Day-ahead price forecasting in restructured power systems using artificial neural networks
Over the past 15 years most electricity supply companies around the world have been restructured from monopoly utilities to deregulated competitive electricity markets. Market participants in the restructured electricity markets find short-term electricity price forecasting (STPF) crucial in formula...
Uloženo v:
| Vydáno v: | Electric power systems research Ročník 78; číslo 8; s. 1332 - 1342 |
|---|---|
| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Amsterdam
Elsevier B.V
01.08.2008
Elsevier |
| Témata: | |
| ISSN: | 0378-7796, 1873-2046 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Over the past 15 years most electricity supply companies around the world have been restructured from monopoly utilities to deregulated competitive electricity markets. Market participants in the restructured electricity markets find short-term electricity price forecasting (STPF) crucial in formulating their risk management strategies. They need to know future electricity prices as their profitability depends on them. This research project classifies and compares different techniques of electricity price forecasting in the literature and selects artificial neural networks (ANN) as a suitable method for price forecasting. To perform this task, market knowledge should be used to optimize the selection of input data for an electricity price forecasting tool. Then sensitivity analysis is used in this research to aid in the selection of the optimum inputs of the ANN and fuzzy c-mean (FCM) algorithm is used for daily load pattern clustering. Finally, ANN with a modified Levenberg–Marquardt (LM) learning algorithm are implemented for forecasting prices in Pennsylvania–New Jersey–Maryland (PJM) market. The forecasting results were compared with the previous works and showed that the results are reasonable and accurate. |
|---|---|
| AbstractList | Over the past 15 years most electricity supply companies around the world have been restructured from monopoly utilities to deregulated competitive electricity markets. Market participants in the restructured electricity markets find short-term electricity price forecasting (STPF) crucial in formulating their risk management strategies. They need to know future electricity prices as their profitability depends on them. This research project classifies and compares different techniques of electricity price forecasting in the literature and selects artificial neural networks (ANN) as a suitable method for price forecasting. To perform this task, market knowledge should be used to optimize the selection of input data for an electricity price forecasting tool. Then sensitivity analysis is used in this research to aid in the selection of the optimum inputs of the ANN and fuzzy c-mean (FCM) algorithm is used for daily load pattern clustering. Finally, ANN with a modified Levenberg–Marquardt (LM) learning algorithm are implemented for forecasting prices in Pennsylvania–New Jersey–Maryland (PJM) market. The forecasting results were compared with the previous works and showed that the results are reasonable and accurate. |
| Author | Jadid, S. Vahidinasab, V. Kazemi, A. |
| Author_xml | – sequence: 1 givenname: V. surname: Vahidinasab fullname: Vahidinasab, V. email: vahidinasab@iust.ac.ir – sequence: 2 givenname: S. surname: Jadid fullname: Jadid, S. – sequence: 3 givenname: A. surname: Kazemi fullname: Kazemi, A. |
| BackLink | http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=20370334$$DView record in Pascal Francis |
| BookMark | eNp9kD1PwzAURS1UJErhDzBlYUx4ttM4kVhQ-ZQqscDCYjn2M7i0SWU7VP33uBQWhk53uedK95ySUdd3SMgFhYICra4WBa6DLxiAKCgrAOgRGdNa8JxBWY3IGLiocyGa6oSchrAAgKoR0zF5u1XbXH2gMtnaO42Z7T1qFaLr3jPXZR5D9IOOg8fU6Dfos7ANEVchG8Kuo3x01mmnllmHg_-JuOn9Zzgjx1YtA57_5oS83t-9zB7z-fPD0-xmnmvOIebIjGlbUzW6baiuS6E1QC1aa62pW8WEAcM4qxqcslKXqrE0AdN2KqqqVpzzCbnc765V0Gppveq0CzLdWSm_lSxdB87L1Kv3Pe37EDxaqV1U0fVd9MotJQW5cykXcudS7lxKymRymVD2D_1bPwhd7yFM578cehm0w06jcUlxlKZ3h_BvN-6Spg |
| CODEN | EPSRDN |
| CitedBy_id | crossref_primary_10_1016_j_neucom_2013_08_025 crossref_primary_10_1016_j_apenergy_2018_02_069 crossref_primary_10_1016_j_enconman_2010_05_006 crossref_primary_10_1016_j_ijepes_2010_08_008 crossref_primary_10_1007_s11081_012_9200_8 crossref_primary_10_1080_15325008_2013_769034 crossref_primary_10_1016_j_eneco_2017_06_020 crossref_primary_10_1016_j_epsr_2013_04_007 crossref_primary_10_1002_etep_1791 crossref_primary_10_1016_j_asoc_2016_07_011 crossref_primary_10_1016_j_tej_2019_106628 crossref_primary_10_1080_15567249_2011_557685 crossref_primary_10_1016_j_ijepes_2014_08_018 crossref_primary_10_1007_s00521_012_0875_8 crossref_primary_10_1007_s00500_013_1168_6 crossref_primary_10_1016_j_asoc_2009_10_004 crossref_primary_10_3390_math10122012 crossref_primary_10_1108_17506220911005731 crossref_primary_10_1515_snde_2019_0009 crossref_primary_10_1016_j_enconman_2010_02_023 crossref_primary_10_1016_j_rser_2018_02_002 crossref_primary_10_1016_j_epsr_2012_11_007 crossref_primary_10_1080_15325000802599353 crossref_primary_10_1016_j_epsr_2009_11_007 crossref_primary_10_1109_TPWRS_2009_2021207 crossref_primary_10_1080_10962247_2012_755940 crossref_primary_10_3846_16484142_2017_1286521 crossref_primary_10_1016_j_renene_2015_05_024 crossref_primary_10_1080_23311916_2017_1358545 crossref_primary_10_3390_en15197336 crossref_primary_10_1016_j_epsr_2019_106080 crossref_primary_10_1016_j_renene_2013_12_042 crossref_primary_10_1016_j_energy_2011_02_003 crossref_primary_10_1016_j_neucom_2011_02_017 crossref_primary_10_1016_j_enconman_2014_04_012 crossref_primary_10_3390_info8010031 crossref_primary_10_1016_j_ijepes_2019_03_056 crossref_primary_10_1016_j_apenergy_2010_05_012 crossref_primary_10_1016_j_apenergy_2016_03_089 crossref_primary_10_3390_en6115897 crossref_primary_10_3390_en12193665 crossref_primary_10_1007_s40998_020_00347_z crossref_primary_10_1016_j_epsr_2016_08_005 crossref_primary_10_1515_eb_2016_0017 crossref_primary_10_1016_j_epsr_2008_12_001 crossref_primary_10_1007_s12543_011_0089_2 crossref_primary_10_1007_s12667_012_0061_1 crossref_primary_10_1016_j_epsr_2015_03_027 crossref_primary_10_1016_j_epsr_2016_10_067 crossref_primary_10_1016_j_cie_2012_03_016 crossref_primary_10_1016_j_apenergy_2020_115599 crossref_primary_10_1016_j_eneco_2025_108651 crossref_primary_10_1007_s00170_011_3804_6 crossref_primary_10_1155_2014_249208 crossref_primary_10_1016_j_jfds_2016_10_001 crossref_primary_10_1016_j_enpol_2018_08_053 crossref_primary_10_1007_s10661_008_0531_z crossref_primary_10_7232_JKIIE_2013_39_1_030 crossref_primary_10_1002_etep_316 crossref_primary_10_3390_app15010183 crossref_primary_10_1109_ACCESS_2021_3100076 crossref_primary_10_1016_j_apenergy_2008_08_014 crossref_primary_10_1016_j_ijforecast_2014_08_008 crossref_primary_10_1016_j_petrol_2015_07_002 crossref_primary_10_1080_02533839_2013_814993 crossref_primary_10_1016_j_apenergy_2017_04_039 crossref_primary_10_1016_j_rser_2011_08_014 crossref_primary_10_1007_s00450_016_0303_x crossref_primary_10_46904_eea_25_73_2_1108010 crossref_primary_10_1016_j_enconman_2009_12_019 crossref_primary_10_1007_s00521_018_3652_5 crossref_primary_10_1177_1063293X11424512 |
