A Combined Method for Short-Term Load Forecasting Considering the Characteristics of Components of Seasonal and Trend Decomposition Using Local Regression Article Swipe
Sile Hu
,
Yuan Wang
,
Wen‐Bin Cai
,
Yuan Yu
,
Chao Chen
,
Jiaqiang Yang
,
Yucan Zhao
,
Yuan Gao
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/app14062286
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/app14062286
In response to the complexity and high volatility of original load data affecting the accuracy of load forecasting, a combined method for short-term load forecasting considering the characteristics of components of seasonal and trend decomposition using local regression (STL) is proposed. The original load data are decomposed into a trend component, seasonal component, and residual component using STL. Then, considering the characteristics of each component, a long short-term memory (LSTM) neural network, a convolutional neural network (CNN), and Gaussian process regression (GPR) are used to predict the trend component, seasonal component, and residual component, respectively. The final outcome of the load forecasting is obtained by summing the forecasted results of each individual component. A specific case study is conducted to compare the proposed combined method with LSTM, CNN, GPR, STL-LSTM, STL-CNN, and STL-GPR prediction methods. Through comparison, the proposed combined method exhibits lower errors and higher accuracy, demonstrating the effectiveness of this method.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/app14062286
- https://www.mdpi.com/2076-3417/14/6/2286/pdf?version=1709906830
- OA Status
- gold
- Cited By
- 4
- References
- 20
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4392600777
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4392600777Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/app14062286Digital Object Identifier
- Title
-
A Combined Method for Short-Term Load Forecasting Considering the Characteristics of Components of Seasonal and Trend Decomposition Using Local RegressionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-03-08Full publication date if available
- Authors
-
Sile Hu, Yuan Wang, Wen‐Bin Cai, Yuan Yu, Chao Chen, Jiaqiang Yang, Yucan Zhao, Yuan GaoList of authors in order
- Landing page
-
https://doi.org/10.3390/app14062286Publisher landing page
- PDF URL
-
https://www.mdpi.com/2076-3417/14/6/2286/pdf?version=1709906830Direct 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/2076-3417/14/6/2286/pdf?version=1709906830Direct OA link when available
- Concepts
-
Term (time), Econometrics, Statistics, Regression analysis, Decomposition, Environmental science, Mathematics, Computer science, Biology, Ecology, Physics, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
4Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 3, 2024: 1Per-year citation counts (last 5 years)
- References (count)
-
20Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4392600777 |
|---|---|
| doi | https://doi.org/10.3390/app14062286 |
| ids.doi | https://doi.org/10.3390/app14062286 |
| ids.openalex | https://openalex.org/W4392600777 |
| fwci | 1.47667389 |
| type | article |
| title | A Combined Method for Short-Term Load Forecasting Considering the Characteristics of Components of Seasonal and Trend Decomposition Using Local Regression |
| biblio.issue | 6 |
| biblio.volume | 14 |
| biblio.last_page | 2286 |
| biblio.first_page | 2286 |
| 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 | 0.9991000294685364 |
| 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/T11490 |
| topics[1].field.id | https://openalex.org/fields/23 |
| topics[1].field.display_name | Environmental Science |
| topics[1].score | 0.9944000244140625 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2305 |
| topics[1].subfield.display_name | Environmental Engineering |
| topics[1].display_name | Hydrological Forecasting Using AI |
| topics[2].id | https://openalex.org/T13955 |
| topics[2].field.id | https://openalex.org/fields/23 |
| topics[2].field.display_name | Environmental Science |
| topics[2].score | 0.9648000001907349 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2302 |
| topics[2].subfield.display_name | Ecological Modeling |
| topics[2].display_name | Evaluation Methods in Various Fields |
| is_xpac | False |
| apc_list.value | 2300 |
| apc_list.currency | CHF |
| apc_list.value_usd | 2490 |
| apc_paid.value | 2300 |
| apc_paid.currency | CHF |
| apc_paid.value_usd | 2490 |
| concepts[0].id | https://openalex.org/C61797465 |
| concepts[0].level | 2 |
| concepts[0].score | 0.6457687616348267 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q1188986 |
| concepts[0].display_name | Term (time) |
| concepts[1].id | https://openalex.org/C149782125 |
| concepts[1].level | 1 |
| concepts[1].score | 0.5144517421722412 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q160039 |
| concepts[1].display_name | Econometrics |
| concepts[2].id | https://openalex.org/C105795698 |
| concepts[2].level | 1 |
| concepts[2].score | 0.435986191034317 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q12483 |
| concepts[2].display_name | Statistics |
| concepts[3].id | https://openalex.org/C152877465 |
| concepts[3].level | 2 |
| concepts[3].score | 0.4230203628540039 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q208042 |
| concepts[3].display_name | Regression analysis |
| concepts[4].id | https://openalex.org/C124681953 |
| concepts[4].level | 2 |
| concepts[4].score | 0.4149148166179657 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q339062 |
| concepts[4].display_name | Decomposition |
| concepts[5].id | https://openalex.org/C39432304 |
| concepts[5].level | 0 |
| concepts[5].score | 0.34488290548324585 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q188847 |
| concepts[5].display_name | Environmental science |
| concepts[6].id | https://openalex.org/C33923547 |
| concepts[6].level | 0 |
| concepts[6].score | 0.33940330147743225 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[6].display_name | Mathematics |
| concepts[7].id | https://openalex.org/C41008148 |
| concepts[7].level | 0 |
| concepts[7].score | 0.32187700271606445 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[7].display_name | Computer science |
| concepts[8].id | https://openalex.org/C86803240 |
| concepts[8].level | 0 |
| concepts[8].score | 0.0 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q420 |
| concepts[8].display_name | Biology |
| concepts[9].id | https://openalex.org/C18903297 |
| concepts[9].level | 1 |
| concepts[9].score | 0.0 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q7150 |
| concepts[9].display_name | Ecology |
| concepts[10].id | https://openalex.org/C121332964 |
| concepts[10].level | 0 |
| concepts[10].score | 0.0 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q413 |
| concepts[10].display_name | Physics |
| concepts[11].id | https://openalex.org/C62520636 |
| concepts[11].level | 1 |
| concepts[11].score | 0.0 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q944 |
| concepts[11].display_name | Quantum mechanics |
| keywords[0].id | https://openalex.org/keywords/term |
| keywords[0].score | 0.6457687616348267 |
| keywords[0].display_name | Term (time) |
| keywords[1].id | https://openalex.org/keywords/econometrics |
| keywords[1].score | 0.5144517421722412 |
| keywords[1].display_name | Econometrics |
| keywords[2].id | https://openalex.org/keywords/statistics |
| keywords[2].score | 0.435986191034317 |
| keywords[2].display_name | Statistics |
| keywords[3].id | https://openalex.org/keywords/regression-analysis |
| keywords[3].score | 0.4230203628540039 |
| keywords[3].display_name | Regression analysis |
| keywords[4].id | https://openalex.org/keywords/decomposition |
| keywords[4].score | 0.4149148166179657 |
| keywords[4].display_name | Decomposition |
| keywords[5].id | https://openalex.org/keywords/environmental-science |
| keywords[5].score | 0.34488290548324585 |
| keywords[5].display_name | Environmental science |
| keywords[6].id | https://openalex.org/keywords/mathematics |
| keywords[6].score | 0.33940330147743225 |
| keywords[6].display_name | Mathematics |
| keywords[7].id | https://openalex.org/keywords/computer-science |
| keywords[7].score | 0.32187700271606445 |
| keywords[7].display_name | Computer science |
| language | en |
| locations[0].id | doi:10.3390/app14062286 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4210205812 |
| locations[0].source.issn | 2076-3417 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 2076-3417 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Applied Sciences |
| 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/2076-3417/14/6/2286/pdf?version=1709906830 |
| 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 | Applied Sciences |
| locations[0].landing_page_url | https://doi.org/10.3390/app14062286 |
| locations[1].id | pmh:oai:doaj.org/article:682e9a9ca6d343609b1a2cc738d231e8 |
| 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 | Applied Sciences, Vol 14, Iss 6, p 2286 (2024) |
| locations[1].landing_page_url | https://doaj.org/article/682e9a9ca6d343609b1a2cc738d231e8 |
| indexed_in | crossref, doaj |
| authorships[0].author.id | https://openalex.org/A5102707474 |
| authorships[0].author.orcid | |
| authorships[0].author.display_name | Sile Hu |
| authorships[0].countries | CN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I76130692 |
| authorships[0].affiliations[0].raw_affiliation_string | College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China |
| authorships[0].affiliations[1].institution_ids | https://openalex.org/I4210088511 |
| authorships[0].affiliations[1].raw_affiliation_string | Inner Mongolia Power (Group) Co., Ltd., Hohhot 010020, China |
| authorships[0].institutions[0].id | https://openalex.org/I4210088511 |
| authorships[0].institutions[0].ror | https://ror.org/0041szh84 |
| authorships[0].institutions[0].type | company |
| authorships[0].institutions[0].lineage | https://openalex.org/I4210088511 |
| authorships[0].institutions[0].country_code | CN |
| authorships[0].institutions[0].display_name | Inner Mongolia Electric Power (China) |
| authorships[0].institutions[1].id | https://openalex.org/I76130692 |
| authorships[0].institutions[1].ror | https://ror.org/00a2xv884 |
| authorships[0].institutions[1].type | education |
| authorships[0].institutions[1].lineage | https://openalex.org/I76130692 |
| authorships[0].institutions[1].country_code | CN |
| authorships[0].institutions[1].display_name | Zhejiang University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Sile Hu |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China, Inner Mongolia Power (Group) Co., Ltd., Hohhot 010020, China |
| authorships[1].author.id | https://openalex.org/A5002438548 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-4951-4286 |
| authorships[1].author.display_name | Yuan Wang |
| authorships[1].countries | CN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I4210088511 |
| authorships[1].affiliations[0].raw_affiliation_string | Inner Mongolia Electric Power Economic and Technological Research Institute, Hohhot 010090, China |
| authorships[1].institutions[0].id | https://openalex.org/I4210088511 |
| authorships[1].institutions[0].ror | https://ror.org/0041szh84 |
| authorships[1].institutions[0].type | company |
| authorships[1].institutions[0].lineage | https://openalex.org/I4210088511 |
| authorships[1].institutions[0].country_code | CN |
| authorships[1].institutions[0].display_name | Inner Mongolia Electric Power (China) |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Yuan Wang |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Inner Mongolia Electric Power Economic and Technological Research Institute, Hohhot 010090, China |
| authorships[2].author.id | https://openalex.org/A5068660364 |
| authorships[2].author.orcid | https://orcid.org/0000-0003-0500-4791 |
| authorships[2].author.display_name | Wen‐Bin Cai |
| authorships[2].countries | CN |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I4210088511 |
| authorships[2].affiliations[0].raw_affiliation_string | Inner Mongolia Electric Power Economic and Technological Research Institute, Hohhot 010090, China |
| authorships[2].institutions[0].id | https://openalex.org/I4210088511 |
| authorships[2].institutions[0].ror | https://ror.org/0041szh84 |
| authorships[2].institutions[0].type | company |
| authorships[2].institutions[0].lineage | https://openalex.org/I4210088511 |
| authorships[2].institutions[0].country_code | CN |
| authorships[2].institutions[0].display_name | Inner Mongolia Electric Power (China) |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Wenbin Cai |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Inner Mongolia Electric Power Economic and Technological Research Institute, Hohhot 010090, China |
| authorships[3].author.id | https://openalex.org/A5100444873 |
| authorships[3].author.orcid | |
| authorships[3].author.display_name | Yuan Yu |
| authorships[3].countries | CN |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I4210088511 |
| authorships[3].affiliations[0].raw_affiliation_string | Inner Mongolia Power (Group) Co., Ltd., Hohhot 010020, China |
| authorships[3].institutions[0].id | https://openalex.org/I4210088511 |
| authorships[3].institutions[0].ror | https://ror.org/0041szh84 |
| authorships[3].institutions[0].type | company |
| authorships[3].institutions[0].lineage | https://openalex.org/I4210088511 |
| authorships[3].institutions[0].country_code | CN |
| authorships[3].institutions[0].display_name | Inner Mongolia Electric Power (China) |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Yuan Yu |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Inner Mongolia Power (Group) Co., Ltd., Hohhot 010020, China |
| authorships[4].author.id | https://openalex.org/A5100408406 |
| authorships[4].author.orcid | https://orcid.org/0000-0003-3080-052X |
| authorships[4].author.display_name | Chao Chen |
| authorships[4].countries | CN |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I76130692 |
| authorships[4].affiliations[0].raw_affiliation_string | College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China |
| authorships[4].institutions[0].id | https://openalex.org/I76130692 |
| authorships[4].institutions[0].ror | https://ror.org/00a2xv884 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I76130692 |
| authorships[4].institutions[0].country_code | CN |
| authorships[4].institutions[0].display_name | Zhejiang University |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Chao Chen |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China |
| authorships[5].author.id | https://openalex.org/A5004621631 |
| authorships[5].author.orcid | https://orcid.org/0000-0002-3822-3301 |
| authorships[5].author.display_name | Jiaqiang Yang |
| authorships[5].countries | CN |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I76130692 |
| authorships[5].affiliations[0].raw_affiliation_string | College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China |
| authorships[5].institutions[0].id | https://openalex.org/I76130692 |
| authorships[5].institutions[0].ror | https://ror.org/00a2xv884 |
| authorships[5].institutions[0].type | education |
| authorships[5].institutions[0].lineage | https://openalex.org/I76130692 |
| authorships[5].institutions[0].country_code | CN |
| authorships[5].institutions[0].display_name | Zhejiang University |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Jiaqiang Yang |
| authorships[5].is_corresponding | True |
| authorships[5].raw_affiliation_strings | College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China |
| authorships[6].author.id | https://openalex.org/A5089646265 |
| authorships[6].author.orcid | https://orcid.org/0009-0004-0394-5779 |
| authorships[6].author.display_name | Yucan Zhao |
| authorships[6].countries | CN |
| authorships[6].affiliations[0].institution_ids | https://openalex.org/I76130692 |
| authorships[6].affiliations[0].raw_affiliation_string | College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China |
| authorships[6].institutions[0].id | https://openalex.org/I76130692 |
| authorships[6].institutions[0].ror | https://ror.org/00a2xv884 |
| authorships[6].institutions[0].type | education |
| authorships[6].institutions[0].lineage | https://openalex.org/I76130692 |
| authorships[6].institutions[0].country_code | CN |
| authorships[6].institutions[0].display_name | Zhejiang University |
| authorships[6].author_position | middle |
| authorships[6].raw_author_name | Yucan Zhao |
| authorships[6].is_corresponding | False |
| authorships[6].raw_affiliation_strings | College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China |
| authorships[7].author.id | https://openalex.org/A5075642035 |
| authorships[7].author.orcid | https://orcid.org/0000-0002-0832-2982 |
| authorships[7].author.display_name | Yuan Gao |
| authorships[7].countries | CN |
| authorships[7].affiliations[0].institution_ids | https://openalex.org/I76130692 |
| authorships[7].affiliations[0].raw_affiliation_string | College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China |
| authorships[7].institutions[0].id | https://openalex.org/I76130692 |
| authorships[7].institutions[0].ror | https://ror.org/00a2xv884 |
| authorships[7].institutions[0].type | education |
| authorships[7].institutions[0].lineage | https://openalex.org/I76130692 |
| authorships[7].institutions[0].country_code | CN |
| authorships[7].institutions[0].display_name | Zhejiang University |
| authorships[7].author_position | last |
| authorships[7].raw_author_name | Yuan Gao |
| authorships[7].is_corresponding | False |
| authorships[7].raw_affiliation_strings | College of Electrical Engineering, Zhejiang University, Hangzhou 310027, 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/2076-3417/14/6/2286/pdf?version=1709906830 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | A Combined Method for Short-Term Load Forecasting Considering the Characteristics of Components of Seasonal and Trend Decomposition Using Local Regression |
| 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 | 0.9991000294685364 |
| 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/W42295635, https://openalex.org/W1973996291, https://openalex.org/W2330575325, https://openalex.org/W4388747848, https://openalex.org/W2163803519, https://openalex.org/W2497592525, https://openalex.org/W3096145648, https://openalex.org/W3197510923, https://openalex.org/W2370579019, https://openalex.org/W4381136829 |
| cited_by_count | 4 |
| 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 | 1 |
| locations_count | 2 |
| best_oa_location.id | doi:10.3390/app14062286 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4210205812 |
| best_oa_location.source.issn | 2076-3417 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 2076-3417 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Applied Sciences |
| 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/2076-3417/14/6/2286/pdf?version=1709906830 |
| 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 | Applied Sciences |
| best_oa_location.landing_page_url | https://doi.org/10.3390/app14062286 |
| primary_location.id | doi:10.3390/app14062286 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4210205812 |
| primary_location.source.issn | 2076-3417 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2076-3417 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Applied Sciences |
| 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/2076-3417/14/6/2286/pdf?version=1709906830 |
| 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 | Applied Sciences |
| primary_location.landing_page_url | https://doi.org/10.3390/app14062286 |
| publication_date | 2024-03-08 |
| publication_year | 2024 |
| referenced_works | https://openalex.org/W2275088575, https://openalex.org/W4225243427, https://openalex.org/W3172514114, https://openalex.org/W2744106784, https://openalex.org/W2884486887, https://openalex.org/W3198895442, https://openalex.org/W4322631283, https://openalex.org/W4381660726, https://openalex.org/W2032161710, https://openalex.org/W2133752269, https://openalex.org/W2007221293, https://openalex.org/W2990830181, https://openalex.org/W2909670596, https://openalex.org/W3085910649, https://openalex.org/W4362680966, https://openalex.org/W4310001529, https://openalex.org/W2064675550, https://openalex.org/W4388919068, https://openalex.org/W3142394151, https://openalex.org/W3040297040 |
| referenced_works_count | 20 |
| abstract_inverted_index.A | 113 |
| abstract_inverted_index.a | 18, 48, 65, 72 |
| abstract_inverted_index.In | 0 |
| abstract_inverted_index.by | 104 |
| abstract_inverted_index.is | 39, 102, 117 |
| abstract_inverted_index.of | 8, 15, 28, 30, 62, 98, 109, 150 |
| abstract_inverted_index.to | 2, 84, 119 |
| abstract_inverted_index.The | 41, 95 |
| abstract_inverted_index.and | 5, 32, 53, 77, 91, 131, 144 |
| abstract_inverted_index.are | 45, 82 |
| abstract_inverted_index.for | 21 |
| abstract_inverted_index.the | 3, 13, 26, 60, 86, 99, 106, 121, 137, 148 |
| abstract_inverted_index.CNN, | 127 |
| abstract_inverted_index.GPR, | 128 |
| abstract_inverted_index.STL. | 57 |
| abstract_inverted_index.case | 115 |
| abstract_inverted_index.data | 11, 44 |
| abstract_inverted_index.each | 63, 110 |
| abstract_inverted_index.high | 6 |
| abstract_inverted_index.into | 47 |
| abstract_inverted_index.load | 10, 16, 23, 43, 100 |
| abstract_inverted_index.long | 66 |
| abstract_inverted_index.this | 151 |
| abstract_inverted_index.used | 83 |
| abstract_inverted_index.with | 125 |
| abstract_inverted_index.(GPR) | 81 |
| abstract_inverted_index.(STL) | 38 |
| abstract_inverted_index.LSTM, | 126 |
| abstract_inverted_index.Then, | 58 |
| abstract_inverted_index.final | 96 |
| abstract_inverted_index.local | 36 |
| abstract_inverted_index.lower | 142 |
| abstract_inverted_index.study | 116 |
| abstract_inverted_index.trend | 33, 49, 87 |
| abstract_inverted_index.using | 35, 56 |
| abstract_inverted_index.(CNN), | 76 |
| abstract_inverted_index.(LSTM) | 69 |
| abstract_inverted_index.errors | 143 |
| abstract_inverted_index.higher | 145 |
| abstract_inverted_index.memory | 68 |
| abstract_inverted_index.method | 20, 124, 140 |
| abstract_inverted_index.neural | 70, 74 |
| abstract_inverted_index.STL-GPR | 132 |
| abstract_inverted_index.Through | 135 |
| abstract_inverted_index.compare | 120 |
| abstract_inverted_index.method. | 152 |
| abstract_inverted_index.network | 75 |
| abstract_inverted_index.outcome | 97 |
| abstract_inverted_index.predict | 85 |
| abstract_inverted_index.process | 79 |
| abstract_inverted_index.results | 108 |
| abstract_inverted_index.summing | 105 |
| abstract_inverted_index.Gaussian | 78 |
| abstract_inverted_index.STL-CNN, | 130 |
| abstract_inverted_index.accuracy | 14 |
| abstract_inverted_index.combined | 19, 123, 139 |
| abstract_inverted_index.exhibits | 141 |
| abstract_inverted_index.methods. | 134 |
| abstract_inverted_index.network, | 71 |
| abstract_inverted_index.obtained | 103 |
| abstract_inverted_index.original | 9, 42 |
| abstract_inverted_index.proposed | 122, 138 |
| abstract_inverted_index.residual | 54, 92 |
| abstract_inverted_index.response | 1 |
| abstract_inverted_index.seasonal | 31, 51, 89 |
| abstract_inverted_index.specific | 114 |
| abstract_inverted_index.STL-LSTM, | 129 |
| abstract_inverted_index.accuracy, | 146 |
| abstract_inverted_index.affecting | 12 |
| abstract_inverted_index.component | 55 |
| abstract_inverted_index.conducted | 118 |
| abstract_inverted_index.proposed. | 40 |
| abstract_inverted_index.complexity | 4 |
| abstract_inverted_index.component, | 50, 52, 64, 88, 90, 93 |
| abstract_inverted_index.component. | 112 |
| abstract_inverted_index.components | 29 |
| abstract_inverted_index.decomposed | 46 |
| abstract_inverted_index.forecasted | 107 |
| abstract_inverted_index.individual | 111 |
| abstract_inverted_index.prediction | 133 |
| abstract_inverted_index.regression | 37, 80 |
| abstract_inverted_index.short-term | 22, 67 |
| abstract_inverted_index.volatility | 7 |
| abstract_inverted_index.comparison, | 136 |
| abstract_inverted_index.considering | 25, 59 |
| abstract_inverted_index.forecasting | 24, 101 |
| abstract_inverted_index.forecasting, | 17 |
| abstract_inverted_index.convolutional | 73 |
| abstract_inverted_index.decomposition | 34 |
| abstract_inverted_index.demonstrating | 147 |
| abstract_inverted_index.effectiveness | 149 |
| abstract_inverted_index.respectively. | 94 |
| abstract_inverted_index.characteristics | 27, 61 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 90 |
| corresponding_author_ids | https://openalex.org/A5004621631 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 8 |
| corresponding_institution_ids | https://openalex.org/I76130692 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/13 |
| sustainable_development_goals[0].score | 0.4099999964237213 |
| sustainable_development_goals[0].display_name | Climate action |
| citation_normalized_percentile.value | 0.7726288 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |