Artificial neural network for building energy consumption prediction Article Swipe
Hung T. Le
,
T. Nguyen‐Thoi
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1063/5.0068981
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1063/5.0068981
The optimization of energy consumption is important, especially in building these days. The energy consumption of Mechanical – Ventilating – Air conditioning (MVAC) systems always the largest proportion in total energy consumption. One of the most effective approach is the estimation and prediction as close as possible to the real operation mode of the MVAC in reality. However, the tasks of energy consumption estimation request large efforts, time consuming but low precise results. Most designers roughly calculate the energy consumption at first and the using automation control to manipulate the system operation later. With the help of the newest, cutting edge technology in soft computing like ANN, ANFIS and intelligent optimization methods, the designers now can apply these technologies to achieve the reliable, rapid, precise results for the further convincing designs. In this paper, the authors simply apply the Multi Layers, Back Propaganda Neural Network (MLB ANN) to estimate the building Heating Load (HL) and Cooling Load (CL). The Mean Square Errors (MSE) and Regression R Values (R) are used as the indicators for ANN with the dataset provided open access by UCI. The results prove that ANN can be reliable approach to predict energy consumption for complex, multi design option building.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1063/5.0068981
- https://aip.scitation.org/doi/pdf/10.1063/5.0068981
- OA Status
- bronze
- References
- 5
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3214406573
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3214406573Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1063/5.0068981Digital Object Identifier
- Title
-
Artificial neural network for building energy consumption predictionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-01-01Full publication date if available
- Authors
-
Hung T. Le, T. Nguyen‐ThoiList of authors in order
- Landing page
-
https://doi.org/10.1063/5.0068981Publisher landing page
- PDF URL
-
https://aip.scitation.org/doi/pdf/10.1063/5.0068981Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
- OA URL
-
https://aip.scitation.org/doi/pdf/10.1063/5.0068981Direct OA link when available
- Concepts
-
Artificial neural network, Energy consumption, Computer science, Artificial intelligence, Consumption (sociology), Energy (signal processing), Machine learning, Engineering, Statistics, Electrical engineering, Mathematics, Sociology, Social scienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
5Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W3214406573 |
|---|---|
| doi | https://doi.org/10.1063/5.0068981 |
| ids.doi | https://doi.org/10.1063/5.0068981 |
| ids.mag | 3214406573 |
| ids.openalex | https://openalex.org/W3214406573 |
| fwci | 0.0 |
| type | article |
| title | Artificial neural network for building energy consumption prediction |
| biblio.issue | |
| biblio.volume | 2420 |
| biblio.last_page | 020025 |
| biblio.first_page | 020025 |
| topics[0].id | https://openalex.org/T10121 |
| topics[0].field.id | https://openalex.org/fields/22 |
| topics[0].field.display_name | Engineering |
| topics[0].score | 0.9933000206947327 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2215 |
| topics[0].subfield.display_name | Building and Construction |
| topics[0].display_name | Building Energy and Comfort Optimization |
| topics[1].id | https://openalex.org/T11052 |
| topics[1].field.id | https://openalex.org/fields/22 |
| topics[1].field.display_name | Engineering |
| topics[1].score | 0.947700023651123 |
| 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 | Energy Load and Power Forecasting |
| topics[2].id | https://openalex.org/T10320 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9279999732971191 |
| 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 | Neural Networks and Applications |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C50644808 |
| concepts[0].level | 2 |
| concepts[0].score | 0.7301596403121948 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q192776 |
| concepts[0].display_name | Artificial neural network |
| concepts[1].id | https://openalex.org/C2780165032 |
| concepts[1].level | 2 |
| concepts[1].score | 0.7033582925796509 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q16869822 |
| concepts[1].display_name | Energy consumption |
| concepts[2].id | https://openalex.org/C41008148 |
| concepts[2].level | 0 |
| concepts[2].score | 0.6230716109275818 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[2].display_name | Computer science |
| concepts[3].id | https://openalex.org/C154945302 |
| concepts[3].level | 1 |
| concepts[3].score | 0.4817812144756317 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[3].display_name | Artificial intelligence |
| concepts[4].id | https://openalex.org/C30772137 |
| concepts[4].level | 2 |
| concepts[4].score | 0.4631905257701874 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q5164762 |
| concepts[4].display_name | Consumption (sociology) |
| concepts[5].id | https://openalex.org/C186370098 |
| concepts[5].level | 2 |
| concepts[5].score | 0.4104006290435791 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q442787 |
| concepts[5].display_name | Energy (signal processing) |
| concepts[6].id | https://openalex.org/C119857082 |
| concepts[6].level | 1 |
| concepts[6].score | 0.3692418038845062 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[6].display_name | Machine learning |
| concepts[7].id | https://openalex.org/C127413603 |
| concepts[7].level | 0 |
| concepts[7].score | 0.1613551676273346 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q11023 |
| concepts[7].display_name | Engineering |
| concepts[8].id | https://openalex.org/C105795698 |
| concepts[8].level | 1 |
| concepts[8].score | 0.09829172492027283 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q12483 |
| concepts[8].display_name | Statistics |
| concepts[9].id | https://openalex.org/C119599485 |
| concepts[9].level | 1 |
| concepts[9].score | 0.09487402439117432 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q43035 |
| concepts[9].display_name | Electrical engineering |
| concepts[10].id | https://openalex.org/C33923547 |
| concepts[10].level | 0 |
| concepts[10].score | 0.07246974110603333 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[10].display_name | Mathematics |
| concepts[11].id | https://openalex.org/C144024400 |
| concepts[11].level | 0 |
| concepts[11].score | 0.0 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q21201 |
| concepts[11].display_name | Sociology |
| concepts[12].id | https://openalex.org/C36289849 |
| concepts[12].level | 1 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q34749 |
| concepts[12].display_name | Social science |
| keywords[0].id | https://openalex.org/keywords/artificial-neural-network |
| keywords[0].score | 0.7301596403121948 |
| keywords[0].display_name | Artificial neural network |
| keywords[1].id | https://openalex.org/keywords/energy-consumption |
| keywords[1].score | 0.7033582925796509 |
| keywords[1].display_name | Energy consumption |
| keywords[2].id | https://openalex.org/keywords/computer-science |
| keywords[2].score | 0.6230716109275818 |
| keywords[2].display_name | Computer science |
| keywords[3].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[3].score | 0.4817812144756317 |
| keywords[3].display_name | Artificial intelligence |
| keywords[4].id | https://openalex.org/keywords/consumption |
| keywords[4].score | 0.4631905257701874 |
| keywords[4].display_name | Consumption (sociology) |
| keywords[5].id | https://openalex.org/keywords/energy |
| keywords[5].score | 0.4104006290435791 |
| keywords[5].display_name | Energy (signal processing) |
| keywords[6].id | https://openalex.org/keywords/machine-learning |
| keywords[6].score | 0.3692418038845062 |
| keywords[6].display_name | Machine learning |
| keywords[7].id | https://openalex.org/keywords/engineering |
| keywords[7].score | 0.1613551676273346 |
| keywords[7].display_name | Engineering |
| keywords[8].id | https://openalex.org/keywords/statistics |
| keywords[8].score | 0.09829172492027283 |
| keywords[8].display_name | Statistics |
| keywords[9].id | https://openalex.org/keywords/electrical-engineering |
| keywords[9].score | 0.09487402439117432 |
| keywords[9].display_name | Electrical engineering |
| keywords[10].id | https://openalex.org/keywords/mathematics |
| keywords[10].score | 0.07246974110603333 |
| keywords[10].display_name | Mathematics |
| language | en |
| locations[0].id | doi:10.1063/5.0068981 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S2764696622 |
| locations[0].source.issn | 0094-243X, 1551-7616, 1935-0465 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | 0094-243X |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | AIP conference proceedings |
| locations[0].source.host_organization | https://openalex.org/P4310320257 |
| locations[0].source.host_organization_name | American Institute of Physics |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310320257 |
| locations[0].source.host_organization_lineage_names | American Institute of Physics |
| locations[0].license | |
| locations[0].pdf_url | https://aip.scitation.org/doi/pdf/10.1063/5.0068981 |
| locations[0].version | publishedVersion |
| locations[0].raw_type | proceedings-article |
| locations[0].license_id | |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | AIP Conference Proceedings |
| locations[0].landing_page_url | https://doi.org/10.1063/5.0068981 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5101936200 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-5431-4377 |
| authorships[0].author.display_name | Hung T. Le |
| authorships[0].countries | VN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I4210123993 |
| authorships[0].affiliations[0].raw_affiliation_string | Faculty of Engineering, Van Lang University, Ho Chi Minh City, Vietnam |
| authorships[0].institutions[0].id | https://openalex.org/I4210123993 |
| authorships[0].institutions[0].ror | https://ror.org/02ryrf141 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I4210123993 |
| authorships[0].institutions[0].country_code | VN |
| authorships[0].institutions[0].display_name | Van Lang University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Hung Tien Le |
| authorships[0].is_corresponding | True |
| authorships[0].raw_affiliation_strings | Faculty of Engineering, Van Lang University, Ho Chi Minh City, Vietnam |
| authorships[1].author.id | https://openalex.org/A5053998535 |
| authorships[1].author.orcid | https://orcid.org/0000-0001-7985-6706 |
| authorships[1].author.display_name | T. Nguyen‐Thoi |
| authorships[1].countries | VN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I141445968 |
| authorships[1].affiliations[0].raw_affiliation_string | Institute for Computational Science, Ton Duc Thang University, Vietnam |
| authorships[1].institutions[0].id | https://openalex.org/I141445968 |
| authorships[1].institutions[0].ror | https://ror.org/01drq0835 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I141445968 |
| authorships[1].institutions[0].country_code | VN |
| authorships[1].institutions[0].display_name | Ton Duc Thang University |
| authorships[1].author_position | last |
| authorships[1].raw_author_name | Thoi Trung Nguyen |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Institute for Computational Science, Ton Duc Thang University, Vietnam |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://aip.scitation.org/doi/pdf/10.1063/5.0068981 |
| open_access.oa_status | bronze |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Artificial neural network for building energy consumption prediction |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10121 |
| primary_topic.field.id | https://openalex.org/fields/22 |
| primary_topic.field.display_name | Engineering |
| primary_topic.score | 0.9933000206947327 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2215 |
| primary_topic.subfield.display_name | Building and Construction |
| primary_topic.display_name | Building Energy and Comfort Optimization |
| related_works | https://openalex.org/W1997864015, https://openalex.org/W2961085424, https://openalex.org/W4297745244, https://openalex.org/W4214628662, https://openalex.org/W2037518538, https://openalex.org/W2156822401, https://openalex.org/W3092380670, https://openalex.org/W3125847301, https://openalex.org/W4313390786, https://openalex.org/W2356819012 |
| cited_by_count | 0 |
| locations_count | 1 |
| best_oa_location.id | doi:10.1063/5.0068981 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S2764696622 |
| best_oa_location.source.issn | 0094-243X, 1551-7616, 1935-0465 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | 0094-243X |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | AIP conference proceedings |
| best_oa_location.source.host_organization | https://openalex.org/P4310320257 |
| best_oa_location.source.host_organization_name | American Institute of Physics |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310320257 |
| best_oa_location.source.host_organization_lineage_names | American Institute of Physics |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://aip.scitation.org/doi/pdf/10.1063/5.0068981 |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | proceedings-article |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | AIP Conference Proceedings |
| best_oa_location.landing_page_url | https://doi.org/10.1063/5.0068981 |
| primary_location.id | doi:10.1063/5.0068981 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S2764696622 |
| primary_location.source.issn | 0094-243X, 1551-7616, 1935-0465 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | 0094-243X |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | AIP conference proceedings |
| primary_location.source.host_organization | https://openalex.org/P4310320257 |
| primary_location.source.host_organization_name | American Institute of Physics |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310320257 |
| primary_location.source.host_organization_lineage_names | American Institute of Physics |
| primary_location.license | |
| primary_location.pdf_url | https://aip.scitation.org/doi/pdf/10.1063/5.0068981 |
| primary_location.version | publishedVersion |
| primary_location.raw_type | proceedings-article |
| primary_location.license_id | |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | AIP Conference Proceedings |
| primary_location.landing_page_url | https://doi.org/10.1063/5.0068981 |
| publication_date | 2021-01-01 |
| publication_year | 2021 |
| referenced_works | https://openalex.org/W2908148552, https://openalex.org/W2138493226, https://openalex.org/W2116507377, https://openalex.org/W1969885422, https://openalex.org/W2284763872 |
| referenced_works_count | 5 |
| abstract_inverted_index.R | 165 |
| abstract_inverted_index.In | 131 |
| abstract_inverted_index.as | 43, 45, 170 |
| abstract_inverted_index.at | 80 |
| abstract_inverted_index.be | 189 |
| abstract_inverted_index.by | 181 |
| abstract_inverted_index.in | 8, 28, 55, 102 |
| abstract_inverted_index.is | 5, 38 |
| abstract_inverted_index.of | 2, 15, 33, 52, 60, 96 |
| abstract_inverted_index.to | 47, 87, 119, 147, 192 |
| abstract_inverted_index.(R) | 167 |
| abstract_inverted_index.ANN | 174, 187 |
| abstract_inverted_index.Air | 20 |
| abstract_inverted_index.One | 32 |
| abstract_inverted_index.The | 0, 12, 158, 183 |
| abstract_inverted_index.and | 41, 82, 108, 154, 163 |
| abstract_inverted_index.are | 168 |
| abstract_inverted_index.but | 69 |
| abstract_inverted_index.can | 115, 188 |
| abstract_inverted_index.for | 126, 173, 196 |
| abstract_inverted_index.low | 70 |
| abstract_inverted_index.now | 114 |
| abstract_inverted_index.the | 25, 34, 39, 48, 53, 58, 77, 83, 89, 94, 97, 112, 121, 127, 134, 138, 149, 171, 176 |
| abstract_inverted_index.– | 17, 19 |
| abstract_inverted_index.(HL) | 153 |
| abstract_inverted_index.(MLB | 145 |
| abstract_inverted_index.ANN) | 146 |
| abstract_inverted_index.ANN, | 106 |
| abstract_inverted_index.Back | 141 |
| abstract_inverted_index.Load | 152, 156 |
| abstract_inverted_index.MVAC | 54 |
| abstract_inverted_index.Mean | 159 |
| abstract_inverted_index.Most | 73 |
| abstract_inverted_index.UCI. | 182 |
| abstract_inverted_index.With | 93 |
| abstract_inverted_index.edge | 100 |
| abstract_inverted_index.help | 95 |
| abstract_inverted_index.like | 105 |
| abstract_inverted_index.mode | 51 |
| abstract_inverted_index.most | 35 |
| abstract_inverted_index.open | 179 |
| abstract_inverted_index.real | 49 |
| abstract_inverted_index.soft | 103 |
| abstract_inverted_index.that | 186 |
| abstract_inverted_index.this | 132 |
| abstract_inverted_index.time | 67 |
| abstract_inverted_index.used | 169 |
| abstract_inverted_index.with | 175 |
| abstract_inverted_index.(CL). | 157 |
| abstract_inverted_index.(MSE) | 162 |
| abstract_inverted_index.ANFIS | 107 |
| abstract_inverted_index.Multi | 139 |
| abstract_inverted_index.apply | 116, 137 |
| abstract_inverted_index.close | 44 |
| abstract_inverted_index.days. | 11 |
| abstract_inverted_index.first | 81 |
| abstract_inverted_index.large | 65 |
| abstract_inverted_index.multi | 198 |
| abstract_inverted_index.prove | 185 |
| abstract_inverted_index.tasks | 59 |
| abstract_inverted_index.these | 10, 117 |
| abstract_inverted_index.total | 29 |
| abstract_inverted_index.using | 84 |
| abstract_inverted_index.(MVAC) | 22 |
| abstract_inverted_index.Errors | 161 |
| abstract_inverted_index.Neural | 143 |
| abstract_inverted_index.Square | 160 |
| abstract_inverted_index.Values | 166 |
| abstract_inverted_index.access | 180 |
| abstract_inverted_index.always | 24 |
| abstract_inverted_index.design | 199 |
| abstract_inverted_index.energy | 3, 13, 30, 61, 78, 194 |
| abstract_inverted_index.later. | 92 |
| abstract_inverted_index.option | 200 |
| abstract_inverted_index.paper, | 133 |
| abstract_inverted_index.rapid, | 123 |
| abstract_inverted_index.simply | 136 |
| abstract_inverted_index.system | 90 |
| abstract_inverted_index.Cooling | 155 |
| abstract_inverted_index.Heating | 151 |
| abstract_inverted_index.Layers, | 140 |
| abstract_inverted_index.Network | 144 |
| abstract_inverted_index.achieve | 120 |
| abstract_inverted_index.authors | 135 |
| abstract_inverted_index.control | 86 |
| abstract_inverted_index.cutting | 99 |
| abstract_inverted_index.dataset | 177 |
| abstract_inverted_index.further | 128 |
| abstract_inverted_index.largest | 26 |
| abstract_inverted_index.newest, | 98 |
| abstract_inverted_index.precise | 71, 124 |
| abstract_inverted_index.predict | 193 |
| abstract_inverted_index.request | 64 |
| abstract_inverted_index.results | 125, 184 |
| abstract_inverted_index.roughly | 75 |
| abstract_inverted_index.systems | 23 |
| abstract_inverted_index.However, | 57 |
| abstract_inverted_index.approach | 37, 191 |
| abstract_inverted_index.building | 9, 150 |
| abstract_inverted_index.complex, | 197 |
| abstract_inverted_index.designs. | 130 |
| abstract_inverted_index.efforts, | 66 |
| abstract_inverted_index.estimate | 148 |
| abstract_inverted_index.methods, | 111 |
| abstract_inverted_index.possible | 46 |
| abstract_inverted_index.provided | 178 |
| abstract_inverted_index.reality. | 56 |
| abstract_inverted_index.reliable | 190 |
| abstract_inverted_index.results. | 72 |
| abstract_inverted_index.building. | 201 |
| abstract_inverted_index.calculate | 76 |
| abstract_inverted_index.computing | 104 |
| abstract_inverted_index.consuming | 68 |
| abstract_inverted_index.designers | 74, 113 |
| abstract_inverted_index.effective | 36 |
| abstract_inverted_index.operation | 50, 91 |
| abstract_inverted_index.reliable, | 122 |
| abstract_inverted_index.Mechanical | 16 |
| abstract_inverted_index.Propaganda | 142 |
| abstract_inverted_index.Regression | 164 |
| abstract_inverted_index.automation | 85 |
| abstract_inverted_index.convincing | 129 |
| abstract_inverted_index.especially | 7 |
| abstract_inverted_index.estimation | 40, 63 |
| abstract_inverted_index.important, | 6 |
| abstract_inverted_index.indicators | 172 |
| abstract_inverted_index.manipulate | 88 |
| abstract_inverted_index.prediction | 42 |
| abstract_inverted_index.proportion | 27 |
| abstract_inverted_index.technology | 101 |
| abstract_inverted_index.Ventilating | 18 |
| abstract_inverted_index.consumption | 4, 14, 62, 79, 195 |
| abstract_inverted_index.intelligent | 109 |
| abstract_inverted_index.conditioning | 21 |
| abstract_inverted_index.consumption. | 31 |
| abstract_inverted_index.optimization | 1, 110 |
| abstract_inverted_index.technologies | 118 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5101936200 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| corresponding_institution_ids | https://openalex.org/I4210123993 |
| 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 | 0.18816116 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |