126, 135, 142, 147, 158, 165, 169, 184 |
| abstract_inverted_index.(ii) | 117 |
| abstract_inverted_index.More | 51 |
| abstract_inverted_index.data | 122, 128 |
| abstract_inverted_index.day. | 149, 186 |
| abstract_inverted_index.kind | 120 |
| abstract_inverted_index.load | 1, 31, 96, 110, 144 |
| abstract_inverted_index.long | 36, 130 |
| abstract_inverted_index.mean | 170 |
| abstract_inverted_index.only | 181 |
| abstract_inverted_index.real | 178 |
| abstract_inverted_index.role | 7 |
| abstract_inverted_index.same | 119 |
| abstract_inverted_index.show | 156 |
| abstract_inverted_index.that | 157 |
| abstract_inverted_index.used | 124, 139 |
| abstract_inverted_index.(iii) | 134 |
| abstract_inverted_index.8.05% | 182 |
| abstract_inverted_index.curve | 145 |
| abstract_inverted_index.days; | 116 |
| abstract_inverted_index.error | 173 |
| abstract_inverted_index.first | 185 |
| abstract_inverted_index.plays | 4 |
| abstract_inverted_index.power | 15, 30, 143 |
| abstract_inverted_index.swarm | 73 |
| abstract_inverted_index.three | 91 |
| abstract_inverted_index.value | 176, 179 |
| abstract_inverted_index.where | 168 |
| abstract_inverted_index.(MDSC) | 64 |
| abstract_inverted_index.(STLF) | 3 |
| abstract_inverted_index.adopts | 56 |
| abstract_inverted_index.energy | 19 |
| abstract_inverted_index.memory | 38, 132 |
| abstract_inverted_index.obtain | 108 |
| abstract_inverted_index.parts: | 92 |
| abstract_inverted_index.scheme | 160 |
| abstract_inverted_index.vector | 98 |
| abstract_inverted_index.applied | 77 |
| abstract_inverted_index.between | 174 |
| abstract_inverted_index.cluster | 112 |
| abstract_inverted_index.combine | 44 |
| abstract_inverted_index.feature | 97, 104 |
| abstract_inverted_index.further | 76 |
| abstract_inverted_index.improve | 26 |
| abstract_inverted_index.market. | 24 |
| abstract_inverted_index.maximum | 58 |
| abstract_inverted_index.network | 39, 137 |
| abstract_inverted_index.predict | 141 |
| abstract_inverted_index.process | 47, 55, 88 |
| abstract_inverted_index.realize | 79 |
| abstract_inverted_index.results | 152 |
| abstract_inverted_index.systems | 16 |
| abstract_inverted_index.trained | 136 |
| abstract_inverted_index.Finally, | 150 |
| abstract_inverted_index.absolute | 171 |
| abstract_inverted_index.accuracy | 28 |
| abstract_inverted_index.achieves | 161 |
| abstract_inverted_index.adaptive | 34, 81 |
| abstract_inverted_index.consists | 89 |
| abstract_inverted_index.learning | 71 |
| abstract_inverted_index.network; | 133 |
| abstract_inverted_index.particle | 72 |
| abstract_inverted_index.planning | 20 |
| abstract_inverted_index.process. | 50 |
| abstract_inverted_index.proposed | 41, 100, 159 |
| abstract_inverted_index.reliable | 12 |
| abstract_inverted_index.training | 127 |
| abstract_inverted_index.accuracy, | 167 |
| abstract_inverted_index.advantage | 163 |
| abstract_inverted_index.algorithm | 63 |
| abstract_inverted_index.criterion | 61 |
| abstract_inverted_index.deviation | 59 |
| abstract_inverted_index.efficient | 10 |
| abstract_inverted_index.important | 6 |
| abstract_inverted_index.predicted | 115, 148, 175 |
| abstract_inverted_index.presented | 154 |
| abstract_inverted_index.Short-term | 0 |
| abstract_inverted_index.clustering | 35, 46, 54, 62, 67 |
| abstract_inverted_index.framework. | 68 |
| abstract_inverted_index.operations | 13 |
| abstract_inverted_index.optimizing | 18 |
| abstract_inverted_index.percentage | 172 |
| abstract_inverted_index.prediction | 49, 87, 166 |
| abstract_inverted_index.short-term | 37, 131 |
| abstract_inverted_index.similarity | 60, 111 |
| abstract_inverted_index.effectively | 43 |
| abstract_inverted_index.electricity | 23 |
| abstract_inverted_index.forecasting | 2 |
| abstract_inverted_index.prediction, | 32 |
| abstract_inverted_index.bee-foraging | 70 |
| abstract_inverted_index.experimental | 151 |
| abstract_inverted_index.facilitating | 9 |
| abstract_inverted_index.optimization | 74, 82 |
| abstract_inverted_index.9-dimensional | 95 |
| abstract_inverted_index.specifically, | 52 |
| abstract_inverted_index.classification | 103 |
| abstract_inverted_index.hyperparameters. | 85 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 96 |
| corresponding_author_ids | https://openalex.org/A5102247298 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| corresponding_institution_ids | https://openalex.org/I139024713 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.9100000262260437 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile.value | 0.75106849 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |