Regional suitability assessment for straw-based power generation : a machine learning approach

Assessing regional suitability of straw-based power generation (SPG) is critical to site selection on straw-based power plants (SPPs). However, the traditional evaluation methods often exhibit subjectivity in determining index weights, lacking a standard method for weight assignments This can hinder...
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Autor*in:	Hou, Yali [verfasserIn] Wang, Qunwei [verfasserIn] Tan, Tao [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Rechteinformationen:	Open Access Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International ; CC BY-NC-ND 4.0

Schlagwörter:	Straw-based power generation Regional suitability Machine learning Resource endowment

Übergeordnetes Werk:	Enthalten in: Energy strategy reviews - Amsterdam [u.a.] : Elsevier, 2012, 49(2023) vom: Sept., Artikel-ID 101173, Seite 1-14
Übergeordnetes Werk:	volume:49 ; year:2023 ; month:09 ; elocationid:101173 ; pages:1-14

Links:	Link aufrufen Link aufrufen

DOI / URN:	10.1016/j.esr.2023.101173

Katalog-ID:	1891169300

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author_variant	y h yh q w qw t t tt
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allfields	10.1016/j.esr.2023.101173 doi (DE-627)1891169300 (DE-599)KXP1891169300 DE-627 ger DE-627 rda eng Hou, Yali verfasserin (DE-588)1339915855 (DE-627)1899421750 aut Regional suitability assessment for straw-based power generation a machine learning approach Yali Hou, Qunwei Wang, Tao Tan 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 Assessing regional suitability of straw-based power generation (SPG) is critical to site selection on straw-based power plants (SPPs). However, the traditional evaluation methods often exhibit subjectivity in determining index weights, lacking a standard method for weight assignments This can hinder the quntification of evaluation accuracy. Machine learning (ML) can derive the feature importance based on empirical data and model training, and quantify the accuracy of model predictions. This study proposes an effective regional suitability assessment framework for SPG from the perspective of resource endowment, population and economic development based on ML. We selected 12 provinces with abundant agricultural straw resources in China and collect data of 1037 counties. 20 indicators with respect to resource endowment, population and economic development were mapped to feature variables of ML classifiers. Various classifiers including the base classifier, the Bagging classifier and the Boosting classifier were employed to predict the regional suitability of SPG. The results indicate that Gradient Boost Decision Tree(GBDT) and eXtreme Gradient Boosting (XGBoost) are the optimal models for predicting regional suitability of SPG in different scenarios respectively. GBDT has the highest Precision (97.94%) and can avoid wrong selection of counties suitable for SPG, while XGBoost has the highest Recall (96.67%) and can avoid miss selection of counties suitable for SPG. The feature importance ranking are also identified based on the optimal models. The results not only provide an effective tool for regional suitability evaluation of SPG but also provide important reference for the preliminary site selection of SPPs. DE-206 Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International CC BY-NC-ND 4.0 cc https://creativecommons.org/licenses/by-nc-nd/4.0/ Straw-based power generation (dpeaa)DE-206 Regional suitability (dpeaa)DE-206 Machine learning (dpeaa)DE-206 Resource endowment (dpeaa)DE-206 Wang, Qunwei verfasserin (DE-588)1049304365 (DE-627)781624770 (DE-576)403282403 aut Tan, Tao verfasserin (DE-588)1153030284 (DE-627)1014405416 (DE-576)50001535X aut Enthalten in Energy strategy reviews Amsterdam [u.a.] : Elsevier, 2012 49(2023) vom: Sept., Artikel-ID 101173, Seite 1-14 Online-Ressource (DE-627)687720338 (DE-600)2652346-2 (DE-576)360555543 nnns volume:49 year:2023 month:09 elocationid:101173 pages:1-14 https://www.sciencedirect.com/science/article/pii/S2211467X23001232/pdf Verlag kostenfrei https://doi.org/10.1016/j.esr.2023.101173 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 49 2023 9 101173 1-14 26 01 0206 4538386502 x1z 13-06-24 2403 01 DE-LFER 4547762716 00 --%%-- --%%-- n --%%-- l01 08-07-24 2403 01 DE-LFER https://doi.org/10.1016/j.esr.2023.101173 2403 01 DE-LFER https://www.sciencedirect.com/science/article/pii/S2211467X23001232/pdf
spelling	10.1016/j.esr.2023.101173 doi (DE-627)1891169300 (DE-599)KXP1891169300 DE-627 ger DE-627 rda eng Hou, Yali verfasserin (DE-588)1339915855 (DE-627)1899421750 aut Regional suitability assessment for straw-based power generation a machine learning approach Yali Hou, Qunwei Wang, Tao Tan 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 Assessing regional suitability of straw-based power generation (SPG) is critical to site selection on straw-based power plants (SPPs). However, the traditional evaluation methods often exhibit subjectivity in determining index weights, lacking a standard method for weight assignments This can hinder the quntification of evaluation accuracy. Machine learning (ML) can derive the feature importance based on empirical data and model training, and quantify the accuracy of model predictions. This study proposes an effective regional suitability assessment framework for SPG from the perspective of resource endowment, population and economic development based on ML. We selected 12 provinces with abundant agricultural straw resources in China and collect data of 1037 counties. 20 indicators with respect to resource endowment, population and economic development were mapped to feature variables of ML classifiers. Various classifiers including the base classifier, the Bagging classifier and the Boosting classifier were employed to predict the regional suitability of SPG. The results indicate that Gradient Boost Decision Tree(GBDT) and eXtreme Gradient Boosting (XGBoost) are the optimal models for predicting regional suitability of SPG in different scenarios respectively. GBDT has the highest Precision (97.94%) and can avoid wrong selection of counties suitable for SPG, while XGBoost has the highest Recall (96.67%) and can avoid miss selection of counties suitable for SPG. The feature importance ranking are also identified based on the optimal models. The results not only provide an effective tool for regional suitability evaluation of SPG but also provide important reference for the preliminary site selection of SPPs. DE-206 Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International CC BY-NC-ND 4.0 cc https://creativecommons.org/licenses/by-nc-nd/4.0/ Straw-based power generation (dpeaa)DE-206 Regional suitability (dpeaa)DE-206 Machine learning (dpeaa)DE-206 Resource endowment (dpeaa)DE-206 Wang, Qunwei verfasserin (DE-588)1049304365 (DE-627)781624770 (DE-576)403282403 aut Tan, Tao verfasserin (DE-588)1153030284 (DE-627)1014405416 (DE-576)50001535X aut Enthalten in Energy strategy reviews Amsterdam [u.a.] : Elsevier, 2012 49(2023) vom: Sept., Artikel-ID 101173, Seite 1-14 Online-Ressource (DE-627)687720338 (DE-600)2652346-2 (DE-576)360555543 nnns volume:49 year:2023 month:09 elocationid:101173 pages:1-14 https://www.sciencedirect.com/science/article/pii/S2211467X23001232/pdf Verlag kostenfrei https://doi.org/10.1016/j.esr.2023.101173 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 49 2023 9 101173 1-14 26 01 0206 4538386502 x1z 13-06-24 2403 01 DE-LFER 4547762716 00 --%%-- --%%-- n --%%-- l01 08-07-24 2403 01 DE-LFER https://doi.org/10.1016/j.esr.2023.101173 2403 01 DE-LFER https://www.sciencedirect.com/science/article/pii/S2211467X23001232/pdf
allfields_unstemmed	10.1016/j.esr.2023.101173 doi (DE-627)1891169300 (DE-599)KXP1891169300 DE-627 ger DE-627 rda eng Hou, Yali verfasserin (DE-588)1339915855 (DE-627)1899421750 aut Regional suitability assessment for straw-based power generation a machine learning approach Yali Hou, Qunwei Wang, Tao Tan 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 Assessing regional suitability of straw-based power generation (SPG) is critical to site selection on straw-based power plants (SPPs). However, the traditional evaluation methods often exhibit subjectivity in determining index weights, lacking a standard method for weight assignments This can hinder the quntification of evaluation accuracy. Machine learning (ML) can derive the feature importance based on empirical data and model training, and quantify the accuracy of model predictions. This study proposes an effective regional suitability assessment framework for SPG from the perspective of resource endowment, population and economic development based on ML. We selected 12 provinces with abundant agricultural straw resources in China and collect data of 1037 counties. 20 indicators with respect to resource endowment, population and economic development were mapped to feature variables of ML classifiers. Various classifiers including the base classifier, the Bagging classifier and the Boosting classifier were employed to predict the regional suitability of SPG. The results indicate that Gradient Boost Decision Tree(GBDT) and eXtreme Gradient Boosting (XGBoost) are the optimal models for predicting regional suitability of SPG in different scenarios respectively. GBDT has the highest Precision (97.94%) and can avoid wrong selection of counties suitable for SPG, while XGBoost has the highest Recall (96.67%) and can avoid miss selection of counties suitable for SPG. The feature importance ranking are also identified based on the optimal models. The results not only provide an effective tool for regional suitability evaluation of SPG but also provide important reference for the preliminary site selection of SPPs. DE-206 Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International CC BY-NC-ND 4.0 cc https://creativecommons.org/licenses/by-nc-nd/4.0/ Straw-based power generation (dpeaa)DE-206 Regional suitability (dpeaa)DE-206 Machine learning (dpeaa)DE-206 Resource endowment (dpeaa)DE-206 Wang, Qunwei verfasserin (DE-588)1049304365 (DE-627)781624770 (DE-576)403282403 aut Tan, Tao verfasserin (DE-588)1153030284 (DE-627)1014405416 (DE-576)50001535X aut Enthalten in Energy strategy reviews Amsterdam [u.a.] : Elsevier, 2012 49(2023) vom: Sept., Artikel-ID 101173, Seite 1-14 Online-Ressource (DE-627)687720338 (DE-600)2652346-2 (DE-576)360555543 nnns volume:49 year:2023 month:09 elocationid:101173 pages:1-14 https://www.sciencedirect.com/science/article/pii/S2211467X23001232/pdf Verlag kostenfrei https://doi.org/10.1016/j.esr.2023.101173 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 49 2023 9 101173 1-14 26 01 0206 4538386502 x1z 13-06-24 2403 01 DE-LFER 4547762716 00 --%%-- --%%-- n --%%-- l01 08-07-24 2403 01 DE-LFER https://doi.org/10.1016/j.esr.2023.101173 2403 01 DE-LFER https://www.sciencedirect.com/science/article/pii/S2211467X23001232/pdf
allfieldsGer	10.1016/j.esr.2023.101173 doi (DE-627)1891169300 (DE-599)KXP1891169300 DE-627 ger DE-627 rda eng Hou, Yali verfasserin (DE-588)1339915855 (DE-627)1899421750 aut Regional suitability assessment for straw-based power generation a machine learning approach Yali Hou, Qunwei Wang, Tao Tan 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 Assessing regional suitability of straw-based power generation (SPG) is critical to site selection on straw-based power plants (SPPs). However, the traditional evaluation methods often exhibit subjectivity in determining index weights, lacking a standard method for weight assignments This can hinder the quntification of evaluation accuracy. Machine learning (ML) can derive the feature importance based on empirical data and model training, and quantify the accuracy of model predictions. This study proposes an effective regional suitability assessment framework for SPG from the perspective of resource endowment, population and economic development based on ML. We selected 12 provinces with abundant agricultural straw resources in China and collect data of 1037 counties. 20 indicators with respect to resource endowment, population and economic development were mapped to feature variables of ML classifiers. Various classifiers including the base classifier, the Bagging classifier and the Boosting classifier were employed to predict the regional suitability of SPG. The results indicate that Gradient Boost Decision Tree(GBDT) and eXtreme Gradient Boosting (XGBoost) are the optimal models for predicting regional suitability of SPG in different scenarios respectively. GBDT has the highest Precision (97.94%) and can avoid wrong selection of counties suitable for SPG, while XGBoost has the highest Recall (96.67%) and can avoid miss selection of counties suitable for SPG. The feature importance ranking are also identified based on the optimal models. The results not only provide an effective tool for regional suitability evaluation of SPG but also provide important reference for the preliminary site selection of SPPs. DE-206 Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International CC BY-NC-ND 4.0 cc https://creativecommons.org/licenses/by-nc-nd/4.0/ Straw-based power generation (dpeaa)DE-206 Regional suitability (dpeaa)DE-206 Machine learning (dpeaa)DE-206 Resource endowment (dpeaa)DE-206 Wang, Qunwei verfasserin (DE-588)1049304365 (DE-627)781624770 (DE-576)403282403 aut Tan, Tao verfasserin (DE-588)1153030284 (DE-627)1014405416 (DE-576)50001535X aut Enthalten in Energy strategy reviews Amsterdam [u.a.] : Elsevier, 2012 49(2023) vom: Sept., Artikel-ID 101173, Seite 1-14 Online-Ressource (DE-627)687720338 (DE-600)2652346-2 (DE-576)360555543 nnns volume:49 year:2023 month:09 elocationid:101173 pages:1-14 https://www.sciencedirect.com/science/article/pii/S2211467X23001232/pdf Verlag kostenfrei https://doi.org/10.1016/j.esr.2023.101173 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 49 2023 9 101173 1-14 26 01 0206 4538386502 x1z 13-06-24 2403 01 DE-LFER 4547762716 00 --%%-- --%%-- n --%%-- l01 08-07-24 2403 01 DE-LFER https://doi.org/10.1016/j.esr.2023.101173 2403 01 DE-LFER https://www.sciencedirect.com/science/article/pii/S2211467X23001232/pdf
allfieldsSound	10.1016/j.esr.2023.101173 doi (DE-627)1891169300 (DE-599)KXP1891169300 DE-627 ger DE-627 rda eng Hou, Yali verfasserin (DE-588)1339915855 (DE-627)1899421750 aut Regional suitability assessment for straw-based power generation a machine learning approach Yali Hou, Qunwei Wang, Tao Tan 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 Assessing regional suitability of straw-based power generation (SPG) is critical to site selection on straw-based power plants (SPPs). However, the traditional evaluation methods often exhibit subjectivity in determining index weights, lacking a standard method for weight assignments This can hinder the quntification of evaluation accuracy. Machine learning (ML) can derive the feature importance based on empirical data and model training, and quantify the accuracy of model predictions. This study proposes an effective regional suitability assessment framework for SPG from the perspective of resource endowment, population and economic development based on ML. We selected 12 provinces with abundant agricultural straw resources in China and collect data of 1037 counties. 20 indicators with respect to resource endowment, population and economic development were mapped to feature variables of ML classifiers. Various classifiers including the base classifier, the Bagging classifier and the Boosting classifier were employed to predict the regional suitability of SPG. The results indicate that Gradient Boost Decision Tree(GBDT) and eXtreme Gradient Boosting (XGBoost) are the optimal models for predicting regional suitability of SPG in different scenarios respectively. GBDT has the highest Precision (97.94%) and can avoid wrong selection of counties suitable for SPG, while XGBoost has the highest Recall (96.67%) and can avoid miss selection of counties suitable for SPG. The feature importance ranking are also identified based on the optimal models. The results not only provide an effective tool for regional suitability evaluation of SPG but also provide important reference for the preliminary site selection of SPPs. DE-206 Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International CC BY-NC-ND 4.0 cc https://creativecommons.org/licenses/by-nc-nd/4.0/ Straw-based power generation (dpeaa)DE-206 Regional suitability (dpeaa)DE-206 Machine learning (dpeaa)DE-206 Resource endowment (dpeaa)DE-206 Wang, Qunwei verfasserin (DE-588)1049304365 (DE-627)781624770 (DE-576)403282403 aut Tan, Tao verfasserin (DE-588)1153030284 (DE-627)1014405416 (DE-576)50001535X aut Enthalten in Energy strategy reviews Amsterdam [u.a.] : Elsevier, 2012 49(2023) vom: Sept., Artikel-ID 101173, Seite 1-14 Online-Ressource (DE-627)687720338 (DE-600)2652346-2 (DE-576)360555543 nnns volume:49 year:2023 month:09 elocationid:101173 pages:1-14 https://www.sciencedirect.com/science/article/pii/S2211467X23001232/pdf Verlag kostenfrei https://doi.org/10.1016/j.esr.2023.101173 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 49 2023 9 101173 1-14 26 01 0206 4538386502 x1z 13-06-24 2403 01 DE-LFER 4547762716 00 --%%-- --%%-- n --%%-- l01 08-07-24 2403 01 DE-LFER https://doi.org/10.1016/j.esr.2023.101173 2403 01 DE-LFER https://www.sciencedirect.com/science/article/pii/S2211467X23001232/pdf
language	English
source	Enthalten in Energy strategy reviews 49(2023) vom: Sept., Artikel-ID 101173, Seite 1-14 volume:49 year:2023 month:09 elocationid:101173 pages:1-14
sourceStr	Enthalten in Energy strategy reviews 49(2023) vom: Sept., Artikel-ID 101173, Seite 1-14 volume:49 year:2023 month:09 elocationid:101173 pages:1-14
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institution	findex.gbv.de
selectbib_iln_str_mv	26@1z 2403@01
topic_facet	Straw-based power generation Regional suitability Machine learning Resource endowment
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container_title	Energy strategy reviews
authorswithroles_txt_mv	Hou, Yali @@aut@@ Wang, Qunwei @@aut@@ Tan, Tao @@aut@@
publishDateDaySort_date	2023-09-01T00:00:00Z
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topic_title	Regional suitability assessment for straw-based power generation a machine learning approach Yali Hou, Qunwei Wang, Tao Tan Straw-based power generation (dpeaa)DE-206 Regional suitability (dpeaa)DE-206 Machine learning (dpeaa)DE-206 Resource endowment (dpeaa)DE-206
topic	misc Straw-based power generation misc Regional suitability misc Machine learning misc Resource endowment
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title_full	Regional suitability assessment for straw-based power generation a machine learning approach Yali Hou, Qunwei Wang, Tao Tan
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title_sort	regional suitability assessment for straw-based power generationa machine learning approach
title_auth	Regional suitability assessment for straw-based power generation a machine learning approach
abstract	Assessing regional suitability of straw-based power generation (SPG) is critical to site selection on straw-based power plants (SPPs). However, the traditional evaluation methods often exhibit subjectivity in determining index weights, lacking a standard method for weight assignments This can hinder the quntification of evaluation accuracy. Machine learning (ML) can derive the feature importance based on empirical data and model training, and quantify the accuracy of model predictions. This study proposes an effective regional suitability assessment framework for SPG from the perspective of resource endowment, population and economic development based on ML. We selected 12 provinces with abundant agricultural straw resources in China and collect data of 1037 counties. 20 indicators with respect to resource endowment, population and economic development were mapped to feature variables of ML classifiers. Various classifiers including the base classifier, the Bagging classifier and the Boosting classifier were employed to predict the regional suitability of SPG. The results indicate that Gradient Boost Decision Tree(GBDT) and eXtreme Gradient Boosting (XGBoost) are the optimal models for predicting regional suitability of SPG in different scenarios respectively. GBDT has the highest Precision (97.94%) and can avoid wrong selection of counties suitable for SPG, while XGBoost has the highest Recall (96.67%) and can avoid miss selection of counties suitable for SPG. The feature importance ranking are also identified based on the optimal models. The results not only provide an effective tool for regional suitability evaluation of SPG but also provide important reference for the preliminary site selection of SPPs.
abstractGer	Assessing regional suitability of straw-based power generation (SPG) is critical to site selection on straw-based power plants (SPPs). However, the traditional evaluation methods often exhibit subjectivity in determining index weights, lacking a standard method for weight assignments This can hinder the quntification of evaluation accuracy. Machine learning (ML) can derive the feature importance based on empirical data and model training, and quantify the accuracy of model predictions. This study proposes an effective regional suitability assessment framework for SPG from the perspective of resource endowment, population and economic development based on ML. We selected 12 provinces with abundant agricultural straw resources in China and collect data of 1037 counties. 20 indicators with respect to resource endowment, population and economic development were mapped to feature variables of ML classifiers. Various classifiers including the base classifier, the Bagging classifier and the Boosting classifier were employed to predict the regional suitability of SPG. The results indicate that Gradient Boost Decision Tree(GBDT) and eXtreme Gradient Boosting (XGBoost) are the optimal models for predicting regional suitability of SPG in different scenarios respectively. GBDT has the highest Precision (97.94%) and can avoid wrong selection of counties suitable for SPG, while XGBoost has the highest Recall (96.67%) and can avoid miss selection of counties suitable for SPG. The feature importance ranking are also identified based on the optimal models. The results not only provide an effective tool for regional suitability evaluation of SPG but also provide important reference for the preliminary site selection of SPPs.
abstract_unstemmed	Assessing regional suitability of straw-based power generation (SPG) is critical to site selection on straw-based power plants (SPPs). However, the traditional evaluation methods often exhibit subjectivity in determining index weights, lacking a standard method for weight assignments This can hinder the quntification of evaluation accuracy. Machine learning (ML) can derive the feature importance based on empirical data and model training, and quantify the accuracy of model predictions. This study proposes an effective regional suitability assessment framework for SPG from the perspective of resource endowment, population and economic development based on ML. We selected 12 provinces with abundant agricultural straw resources in China and collect data of 1037 counties. 20 indicators with respect to resource endowment, population and economic development were mapped to feature variables of ML classifiers. Various classifiers including the base classifier, the Bagging classifier and the Boosting classifier were employed to predict the regional suitability of SPG. The results indicate that Gradient Boost Decision Tree(GBDT) and eXtreme Gradient Boosting (XGBoost) are the optimal models for predicting regional suitability of SPG in different scenarios respectively. GBDT has the highest Precision (97.94%) and can avoid wrong selection of counties suitable for SPG, while XGBoost has the highest Recall (96.67%) and can avoid miss selection of counties suitable for SPG. The feature importance ranking are also identified based on the optimal models. The results not only provide an effective tool for regional suitability evaluation of SPG but also provide important reference for the preliminary site selection of SPPs.
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title_short	Regional suitability assessment for straw-based power generation
url	https://www.sciencedirect.com/science/article/pii/S2211467X23001232/pdf https://doi.org/10.1016/j.esr.2023.101173
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a2200265 4500</leader><controlfield tag="001">1891169300</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240823091627.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240613s2023 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.esr.2023.101173</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)1891169300</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)KXP1891169300</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Hou, Yali</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(DE-588)1339915855</subfield><subfield code="0">(DE-627)1899421750</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Regional suitability assessment for straw-based power generation</subfield><subfield code="b">a machine learning approach</subfield><subfield code="c">Yali Hou, Qunwei Wang, Tao Tan</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="506" ind1="0" ind2=" "><subfield code="q">DE-206</subfield><subfield code="a">Open Access</subfield><subfield code="e">Controlled Vocabulary for Access Rights</subfield><subfield code="u">http://purl.org/coar/access_right/c_abf2</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Assessing regional suitability of straw-based power generation (SPG) is critical to site selection on straw-based power plants (SPPs). However, the traditional evaluation methods often exhibit subjectivity in determining index weights, lacking a standard method for weight assignments This can hinder the quntification of evaluation accuracy. Machine learning (ML) can derive the feature importance based on empirical data and model training, and quantify the accuracy of model predictions. This study proposes an effective regional suitability assessment framework for SPG from the perspective of resource endowment, population and economic development based on ML. We selected 12 provinces with abundant agricultural straw resources in China and collect data of 1037 counties. 20 indicators with respect to resource endowment, population and economic development were mapped to feature variables of ML classifiers. Various classifiers including the base classifier, the Bagging classifier and the Boosting classifier were employed to predict the regional suitability of SPG. The results indicate that Gradient Boost Decision Tree(GBDT) and eXtreme Gradient Boosting (XGBoost) are the optimal models for predicting regional suitability of SPG in different scenarios respectively. GBDT has the highest Precision (97.94%) and can avoid wrong selection of counties suitable for SPG, while XGBoost has the highest Recall (96.67%) and can avoid miss selection of counties suitable for SPG. The feature importance ranking are also identified based on the optimal models. 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Regional suitability assessment for straw-based power generation : a machine learning approach

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