{"id":"https://openalex.org/W7213237019","doi":"https://doi.org/10.1002/ese3.70650","title":"High Precision Prediction Model of Wind Speed for Underground Mine Tunnel by Using ASTF Net","display_name":"High Precision Prediction Model of Wind Speed for Underground Mine Tunnel by Using ASTF Net","publication_year":2026,"publication_date":"2026-09-15","ids":{"openalex":"https://openalex.org/W7213237019","doi":"https://doi.org/10.1002/ese3.70650"},"language":"en","primary_location":{"id":"doi:10.1002/ese3.70650","doi":"https://doi.org/10.1002/ese3.70650","is_oa":true,"landing_page_url":"https://doi.org/10.1002/ese3.70650","pdf_url":"https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/ese3.70650","source":{"id":"https://openalex.org/S2737327489","display_name":"Energy Science & Engineering","issn_l":"2050-0505","issn":["2050-0505"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","dongbi-d","jufo-1","ki-jl-1","norway-1","oarl","poland-100","russia-white-list-2"],"host_organization":"https://openalex.org/P4310320595","host_organization_name":"Wiley","host_organization_lineage":["https://openalex.org/P4310320595"],"host_organization_lineage_names":["Wiley"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Energy Science & Engineering","raw_type":"journal-article"},"volume":null,"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/ese3.70650","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5054458341","display_name":"Ruo-Qi Li","orcid":"https://orcid.org/0009-0008-1061-5347","observed_orcids":["https://orcid.org/0009-0008-1061-5347"]},"institutions":[{"id":"https://openalex.org/I154893126","display_name":"Guizhou Normal University","ror":"https://ror.org/02x1pa065","country_code":"CN","type":"education","lineage":["https://openalex.org/I154893126"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ruo\u2010Qi Li","raw_affiliation_strings":["School of Big Data and Computer Science Guizhou Normal University Guiyang China"],"raw_orcid":"https://orcid.org/0009-0008-1061-5347","affiliations":[{"raw_affiliation_string":"School of Big Data and Computer Science Guizhou Normal University Guiyang China","institution_ids":["https://openalex.org/I154893126"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Yong\u2010Qiang Chai","orcid":"https://orcid.org/0000-0003-1157-5718","observed_orcids":["https://orcid.org/0000-0003-1157-5718"]},"institutions":[{"id":"https://openalex.org/I154893126","display_name":"Guizhou Normal University","ror":"https://ror.org/02x1pa065","country_code":"CN","type":"education","lineage":["https://openalex.org/I154893126"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yong\u2010Qiang Chai","raw_affiliation_strings":["School of Big Data and Computer Science Guizhou Normal University Guiyang China"],"raw_orcid":"https://orcid.org/0000-0003-1157-5718","affiliations":[{"raw_affiliation_string":"School of Big Data and Computer Science Guizhou Normal University Guiyang China","institution_ids":["https://openalex.org/I154893126"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128156170","display_name":"Lian Shi","orcid":"https://orcid.org/0009-0005-9902-3505","observed_orcids":["https://orcid.org/0009-0005-9902-3505"]},"institutions":[{"id":"https://openalex.org/I154893126","display_name":"Guizhou Normal University","ror":"https://ror.org/02x1pa065","country_code":"CN","type":"education","lineage":["https://openalex.org/I154893126"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lian Shi","raw_affiliation_strings":["School of Big Data and Computer Science Guizhou Normal University Guiyang China"],"raw_orcid":"https://orcid.org/0009-0005-9902-3505","affiliations":[{"raw_affiliation_string":"School of Big Data and Computer Science Guizhou Normal University Guiyang China","institution_ids":["https://openalex.org/I154893126"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128173563","display_name":"Fu-Gang Wang","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fu\u2010Gang Wang","raw_affiliation_strings":["Ming\u2010Chuang Hui\u2010Yuan (Guizhou) Technology Co., Ltd Guiyang China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ming\u2010Chuang Hui\u2010Yuan (Guizhou) Technology Co., Ltd Guiyang China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5128143653","display_name":"Mi Liu","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mi Liu","raw_affiliation_strings":["Ming\u2010Chuang Hui\u2010Yuan (Guizhou) Technology Co., Ltd Guiyang China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ming\u2010Chuang Hui\u2010Yuan (Guizhou) Technology Co., Ltd Guiyang China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I154893126"],"apc_list":{"value":2780,"currency":"USD","value_usd":2780},"apc_paid":{"value":2780,"currency":"USD","value_usd":2780},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.61070249,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12120","display_name":"Air Quality Monitoring and