Explainable artificial intelligence for wind power forecasting model based on long short-term memory Article Swipe
Mona Ahmed Yassen
,
El‐Sayed M. El‐kenawy
,
Mohamed S. Abdelfattah
,
Idris Ismail
,
Hassan Mostafa
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1007/s00521-025-11230-5
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1007/s00521-025-11230-5
Expanding the world’s economy leads to higher requirements for energy storage. Traditional energy resources decline at the same time that environmental contamination levels increase in the world. Wind power is the most potential energy resource supported by its status as a significant renewable energy system. Wind power generation has become a popular and exciting method among nations worldwide for generating renewable energy. High wind power generation unpredictability results in unavoidable errors throughout the wind power prediction process, creating substantial difficulties in the optimal management of power systems. Wind power prediction errors remain unavoidable, but appropriate wind power uncertainty models help power system operators reduce their adverse impact on operational decision-making performance. In this paper, developed an appropriate machine learning model that efficiently forecasted wind power data through time series analysis. The long Short-Term Memory (LSTM), Gated Reference Unit (GRU), and Autoregressive Integrated Moving Average (ARIMA) are the machine algorithms used in this investigation. This paper proposed an X-double LSTM, which integrates explainable artificial intelligence (XAI) and long short-term memory (LSTM). The XAI-Shapley Additive Explanations (SHAP) model is modified to pinpoint the crucial elements affecting the power generation forecasting model’s accuracy in cutting-edge solar systems. Nine metrics are used to assess the efficacy of the proposed X-double LSTM model: root mean square error, mean bias error, correlation coefficient, relative root mean square error, Nash–Sutcliffe efficiency, mean square error, mean absolute deviation, coefficient of multiple determination, and Willmott index of agreement. For MSE, RMSE, MAE, MBE, r, R2, RRMSE, NSE, and WI, the suggested model improves by 0.000 11, 0.011, 0.008, 0.008, 0.99, 0.98, 2.5, 0.98, and 0.98, respectively. Other machine learning methods, including the Transformer model, single-layer LSTM networks, Autoregressive Moving Average (ARMA) models, Gated Recurrent Unit (GRU) networks, and Bidirectional (Bi-LSTM) networks, are compared to the performance of double LSTM. The twin LSTM model was demonstrated to perform better. The simulations and experimental findings show that the suggested model can precisely estimate wind power. Within the Google Collab environment, the suggested model uses TensorFlow and Keras.
Related Topics
Concepts
Computational Science and Engineering
Term (time)
Computer science
Long short term memory
Wind power forecasting
Wind power
Artificial neural network
Power (physics)
Artificial intelligence
Machine learning
Electric power system
Electrical engineering
Engineering
Recurrent neural network
Physics
Quantum mechanics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s00521-025-11230-5
- https://link.springer.com/content/pdf/10.1007/s00521-025-11230-5.pdf
- OA Status
- hybrid
- Cited By
- 3
- References
- 39
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4410358160
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4410358160Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1007/s00521-025-11230-5Digital Object Identifier
- Title
-
Explainable artificial intelligence for wind power forecasting model based on long short-term memoryWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-05-14Full publication date if available
- Authors
-
Mona Ahmed Yassen, El‐Sayed M. El‐kenawy, Mohamed S. Abdelfattah, Idris Ismail, Hassan MostafaList of authors in order
- Landing page
-
https://doi.org/10.1007/s00521-025-11230-5Publisher landing page
- PDF URL
-
https://link.springer.com/content/pdf/10.1007/s00521-025-11230-5.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://link.springer.com/content/pdf/10.1007/s00521-025-11230-5.pdfDirect OA link when available
- Concepts
-
Computational Science and Engineering, Term (time), Computer science, Long short term memory, Wind power forecasting, Wind power, Artificial neural network, Power (physics), Artificial intelligence, Machine learning, Electric power system, Electrical engineering, Engineering, Recurrent neural network, Physics, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 3Per-year citation counts (last 5 years)
- References (count)
-
39Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4410358160 |
|---|---|
| doi | https://doi.org/10.1007/s00521-025-11230-5 |
| ids.doi | https://doi.org/10.1007/s00521-025-11230-5 |
| ids.openalex | https://openalex.org/W4410358160 |
| fwci | 6.06469722 |
| type | article |
| title | Explainable artificial intelligence for wind power forecasting model based on long short-term memory |
| biblio.issue | 19 |
| biblio.volume | 37 |
| biblio.last_page | 14611 |
| biblio.first_page | 14589 |
| 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 | 0.9993000030517578 |
| 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/T12451 |
| topics[1].field.id | https://openalex.org/fields/22 |
| topics[1].field.display_name | Engineering |
| topics[1].score | 0.9786999821662903 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2208 |
| topics[1].subfield.display_name | Electrical and Electronic Engineering |
| topics[1].display_name | Smart Grid and Power Systems |
| topics[2].id | https://openalex.org/T13650 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9617999792098999 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1702 |
| topics[2].subfield.display_name | Artificial Intelligence |
| topics[2].display_name | Computational Physics and Python Applications |
| funders[0].id | https://openalex.org/F4320325716 |
| funders[0].ror | https://ror.org/01k8vtd75 |
| funders[0].display_name | Mansoura University |
| is_xpac | False |
| apc_list.value | 2390 |
| apc_list.currency | EUR |
| apc_list.value_usd | 2990 |
| apc_paid.value | 2390 |
| apc_paid.currency | EUR |
| apc_paid.value_usd | 2990 |
| concepts[0].id | https://openalex.org/C68597687 |
| concepts[0].level | 2 |
| concepts[0].score | 0.8641150593757629 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q362601 |
| concepts[0].display_name | Computational Science and Engineering |
| concepts[1].id | https://openalex.org/C61797465 |
| concepts[1].level | 2 |
| concepts[1].score | 0.7347097396850586 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q1188986 |
| concepts[1].display_name | Term (time) |
| concepts[2].id | https://openalex.org/C41008148 |
| concepts[2].level | 0 |
| concepts[2].score | 0.6069744825363159 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[2].display_name | Computer science |
| concepts[3].id | https://openalex.org/C133488467 |
| concepts[3].level | 4 |
| concepts[3].score | 0.5859121084213257 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q6673524 |
| concepts[3].display_name | Long short term memory |
| concepts[4].id | https://openalex.org/C2781084341 |
| concepts[4].level | 4 |
| concepts[4].score | 0.5066112279891968 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q2583670 |
| concepts[4].display_name | Wind power forecasting |
| concepts[5].id | https://openalex.org/C78600449 |
| concepts[5].level | 2 |
| concepts[5].score | 0.45088115334510803 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q43302 |
| concepts[5].display_name | Wind power |
| concepts[6].id | https://openalex.org/C50644808 |
| concepts[6].level | 2 |
| concepts[6].score | 0.4246557056903839 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q192776 |
| concepts[6].display_name | Artificial neural network |
| concepts[7].id | https://openalex.org/C163258240 |
| concepts[7].level | 2 |
| concepts[7].score | 0.404822438955307 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q25342 |
| concepts[7].display_name | Power (physics) |
| concepts[8].id | https://openalex.org/C154945302 |
| concepts[8].level | 1 |
| concepts[8].score | 0.3944697380065918 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[8].display_name | Artificial intelligence |
| concepts[9].id | https://openalex.org/C119857082 |
| concepts[9].level | 1 |
| concepts[9].score | 0.3046630024909973 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[9].display_name | Machine learning |
| concepts[10].id | https://openalex.org/C89227174 |
| concepts[10].level | 3 |
| concepts[10].score | 0.2417193353176117 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q2388981 |
| concepts[10].display_name | Electric power system |
| concepts[11].id | https://openalex.org/C119599485 |
| concepts[11].level | 1 |
| concepts[11].score | 0.09391742944717407 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q43035 |
| concepts[11].display_name | Electrical engineering |
| concepts[12].id | https://openalex.org/C127413603 |
| concepts[12].level | 0 |
| concepts[12].score | 0.0897853672504425 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q11023 |
| concepts[12].display_name | Engineering |
| concepts[13].id | https://openalex.org/C147168706 |
| concepts[13].level | 3 |
| concepts[13].score | 0.07484248280525208 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q1457734 |
| concepts[13].display_name | Recurrent neural network |
| concepts[14].id | https://openalex.org/C121332964 |
| concepts[14].level | 0 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q413 |
| concepts[14].display_name | Physics |
| concepts[15].id | https://openalex.org/C62520636 |
| concepts[15].level | 1 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q944 |
| concepts[15].display_name | Quantum mechanics |
| keywords[0].id | https://openalex.org/keywords/computational-science-and-engineering |
| keywords[0].score | 0.8641150593757629 |
| keywords[0].display_name | Computational Science and Engineering |
| keywords[1].id | https://openalex.org/keywords/term |
| keywords[1].score | 0.7347097396850586 |
| keywords[1].display_name | Term (time) |
| keywords[2].id | https://openalex.org/keywords/computer-science |
| keywords[2].score | 0.6069744825363159 |
| keywords[2].display_name | Computer science |
| keywords[3].id | https://openalex.org/keywords/long-short-term-memory |
| keywords[3].score | 0.5859121084213257 |
| keywords[3].display_name | Long short term memory |
| keywords[4].id | https://openalex.org/keywords/wind-power-forecasting |
| keywords[4].score | 0.5066112279891968 |
| keywords[4].display_name | Wind power forecasting |
| keywords[5].id | https://openalex.org/keywords/wind-power |
| keywords[5].score | 0.45088115334510803 |
| keywords[5].display_name | Wind power |
| keywords[6].id | https://openalex.org/keywords/artificial-neural-network |
| keywords[6].score | 0.4246557056903839 |
| keywords[6].display_name | Artificial neural network |
| keywords[7].id | https://openalex.org/keywords/power |
| keywords[7].score | 0.404822438955307 |
| keywords[7].display_name | Power (physics) |
| keywords[8].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[8].score | 0.3944697380065918 |
| keywords[8].display_name | Artificial intelligence |
| keywords[9].id | https://openalex.org/keywords/machine-learning |
| keywords[9].score | 0.3046630024909973 |
| keywords[9].display_name | Machine learning |
| keywords[10].id | https://openalex.org/keywords/electric-power-system |
| keywords[10].score | 0.2417193353176117 |
| keywords[10].display_name | Electric power system |
| keywords[11].id | https://openalex.org/keywords/electrical-engineering |
| keywords[11].score | 0.09391742944717407 |
| keywords[11].display_name | Electrical engineering |
| keywords[12].id | https://openalex.org/keywords/engineering |
| keywords[12].score | 0.0897853672504425 |
| keywords[12].display_name | Engineering |
| keywords[13].id | https://openalex.org/keywords/recurrent-neural-network |
| keywords[13].score | 0.07484248280525208 |
| keywords[13].display_name | Recurrent neural network |
| language | en |
| locations[0].id | doi:10.1007/s00521-025-11230-5 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S147897268 |
| locations[0].source.issn | 0941-0643, 1433-3058 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | 0941-0643 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Neural Computing and Applications |
| locations[0].source.host_organization | https://openalex.org/P4310319900 |
| locations[0].source.host_organization_name | Springer Science+Business Media |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310319900, https://openalex.org/P4310319965 |
| locations[0].source.host_organization_lineage_names | Springer Science+Business Media, Springer Nature |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://link.springer.com/content/pdf/10.1007/s00521-025-11230-5.pdf |
| 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 | Neural Computing and Applications |
| locations[0].landing_page_url | https://doi.org/10.1007/s00521-025-11230-5 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5116782348 |
| authorships[0].author.orcid | |
| authorships[0].author.display_name | Mona Ahmed Yassen |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Mona Ahmed Yassen |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5108139013 |
| authorships[1].author.orcid | https://orcid.org/0009-0006-0605-647X |
| authorships[1].author.display_name | El‐Sayed M. El‐kenawy |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | El-Sayed M. El-Kenawy |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5000815783 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-6416-9011 |
| authorships[2].author.display_name | Mohamed S. Abdelfattah |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Mohamed Gamal Abdel-Fattah |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5035347985 |
| authorships[3].author.orcid | https://orcid.org/0000-0001-6923-7530 |
| authorships[3].author.display_name | Idris Ismail |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Islam Ismail |
| authorships[3].is_corresponding | False |
| authorships[4].author.id | https://openalex.org/A5063929219 |
| authorships[4].author.orcid | https://orcid.org/0000-0003-0043-5007 |
| authorships[4].author.display_name | Hassan Mostafa |
| authorships[4].author_position | last |
| authorships[4].raw_author_name | Hossam El-Deen Salah Mostafa |
| authorships[4].is_corresponding | False |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://link.springer.com/content/pdf/10.1007/s00521-025-11230-5.pdf |
| open_access.oa_status | hybrid |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Explainable artificial intelligence for wind power forecasting model based on long short-term memory |
| has_fulltext | False |
| 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 | 0.9993000030517578 |
| 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/W2034182965, https://openalex.org/W2626641865, https://openalex.org/W2246158493, https://openalex.org/W2102851390, https://openalex.org/W2946066052, https://openalex.org/W4200305155, https://openalex.org/W2142241280, https://openalex.org/W1963538988, https://openalex.org/W4200518045, https://openalex.org/W3157443038 |
| cited_by_count | 3 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 3 |
| locations_count | 1 |
| best_oa_location.id | doi:10.1007/s00521-025-11230-5 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S147897268 |
| best_oa_location.source.issn | 0941-0643, 1433-3058 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | 0941-0643 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | Neural Computing and Applications |
| best_oa_location.source.host_organization | https://openalex.org/P4310319900 |
| best_oa_location.source.host_organization_name | Springer Science+Business Media |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310319900, https://openalex.org/P4310319965 |
| best_oa_location.source.host_organization_lineage_names | Springer Science+Business Media, Springer Nature |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://link.springer.com/content/pdf/10.1007/s00521-025-11230-5.pdf |
| 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 | Neural Computing and Applications |
| best_oa_location.landing_page_url | https://doi.org/10.1007/s00521-025-11230-5 |
| primary_location.id | doi:10.1007/s00521-025-11230-5 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S147897268 |
| primary_location.source.issn | 0941-0643, 1433-3058 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | 0941-0643 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Neural Computing and Applications |
| primary_location.source.host_organization | https://openalex.org/P4310319900 |
| primary_location.source.host_organization_name | Springer Science+Business Media |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310319900, https://openalex.org/P4310319965 |
| primary_location.source.host_organization_lineage_names | Springer Science+Business Media, Springer Nature |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://link.springer.com/content/pdf/10.1007/s00521-025-11230-5.pdf |
| 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 | Neural Computing and Applications |
| primary_location.landing_page_url | https://doi.org/10.1007/s00521-025-11230-5 |
| publication_date | 2025-05-14 |
| publication_year | 2025 |
| referenced_works | https://openalex.org/W2885195348, https://openalex.org/W4367309548, https://openalex.org/W3176181793, https://openalex.org/W4385376086, https://openalex.org/W4283826931, https://openalex.org/W4281651325, https://openalex.org/W4388191762, https://openalex.org/W4200287216, https://openalex.org/W4309049997, https://openalex.org/W3003714201, https://openalex.org/W4224981792, https://openalex.org/W4220771247, https://openalex.org/W3114878118, https://openalex.org/W3209405477, https://openalex.org/W4205371941, https://openalex.org/W4206666063, https://openalex.org/W2963335750, https://openalex.org/W3200790647, https://openalex.org/W3006193923, https://openalex.org/W1969141215, https://openalex.org/W4378193611, https://openalex.org/W4284971318, https://openalex.org/W3132882148, https://openalex.org/W2508187212, https://openalex.org/W2901240220, https://openalex.org/W2600292797, https://openalex.org/W2078476201, https://openalex.org/W2992578121, https://openalex.org/W2895755967, https://openalex.org/W2980867051, https://openalex.org/W2064675550, https://openalex.org/W4387737991, https://openalex.org/W4313892538, https://openalex.org/W3094568574, https://openalex.org/W3213465579, https://openalex.org/W4398782346, https://openalex.org/W4292417287, https://openalex.org/W6604896550, https://openalex.org/W4312994540 |
| referenced_works_count | 39 |
| abstract_inverted_index.a | 41, 51 |
| abstract_inverted_index.In | 112 |
| abstract_inverted_index.an | 116, 157 |
| abstract_inverted_index.as | 40 |
| abstract_inverted_index.at | 16 |
| abstract_inverted_index.by | 37, 255 |
| abstract_inverted_index.in | 25, 69, 81, 151, 191 |
| abstract_inverted_index.is | 30, 177 |
| abstract_inverted_index.of | 85, 203, 232, 238, 298 |
| abstract_inverted_index.on | 108 |
| abstract_inverted_index.r, | 245 |
| abstract_inverted_index.to | 6, 179, 199, 295, 307 |
| abstract_inverted_index.11, | 257 |
| abstract_inverted_index.For | 240 |
| abstract_inverted_index.R2, | 246 |
| abstract_inverted_index.The | 131, 171, 301, 310 |
| abstract_inverted_index.WI, | 250 |
| abstract_inverted_index.and | 53, 140, 166, 235, 249, 265, 289, 312, 335 |
| abstract_inverted_index.are | 146, 197, 293 |
| abstract_inverted_index.but | 94 |
| abstract_inverted_index.can | 320 |
| abstract_inverted_index.for | 9, 59 |
| abstract_inverted_index.has | 49 |
| abstract_inverted_index.its | 38 |
| abstract_inverted_index.the | 2, 17, 26, 31, 73, 82, 147, 181, 185, 201, 204, 251, 273, 296, 317, 326, 330 |
| abstract_inverted_index.was | 305 |
| abstract_inverted_index.2.5, | 263 |
| abstract_inverted_index.High | 63 |
| abstract_inverted_index.LSTM | 207, 277, 303 |
| abstract_inverted_index.MAE, | 243 |
| abstract_inverted_index.MBE, | 244 |
| abstract_inverted_index.MSE, | 241 |
| abstract_inverted_index.NSE, | 248 |
| abstract_inverted_index.Nine | 195 |
| abstract_inverted_index.This | 154 |
| abstract_inverted_index.Unit | 138, 286 |
| abstract_inverted_index.Wind | 28, 46, 88 |
| abstract_inverted_index.bias | 214 |
| abstract_inverted_index.data | 126 |
| abstract_inverted_index.help | 100 |
| abstract_inverted_index.long | 132, 167 |
| abstract_inverted_index.mean | 210, 213, 220, 225, 228 |
| abstract_inverted_index.most | 32 |
| abstract_inverted_index.root | 209, 219 |
| abstract_inverted_index.same | 18 |
| abstract_inverted_index.show | 315 |
| abstract_inverted_index.that | 20, 121, 316 |
| abstract_inverted_index.this | 113, 152 |
| abstract_inverted_index.time | 19, 128 |
| abstract_inverted_index.twin | 302 |
| abstract_inverted_index.used | 150, 198 |
| abstract_inverted_index.uses | 333 |
| abstract_inverted_index.wind | 64, 74, 96, 124, 323 |
| abstract_inverted_index.(GRU) | 287 |
| abstract_inverted_index.(XAI) | 165 |
| abstract_inverted_index.0.000 | 256 |
| abstract_inverted_index.0.98, | 262, 264, 266 |
| abstract_inverted_index.0.99, | 261 |
| abstract_inverted_index.Gated | 136, 284 |
| abstract_inverted_index.LSTM, | 159 |
| abstract_inverted_index.LSTM. | 300 |
| abstract_inverted_index.Other | 268 |
| abstract_inverted_index.RMSE, | 242 |
| abstract_inverted_index.among | 56 |
| abstract_inverted_index.index | 237 |
| abstract_inverted_index.leads | 5 |
| abstract_inverted_index.model | 120, 176, 253, 304, 319, 332 |
| abstract_inverted_index.paper | 155 |
| abstract_inverted_index.power | 29, 47, 65, 75, 86, 89, 97, 101, 125, 186 |
| abstract_inverted_index.solar | 193 |
| abstract_inverted_index.their | 105 |
| abstract_inverted_index.which | 160 |
| abstract_inverted_index.(ARMA) | 282 |
| abstract_inverted_index.(GRU), | 139 |
| abstract_inverted_index.(SHAP) | 175 |
| abstract_inverted_index.0.008, | 259, 260 |
| abstract_inverted_index.0.011, | 258 |
| abstract_inverted_index.Collab | 328 |
| abstract_inverted_index.Google | 327 |
| abstract_inverted_index.Keras. | 336 |
| abstract_inverted_index.Memory | 134 |
| abstract_inverted_index.Moving | 143, 280 |
| abstract_inverted_index.RRMSE, | 247 |
| abstract_inverted_index.Within | 325 |
| abstract_inverted_index.assess | 200 |
| abstract_inverted_index.become | 50 |
| abstract_inverted_index.double | 299 |
| abstract_inverted_index.energy | 10, 13, 34, 44 |
| abstract_inverted_index.error, | 212, 215, 222, 227 |
| abstract_inverted_index.errors | 71, 91 |
| abstract_inverted_index.higher | 7 |
| abstract_inverted_index.impact | 107 |
| abstract_inverted_index.levels | 23 |
| abstract_inverted_index.memory | 169 |
| abstract_inverted_index.method | 55 |
| abstract_inverted_index.model, | 275 |
| abstract_inverted_index.model: | 208 |
| abstract_inverted_index.models | 99 |
| abstract_inverted_index.paper, | 114 |
| abstract_inverted_index.power. | 324 |
| abstract_inverted_index.reduce | 104 |
| abstract_inverted_index.remain | 92 |
| abstract_inverted_index.series | 129 |
| abstract_inverted_index.square | 211, 221, 226 |
| abstract_inverted_index.status | 39 |
| abstract_inverted_index.system | 102 |
| abstract_inverted_index.world. | 27 |
| abstract_inverted_index.(ARIMA) | 145 |
| abstract_inverted_index.(LSTM), | 135 |
| abstract_inverted_index.(LSTM). | 170 |
| abstract_inverted_index.Average | 144, 281 |
| abstract_inverted_index.adverse | 106 |
| abstract_inverted_index.better. | 309 |
| abstract_inverted_index.crucial | 182 |
| abstract_inverted_index.decline | 15 |
| abstract_inverted_index.economy | 4 |
| abstract_inverted_index.energy. | 62 |
| abstract_inverted_index.machine | 118, 148, 269 |
| abstract_inverted_index.metrics | 196 |
| abstract_inverted_index.models, | 283 |
| abstract_inverted_index.nations | 57 |
| abstract_inverted_index.optimal | 83 |
| abstract_inverted_index.perform | 308 |
| abstract_inverted_index.popular | 52 |
| abstract_inverted_index.results | 68 |
| abstract_inverted_index.system. | 45 |
| abstract_inverted_index.through | 127 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Additive | 173 |
| abstract_inverted_index.Willmott | 236 |
| abstract_inverted_index.X-double | 158, 206 |
| abstract_inverted_index.absolute | 229 |
| abstract_inverted_index.accuracy | 190 |
| abstract_inverted_index.compared | 294 |
| abstract_inverted_index.creating | 78 |
| abstract_inverted_index.efficacy | 202 |
| abstract_inverted_index.elements | 183 |
| abstract_inverted_index.estimate | 322 |
| abstract_inverted_index.exciting | 54 |
| abstract_inverted_index.findings | 314 |
| abstract_inverted_index.improves | 254 |
| abstract_inverted_index.increase | 24 |
| abstract_inverted_index.learning | 119, 270 |
| abstract_inverted_index.methods, | 271 |
| abstract_inverted_index.modified | 178 |
| abstract_inverted_index.multiple | 233 |
| abstract_inverted_index.pinpoint | 180 |
| abstract_inverted_index.process, | 77 |
| abstract_inverted_index.proposed | 156, 205 |
| abstract_inverted_index.relative | 218 |
| abstract_inverted_index.resource | 35 |
| abstract_inverted_index.storage. | 11 |
| abstract_inverted_index.systems. | 87, 194 |
| abstract_inverted_index.(Bi-LSTM) | 291 |
| abstract_inverted_index.Expanding | 1 |
| abstract_inverted_index.Recurrent | 285 |
| abstract_inverted_index.Reference | 137 |
| abstract_inverted_index.affecting | 184 |
| abstract_inverted_index.analysis. | 130 |
| abstract_inverted_index.developed | 115 |
| abstract_inverted_index.including | 272 |
| abstract_inverted_index.model’s | 189 |
| abstract_inverted_index.networks, | 278, 288, 292 |
| abstract_inverted_index.operators | 103 |
| abstract_inverted_index.potential | 33 |
| abstract_inverted_index.precisely | 321 |
| abstract_inverted_index.renewable | 43, 61 |
| abstract_inverted_index.resources | 14 |
| abstract_inverted_index.suggested | 252, 318, 331 |
| abstract_inverted_index.supported | 36 |
| abstract_inverted_index.worldwide | 58 |
| abstract_inverted_index.world’s | 3 |
| abstract_inverted_index.Integrated | 142 |
| abstract_inverted_index.Short-Term | 133 |
| abstract_inverted_index.TensorFlow | 334 |
| abstract_inverted_index.agreement. | 239 |
| abstract_inverted_index.algorithms | 149 |
| abstract_inverted_index.artificial | 163 |
| abstract_inverted_index.deviation, | 230 |
| abstract_inverted_index.forecasted | 123 |
| abstract_inverted_index.generating | 60 |
| abstract_inverted_index.generation | 48, 66, 187 |
| abstract_inverted_index.integrates | 161 |
| abstract_inverted_index.management | 84 |
| abstract_inverted_index.prediction | 76, 90 |
| abstract_inverted_index.short-term | 168 |
| abstract_inverted_index.throughout | 72 |
| abstract_inverted_index.Traditional | 12 |
| abstract_inverted_index.Transformer | 274 |
| abstract_inverted_index.XAI-Shapley | 172 |
| abstract_inverted_index.appropriate | 95, 117 |
| abstract_inverted_index.coefficient | 231 |
| abstract_inverted_index.correlation | 216 |
| abstract_inverted_index.efficiency, | 224 |
| abstract_inverted_index.efficiently | 122 |
| abstract_inverted_index.explainable | 162 |
| abstract_inverted_index.forecasting | 188 |
| abstract_inverted_index.operational | 109 |
| abstract_inverted_index.performance | 297 |
| abstract_inverted_index.significant | 42 |
| abstract_inverted_index.simulations | 311 |
| abstract_inverted_index.substantial | 79 |
| abstract_inverted_index.unavoidable | 70 |
| abstract_inverted_index.uncertainty | 98 |
| abstract_inverted_index.Explanations | 174 |
| abstract_inverted_index.coefficient, | 217 |
| abstract_inverted_index.cutting-edge | 192 |
| abstract_inverted_index.demonstrated | 306 |
| abstract_inverted_index.difficulties | 80 |
| abstract_inverted_index.environment, | 329 |
| abstract_inverted_index.experimental | 313 |
| abstract_inverted_index.intelligence | 164 |
| abstract_inverted_index.performance. | 111 |
| abstract_inverted_index.requirements | 8 |
| abstract_inverted_index.single-layer | 276 |
| abstract_inverted_index.unavoidable, | 93 |
| abstract_inverted_index.Bidirectional | 290 |
| abstract_inverted_index.contamination | 22 |
| abstract_inverted_index.environmental | 21 |
| abstract_inverted_index.respectively. | 267 |
| abstract_inverted_index.Autoregressive | 141, 279 |
| abstract_inverted_index.determination, | 234 |
| abstract_inverted_index.investigation. | 153 |
| abstract_inverted_index.decision-making | 110 |
| abstract_inverted_index.Nash–Sutcliffe | 223 |
| abstract_inverted_index.unpredictability | 67 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 96 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.9259637 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |