Combining Probability Density Forecasts for Power Electrical Loads
Researchers have proposed various probabilistic load forecasting models in the form of quantiles, densities, or intervals to describe the uncertainties of future energy demand. Density forecasts can provide more uncertainty information than can be expressed by just the quantile and interval. However...
Uloženo v:
| Vydáno v: | IEEE transactions on smart grid Ročník 11; číslo 2; s. 1679 - 1690 |
|---|---|
| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Piscataway
IEEE
01.03.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Témata: | |
| ISSN: | 1949-3053, 1949-3061 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Researchers have proposed various probabilistic load forecasting models in the form of quantiles, densities, or intervals to describe the uncertainties of future energy demand. Density forecasts can provide more uncertainty information than can be expressed by just the quantile and interval. However, the combining method for density forecasts is seldom investigated. This paper proposes a novel and easily implemented approach to combine density probabilistic load forecasts to further improve the performance of the final probabilistic forecasts. The combination problem is formulated as an optimization problem to minimize the continuous ranked probability score of the combined model by searching the weights of different individual methods. Under the Gaussian mixture distribution assumption of the density forecasts, the problem is cast to a linearly constrained quadratic programming problem and can be solved efficiently. Case studies on the electric load datasets of eight areas verify the effectiveness of the proposed method. |
|---|---|
| AbstractList | Researchers have proposed various probabilistic load forecasting models in the form of quantiles, densities, or intervals to describe the uncertainties of future energy demand. Density forecasts can provide more uncertainty information than can be expressed by just the quantile and interval. However, the combining method for density forecasts is seldom investigated. This paper proposes a novel and easily implemented approach to combine density probabilistic load forecasts to further improve the performance of the final probabilistic forecasts. The combination problem is formulated as an optimization problem to minimize the continuous ranked probability score of the combined model by searching the weights of different individual methods. Under the Gaussian mixture distribution assumption of the density forecasts, the problem is cast to a linearly constrained quadratic programming problem and can be solved efficiently. Case studies on the electric load datasets of eight areas verify the effectiveness of the proposed method. |
| Author | Wang, Yi Li, Tianyi Zhang, Ning |
| Author_xml | – sequence: 1 givenname: Tianyi orcidid: 0000-0001-9572-4164 surname: Li fullname: Li, Tianyi organization: Department of Electrical Engineering, State Key Laboratories of Power Systems, Tsinghua University, Beijing, China – sequence: 2 givenname: Yi orcidid: 0000-0003-1143-0666 surname: Wang fullname: Wang, Yi organization: Department of Electrical Engineering, State Key Laboratories of Power Systems, Tsinghua University, Beijing, China – sequence: 3 givenname: Ning orcidid: 0000-0003-0366-4657 surname: Zhang fullname: Zhang, Ning email: ningzhang@tsinghua.edu.cn organization: Department of Electrical Engineering, State Key Laboratories of Power Systems, Tsinghua University, Beijing, China |
| BookMark | eNp9kE1rAjEQhkOxUGu9F3pZ6HltJtmP5NhatQWhQu05ZGNSImtik0jx37uieOihc3nn8D4z8NyinvNOI3QPeASA-dPyczYiGPiI8IJgUlyhPvCC5xRX0LvsJb1BwxjXuBtKaUV4H72M_aaxzrrvbBF8Ixvb2rTPXrWLx5z6oJWMKWbGh2zhf3XIJq1WKVgl22zu5SreoWsj26iH5xygr-lkOX7L5x-z9_HzPFeEQ8pBGlVwUrFaQ0lMSZsSAAiuGVthA1ipppaKNwxjUpkaG1rIlSSacVkqDYQO0OPp7jb4n52OSaz9LrjupSC05LzDaNW18Kmlgo8xaCO2wW5k2AvA4ihLdLLEUZY4y-qQ6g-ibJLJepeCtO1_4MMJtFrryx_GCsppTQ-lhHdF |
| CODEN | ITSGBQ |
| CitedBy_id | crossref_primary_10_1016_j_apenergy_2022_119507 crossref_primary_10_1016_j_knosys_2024_111729 crossref_primary_10_3390_s22197173 crossref_primary_10_3390_app12199603 crossref_primary_10_1007_s10479_022_04599_2 crossref_primary_10_1016_j_egyr_2024_08_002 crossref_primary_10_1109_TSG_2021_3132039 crossref_primary_10_1049_gtd2_13273 crossref_primary_10_1109_TSG_2021_3107159 crossref_primary_10_1109_TSG_2022_3158387 crossref_primary_10_1109_TII_2023_3341242 crossref_primary_10_3390_en13236191 crossref_primary_10_1109_TSG_2023_3296647 crossref_primary_10_3390_en15218308 crossref_primary_10_1051_e3sconf_202452201017 crossref_primary_10_1109_TII_2022_3228383 crossref_primary_10_1063_5_0256079 crossref_primary_10_1016_j_apenergy_2024_123751 crossref_primary_10_1109_TSTE_2021_3086851 crossref_primary_10_3390_en17246211 crossref_primary_10_1007_s42835_022_01127_x crossref_primary_10_1109_JIOT_2021_3063677 crossref_primary_10_3390_app13116520 crossref_primary_10_1016_j_epsr_2023_109804 crossref_primary_10_1109_TPWRS_2024_3502114 crossref_primary_10_1049_rpg2_12918 crossref_primary_10_1109_TIE_2021_3102431 crossref_primary_10_1016_j_egyr_2022_03_117 crossref_primary_10_1109_JSYST_2021_3073493 crossref_primary_10_1109_TNSE_2023_3270632 crossref_primary_10_1016_j_rser_2021_110735 crossref_primary_10_1016_j_apenergy_2025_126518 crossref_primary_10_1007_s12667_024_00704_5 crossref_primary_10_1109_TSG_2020_2972513 crossref_primary_10_1016_j_apenergy_2021_118341 crossref_primary_10_1016_j_ijepes_2022_108243 crossref_primary_10_1049_rpg2_70019 crossref_primary_10_1109_TSG_2020_2995777 crossref_primary_10_1016_j_ijepes_2023_109253 crossref_primary_10_1109_TII_2023_3331076 crossref_primary_10_1016_j_energy_2021_122317 crossref_primary_10_1016_j_energy_2021_122955 crossref_primary_10_1109_TIA_2025_3529796 crossref_primary_10_1109_TSG_2022_3226423 |
| Cites_doi | 10.1016/j.ijforecast.2016.02.001 10.1109/TSG.2018.2807985 10.1016/j.ijforecast.2015.11.004 10.1016/j.energy.2016.08.023 10.2307/2527342 10.1109/TSG.2018.2817284 10.1016/j.rser.2017.05.234 10.1109/TSG.2018.2833869 10.1016/j.apenergy.2017.11.035 10.1109/TSG.2013.2280649 10.1016/j.omega.2014.08.008 10.1145/2939672.2939785 10.1109/TSG.2017.2763827 10.1198/jbes.2010.08110 10.1109/TPWRS.2018.2830785 10.1023/A:1021765131316 10.1109/TSG.2013.2274465 10.1109/TPWRS.2015.2438322 10.1016/j.ijforecast.2015.11.005 10.1016/S0047-259X(03)00079-4 10.1109/TIE.2018.2803732 10.1109/TSTE.2018.2831238 10.1109/TSG.2016.2527820 10.1146/annurev-statistics-062713-085831 10.1016/j.ijforecast.2019.02.006 10.1109/TIE.2017.2714127 10.1016/j.apenergy.2018.02.165 10.1016/j.ijforecast.2015.11.011 10.1109/TSG.2018.2818167 10.1109/TSTE.2015.2441747 10.1109/TPWRS.2009.2030271 |
| ContentType | Journal Article |
| Copyright | Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2020 |
| Copyright_xml | – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2020 |
| DBID | 97E RIA RIE AAYXX CITATION 7SP 7TB 8FD FR3 KR7 L7M |
| DOI | 10.1109/TSG.2019.2942024 |
| DatabaseName | IEEE Xplore (IEEE) IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef Electronics & Communications Abstracts Mechanical & Transportation Engineering Abstracts Technology Research Database Engineering Research Database Civil Engineering Abstracts Advanced Technologies Database with Aerospace |
| DatabaseTitle | CrossRef Civil Engineering Abstracts Engineering Research Database Technology Research Database Mechanical & Transportation Engineering Abstracts Advanced Technologies Database with Aerospace Electronics & Communications Abstracts |
| DatabaseTitleList | Civil Engineering Abstracts |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 1949-3061 |
| EndPage | 1690 |
| ExternalDocumentID | 10_1109_TSG_2019_2942024 8843937 |
| Genre | orig-research |
| GrantInformation_xml | – fundername: National Basic Research Program of China (973 Program); National Key Research and Development Program of China grantid: 2016YFB0900100 funderid: 10.13039/501100012166 – fundername: Technical Project of the State Grid: Research and Application of Internet-Based Operation Platform for Ubiquitous Internet of Things in Electricity |
| GroupedDBID | 0R~ 4.4 5VS 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACIWK AENEX AGQYO AGSQL AHBIQ AKJIK AKQYR ALMA_UNASSIGNED_HOLDINGS ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ EBS EJD HZ~ IFIPE IPLJI JAVBF M43 O9- OCL P2P RIA RIE RNS AAYXX CITATION 7SP 7TB 8FD FR3 KR7 L7M |
| ID | FETCH-LOGICAL-c291t-1afc492687e152f53b511120788d0f10ccb7ac9b80026f70f34ada2e89a5ce123 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 54 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000519592100065&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1949-3053 |
| IngestDate | Mon Jun 30 09:40:49 EDT 2025 Sat Nov 29 03:45:56 EST 2025 Tue Nov 18 22:20:44 EST 2025 Wed Aug 27 06:30:02 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 2 |
| Language | English |
| License | https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html https://doi.org/10.15223/policy-029 https://doi.org/10.15223/policy-037 |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c291t-1afc492687e152f53b511120788d0f10ccb7ac9b80026f70f34ada2e89a5ce123 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0001-9572-4164 0000-0003-1143-0666 0000-0003-0366-4657 |
| PQID | 2359902636 |
| PQPubID | 2040408 |
| PageCount | 12 |
| ParticipantIDs | ieee_primary_8843937 crossref_primary_10_1109_TSG_2019_2942024 proquest_journals_2359902636 crossref_citationtrail_10_1109_TSG_2019_2942024 |
| PublicationCentury | 2000 |
| PublicationDate | 2020-03-01 |
| PublicationDateYYYYMMDD | 2020-03-01 |
| PublicationDate_xml | – month: 03 year: 2020 text: 2020-03-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | Piscataway |
| PublicationPlace_xml | – name: Piscataway |
| PublicationTitle | IEEE transactions on smart grid |
| PublicationTitleAbbrev | TSG |
| PublicationYear | 2020 |
| Publisher | IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher_xml | – name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| References | ref13 ref12 ref37 ref15 ref36 ref14 ref30 ref11 ref10 ref2 ref1 chollet (ref35) 2015 ref17 ioffe (ref33) 2015; 1 ref16 ref19 ref18 li (ref38) 2019 limaye (ref31) 1984 ref24 ref23 ref26 ref25 ref20 ref22 fikri (ref29) 1975 ref21 ref28 ref27 ref8 ref7 ref9 ref4 matthews (ref34) 2017; 18 ref3 ref6 ref5 williams (ref32) 2006; 2 |
| References_xml | – ident: ref3 doi: 10.1016/j.ijforecast.2016.02.001 – ident: ref19 doi: 10.1109/TSG.2018.2807985 – ident: ref6 doi: 10.1016/j.ijforecast.2015.11.004 – ident: ref9 doi: 10.1016/j.energy.2016.08.023 – year: 2019 ident: ref38 publication-title: GitHub-Combining Probability Density Forecasts – ident: ref14 doi: 10.2307/2527342 – ident: ref27 doi: 10.1109/TSG.2018.2817284 – ident: ref21 doi: 10.1016/j.rser.2017.05.234 – ident: ref25 doi: 10.1109/TSG.2018.2833869 – ident: ref10 doi: 10.1016/j.apenergy.2017.11.035 – year: 1984 ident: ref31 article-title: Selected statistical methods for analysis of load research data – ident: ref24 doi: 10.1109/TSG.2013.2280649 – ident: ref7 doi: 10.1016/j.omega.2014.08.008 – ident: ref36 doi: 10.1145/2939672.2939785 – ident: ref16 doi: 10.1109/TSG.2017.2763827 – volume: 2 year: 2006 ident: ref32 publication-title: Gaussian Processes for Machine Learning – ident: ref18 doi: 10.1198/jbes.2010.08110 – ident: ref1 doi: 10.1109/TPWRS.2018.2830785 – ident: ref37 doi: 10.1023/A:1021765131316 – ident: ref22 doi: 10.1109/TSG.2013.2274465 – ident: ref20 doi: 10.1109/TPWRS.2015.2438322 – ident: ref5 doi: 10.1016/j.ijforecast.2015.11.005 – ident: ref28 doi: 10.1016/S0047-259X(03)00079-4 – ident: ref17 doi: 10.1109/TIE.2018.2803732 – ident: ref26 doi: 10.1109/TSTE.2018.2831238 – ident: ref8 doi: 10.1109/TSG.2016.2527820 – volume: 18 start-page: 1 year: 2017 ident: ref34 article-title: GPflow: A Gaussian process library using TensorFlow publication-title: J Mach Learn Res – ident: ref13 doi: 10.1146/annurev-statistics-062713-085831 – year: 2015 ident: ref35 publication-title: Keras – year: 1975 ident: ref29 article-title: Statistical load analysis for distribution network planning – ident: ref4 doi: 10.1016/j.ijforecast.2019.02.006 – volume: 1 start-page: 448 year: 2015 ident: ref33 article-title: Batch normalization: Accelerating deep network training by reducing internal covariate shift publication-title: Proc 32nd Int Conf Mach Learn (ICML) – ident: ref11 doi: 10.1109/TIE.2017.2714127 – ident: ref12 doi: 10.1016/j.apenergy.2018.02.165 – ident: ref15 doi: 10.1016/j.ijforecast.2015.11.011 – ident: ref2 doi: 10.1109/TSG.2018.2818167 – ident: ref23 doi: 10.1109/TSTE.2015.2441747 – ident: ref30 doi: 10.1109/TPWRS.2009.2030271 |
| SSID | ssj0000333629 |
| Score | 2.4909382 |
| Snippet | Researchers have proposed various probabilistic load forecasting models in the form of quantiles, densities, or intervals to describe the uncertainties of... |
| SourceID | proquest crossref ieee |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 1679 |
| SubjectTerms | continuous ranked probability score Density density forecasting Electrical loads Electricity consumption ensemble learning Estimation Forecasting Gaussian distribution linearly constrained quadratic programming Load forecasting Load modeling Mathematical models Optimization Performance enhancement Predictive models Probabilistic load forecasting Probabilistic logic Quadratic programming Quantiles Statistical analysis Uncertainty |
| Title | Combining Probability Density Forecasts for Power Electrical Loads |
| URI | https://ieeexplore.ieee.org/document/8843937 https://www.proquest.com/docview/2359902636 |
| Volume | 11 |
| WOSCitedRecordID | wos000519592100065&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVIEE databaseName: IEEE Electronic Library (IEL) customDbUrl: eissn: 1949-3061 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000333629 issn: 1949-3053 databaseCode: RIE dateStart: 20100101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3dS8MwED_m8EEf_JridEoffBHsliZpkzz6semDjIET9lbSNAVBNlk7wf_eS9YNRRF8agsJlN_lcne5y_0ALoi1keXchllUmJBbrnEfpCIkptD4hQECyzzZhBgO5WSiRg24Wt-Fsdb64jPbda8-l5_PzMIdlfWk5K5_2wZsCJEs72qtz1MIY7gXK59E5i6dH7NVVpKo3vjp3pVxqS5VHMN9_s0KeVqVH3uxNzCD3f_92h7s1I5kcL2U_D407PQAtr-0F2zBDSp75gkggtEc9dbXwX4Ed65mHZ-OldPosioDdFyDkaNLC_qeFccJLnic6bw8hOdBf3z7ENacCaGhKqrCSBfG9QCUwqJlLmKWoUcVUXQEZE6KiBiTCW1U5vzEpBCkYFznmlqpdGwsmrEjaE5nU3sMAUZeMlOCUpIlPDeJZlGC-q2JlXFcSN2G3grD1NQNxR2vxWvqAwuiUkQ9dainNeptuFzPeFs20_hjbMuhvB5XA9yGzkpMaa1tZUpZjEaVJiw5-X3WKWxRFyf72rEONKv5wp7BpnmvXsr5uV9In5thw_M |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3dS8MwED_mB6gPfk1xOrUPvgh2S5O0TR792Jw4x8AJeytpmoIgm6xT8L_3knVDUQSf2kIOyl3uK3e5H8AZMSYwnBs_DXLtc8MV2kEa-0TnCr8wQWCpA5uIez0xHMp-BS4Wd2GMMa75zDTsq6vlZ2P9Zo_KmkJwO79tCVZCzimZ3dZanKgQxtAaS1dG5ragH7J5XZLI5uDx1jZyyQaVSEr5Nz_kgFV-WGPnYtpb__u5bdgsQ0nvcib7HaiY0S5sfBkwWIUrVPfUQUB4_QlqruuE_fBubNc6Pi0up1bFtPAwdPX6FjDNazlcHCs6rztWWbEHT-3W4Lrjl6gJvqYymPqByrWdAihig745D1mKMVVAMRQQGckDonUaKy1TGylGeUxyxlWmqBFShdqgI9uH5dF4ZA7Aw9xLpDKmlKQRz3SkWBChhitiRBjmQtWgOedhosuR4hbZ4iVxqQWRCXI9sVxPSq7X4HxB8Tobp_HH2qrl8mJdyeAa1OdiSkp9KxLKQnSrNGLR4e9Up7DWGTx0k-5d7_4I1qnNml0nWR2Wp5M3cwyr-n36XExO3Kb6BPsYxzo |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Combining+Probability+Density+Forecasts+for+Power+Electrical+Loads&rft.jtitle=IEEE+transactions+on+smart+grid&rft.au=Li%2C+Tianyi&rft.au=Wang%2C+Yi&rft.au=Zhang%2C+Ning&rft.date=2020-03-01&rft.pub=IEEE&rft.issn=1949-3053&rft.volume=11&rft.issue=2&rft.spage=1679&rft.epage=1690&rft_id=info:doi/10.1109%2FTSG.2019.2942024&rft.externalDocID=8843937 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1949-3053&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1949-3053&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1949-3053&client=summon |