Forecasting","score":0.227400004863739,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12120","display_name":"Air Quality Monitoring and Forecasting","score":0.227400004863739,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.20890000462532043,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T14392","display_name":"Geoscience and Mining Technology","score":0.15109999477863312,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/wind-speed-forecasting","display_name":"wind speed forecasting","score":0.8399999737739563},{"id":"https://openalex.org/keywords/underground-coal-mines","display_name":"underground coal mines","score":0.722000002861023},{"id":"https://openalex.org/keywords/stl-decomposition","display_name":"STL decomposition","score":0.9829999804496765},{"id":"https://openalex.org/keywords/temporal-convolutional-network","display_name":"temporal convolutional network","score":0.9089999794960022},{"id":"https://openalex.org/keywords/bidirectional-lstm","display_name":"bidirectional LSTM","score":0.9829999804496765},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"anomaly detection","score":0.9829999804496765},{"id":"https://openalex.org/keywords/short-time-fourier-transform","display_name":"short-time Fourier transform","score":0.9139999747276306},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"ensemble learning","score":0.6449999809265137}],"concepts":[{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.7376000285148621},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.699400007724762},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6057000160217285},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.5884000062942505},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5672000050544739},{"id":"https://openalex.org/C161067210","wikidata":"https://www.wikidata.org/wiki/Q1464943","display_name":"Wind speed","level":2,"score":0.5515999794006348},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.49309998750686646},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.46059998869895935},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.429500013589859},{"id":"https://openalex.org/C71907059","wikidata":"https://www.wikidata.org/wiki/Q223323","display_name":"Root mean square","level":2,"score":0.4066999852657318},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.38199999928474426},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.3817000091075897},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.36629998683929443},{"id":"https://openalex.org/C150217764","wikidata":"https://www.wikidata.org/wiki/Q6803607","display_name":"Mean absolute percentage error","level":3,"score":0.35510000586509705},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.33160001039505005},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.32269999384880066},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.29580000042915344},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.289000004529953},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.28679999709129333},{"id":"https://openalex.org/C167085575","wikidata":"https://www.wikidata.org/wiki/Q6803654","display_name":"Mean squared prediction error","level":2,"score":0.2702000141143799},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.26669999957084656},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.26510000228881836},{"id":"https://openalex.org/C10551718","wikidata":"https://www.wikidata.org/wiki/Q5227332","display_name":"Data pre-processing","level":2,"score":0.2614000141620636},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.2531000077724457},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2524999976158142}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1002/ese3.70650","doi":"https://doi.org/10.1002/ese3.70650","is_oa":true,"landing_page_url":"https://doi.org/10.1002/ese3.70650","pdf_url":"https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/ese3.70650","source":{"id":"https://openalex.org/S2737327489","display_name":"Energy Science & Engineering","issn_l":"2050-0505","issn":["2050-0505"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","dongbi-d","jufo-1","ki-jl-1","norway-1","oarl","poland-100","russia-white-list-2"],"host_organization":"https://openalex.org/P4310320595","host_organization_name":"Wiley","host_organization_lineage":["https://openalex.org/P4310320595"],"host_organization_lineage_names":["Wiley"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Energy Science & Engineering","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1002/ese3.70650","doi":"https://doi.org/10.1002/ese3.70650","is_oa":true,"landing_page_url":"https://doi.org/10.1002/ese3.70650","pdf_url":"https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/ese3.70650","source":{"id":"https://openalex.org/S2737327489","display_name":"Energy Science & Engineering","issn_l":"2050-0505","issn":["2050-0505"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","dongbi-d","jufo-1","ki-jl-1","norway-1","oarl","poland-100","russia-white-list-2"],"host_organization":"https://openalex.org/P4310320595","host_organization_name":"Wiley","host_organization_lineage":["https://openalex.org/P4310320595"],"host_organization_lineage_names":["Wiley"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Energy Science & Engineering","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Affordable and clean energy","score":0.75,"id":"https://metadata.un.org/sdg/7"},{"display_name":"Industry, innovation and infrastructure","score":0.5378999710083008,"id":"https://metadata.un.org/sdg/9"},{"display_name":"Climate action","score":0.5351999998092651,"id":"https://metadata.un.org/sdg/13"}],"study_designs":[],"awards":[{"id":"https://openalex.org/G7163297839","display_name":null,"funder_award_id":"62561014","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7213237019.pdf","grobid_xml":"https://content.openalex.org/works/W7213237019.grobid-xml"},"referenced_works_count":30,"referenced_works":["https://openalex.org/W2600292797","https://openalex.org/W2895135437","https://openalex.org/W2910279921","https://openalex.org/W2954586649","https://openalex.org/W3088961167","https://openalex.org/W3097101706","https://openalex.org/W3108680670","https://openalex.org/W3148107757","https://openalex.org/W3196636665","https://openalex.org/W4205242494","https://openalex.org/W4280623724","https://openalex.org/W4292814179","https://openalex.org/W4297152860","https://openalex.org/W4301397966","https://openalex.org/W4376116890","https://openalex.org/W4379931848","https://openalex.org/W4380785233","https://openalex.org/W4388915653","https://openalex.org/W4391504370","https://openalex.org/W4391685278","https://openalex.org/W4391938766","https://openalex.org/W4396582076","https://openalex.org/W4396886545","https://openalex.org/W4399044596","https://openalex.org/W4399432229","https://openalex.org/W4401766419","https://openalex.org/W4403258216","https://openalex.org/W4405620476","https://openalex.org/W4409806263","https://openalex.org/W4410990205"],"related_works":["https://openalex.org/W4416609047","https://openalex.org/W4226255812","https://openalex.org/W4404137629","https://openalex.org/W4411173645","https://openalex.org/W2884096742","https://openalex.org/W4390044764","https://openalex.org/W3095556171","https://openalex.org/W4402615325","https://openalex.org/W4308822991","https://openalex.org/W4213173073"],"abstract_inverted_index":{"ABSTRACT":[0],"An":[1],"Adaptive":[2],"Spectral\u2010Temporal":[3],"Fusion":[4],"Net":[5,127,190],"(ASTF":[6],"Net)":[7],"is":[8,78],"proposed":[9,97,125,152,188],"in":[10,19],"this":[11],"paper":[12],"to":[13,64,86,113,120],"improve":[14],"wind":[15,28],"speed":[16,29],"prediction":[17,72],"accuracy":[18],"underground":[20],"coal":[21],"mines.":[22],"The":[23,187],"model":[24,153],"first":[25],"decomposes":[26],"the":[27,75,93,96,114,124,141,151,159,179],"time":[30],"series":[31],"data":[32],"into":[33],"three":[34],"sub\u2010components:":[35],"trend":[36],"term,":[37,39],"seasonal":[38],"and":[40,58,88,178],"residual":[41,76],"term":[42],"through":[43,103],"STL":[44],"decomposition.":[45],"Subsequently,":[46],"it":[47],"employs":[48],"Temporal":[49],"Convolutional":[50,59],"Network":[51,61],"(TCN),":[52],"Bidirectional":[53],"Long":[54],"Short\u2010Term":[55],"Memory":[56],"(Bi\u2010LSTM),":[57],"Neural":[60],"(CNN)":[62],"models":[63],"predict":[65],"each":[66,145],"component":[67,77],"separately,":[68],"which":[69,91,138],"greatly":[70],"improves":[71],"accuracy.":[73],"Furthermore,":[74],"analyzed":[79],"via":[80],"Compact":[81],"Negative":[82],"Selection":[83],"Algorithm":[84],"(CNSA)":[85],"detect":[87],"label":[89],"anomalies,":[90],"increases":[92],"robustness":[94],"of":[95,144],"model.":[98],"Simultaneously,":[99],"spectral":[100],"features":[101],"extracted":[102],"Short\u2010Time":[104],"Fourier":[105],"Transform":[106],"(STFT)":[107],"are":[108],"incorporated":[109],"as":[110],"auxiliary":[111],"inputs":[112],"dynamic":[115],"ensemble":[116],"module.":[117],"In":[118],"contrast":[119],"static":[121],"weighting":[122],"models,":[123,157],"ASTF":[126,189],"introduces":[128],"a":[129,134],"feature\u2010wise":[130],"attention":[131],"mechanism":[132],"within":[133],"Multi\u2010Layer":[135],"Perceptron":[136],"(MLP),":[137],"adaptively":[139],"adjusts":[140],"aggregation":[142],"weight":[143],"sub\u2010model.":[146],"Experimental":[147],"results":[148],"demonstrate":[149],"that":[150],"outperforms":[154],"all":[155],"traditional":[156],"achieving":[158],"lowest":[160],"Root":[161],"Mean":[162,166,170,174],"Square":[163],"Error":[164,168,172,176],"(RMSE),":[165],"Squared":[167],"(MSE),":[169],"Absolute":[171],"(MAE),":[173],"Bias":[175],"(MBE),":[177],"highest":[180],"R":[181,184],"\u2010squared":[182],"(":[183],"2":[185],").":[186],"offers":[191],"practical":[192],"support":[193],"for":[194],"intelligent":[195],"ventilation":[196],"control.":[197]},"counts_by_year":[],"updated_date":"2026-09-27T08:07:27.782135","created_date":"2026-09-16T00:00:00"}