| Cites_doi | 10.1109/TPWRS.2002.804943 10.1109/TPWRS.2002.1007902 10.1049/ip-gtd:20045131 10.1109/TPWRS.2004.840412 10.1109/59.780895 10.1016/j.ijforecast.2004.12.005 10.1049/ip-gtd:20020371 10.1109/TPWRS.2005.846054 10.1109/TPWRS.2002.807062 10.1109/72.329697 10.1109/TPWRS.2004.837759 10.1109/TPWRS.2005.846044 |
| ContentType | Journal Article |
| Copyright | 2007 Elsevier B.V. 2008 INIST-CNRS |
| Copyright_xml | – notice: 2007 Elsevier B.V. – notice: 2008 INIST-CNRS |
| DBID | AAYXX CITATION IQODW |
| DOI | 10.1016/j.epsr.2007.12.001 |
| DatabaseName | CrossRef Pascal-Francis |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Applied Sciences |
| EISSN | 1873-2046 |
| EndPage | 1342 |
| ExternalDocumentID | 20370334 10_1016_j_epsr_2007_12_001 S0378779607002362 |
| GroupedDBID | --K --M -~X .~1 0R~ 1B1 1~. 1~5 29G 4.4 457 4G. 5GY 5VS 7-5 71M 8P~ 9JN AABNK AACTN AAEDT AAEDW AAHCO AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AARJD AAXUO ABFNM ABMAC ABXDB ABYKQ ACAZW ACDAQ ACGFS ACIWK ACNNM ACRLP ADBBV ADEZE ADHUB ADMUD ADTZH AEBSH AECPX AEKER AENEX AFKWA AFTJW AGHFR AGUBO AGYEJ AHHHB AHIDL AHJVU AI. AIEXJ AIKHN AITUG AJBFU AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ ARUGR ASPBG AVWKF AXJTR AZFZN BELTK BJAXD BKOJK BLXMC CS3 DU5 E.L EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 FDB FEDTE FGOYB FIRID FNPLU FYGXN G-2 G-Q GBLVA HVGLF HZ~ IHE J1W JARJE JJJVA K-O KOM LY6 LY7 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 R2- RIG ROL RPZ SAC SDF SDG SES SET SEW SPC SPCBC SSR SST SSW SSZ T5K VH1 WUQ ZMT ~G- 9DU AATTM AAXKI AAYWO AAYXX ABJNI ABWVN ACLOT ACRPL ACVFH ADCNI ADNMO AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP CITATION EFKBS ~HD AFXIZ AGCQF AGRNS BNPGV IQODW SSH |
| ID | FETCH-LOGICAL-c330t-e2ddbbd69cb91c847cc0087bfffd8ba27d0d23269e524c4a9f1ddb5b57668a333 |
| ISICitedReferencesCount | 92 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000256572200003&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0378-7796 |
| IngestDate | Mon Jul 21 09:16:08 EDT 2025 Sat Nov 29 06:43:19 EST 2025 Tue Nov 18 22:25:28 EST 2025 Fri Feb 23 02:27:39 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 8 |
| Keywords | Electricity price forecasting Sensitivity analysis Levenberg–Marquardt algorithm Fuzzy clustering method Artificial neural networks Short term Automatic classification Deregulation Profitability Power system economics Restructuration Daily variation Neural network Risk analysis Implementation Electrical network Levenberg Marquardt algorithm Pricing Power markets Economic aspect Open market Risk management Learning algorithm Comparative study Levenberg-Marquardt algorithm |
| Language | English |
| License | https://www.elsevier.com/tdm/userlicense/1.0 CC BY 4.0 |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c330t-e2ddbbd69cb91c847cc0087bfffd8ba27d0d23269e524c4a9f1ddb5b57668a333 |
| PageCount | 11 |
| ParticipantIDs | pascalfrancis_primary_20370334 crossref_citationtrail_10_1016_j_epsr_2007_12_001 crossref_primary_10_1016_j_epsr_2007_12_001 elsevier_sciencedirect_doi_10_1016_j_epsr_2007_12_001 |
| PublicationCentury | 2000 |
| PublicationDate | 2008-08-01 |
| PublicationDateYYYYMMDD | 2008-08-01 |
| PublicationDate_xml | – month: 08 year: 2008 text: 2008-08-01 day: 01 |
| PublicationDecade | 2000 |
| PublicationPlace | Amsterdam |
| PublicationPlace_xml | – name: Amsterdam |
| PublicationTitle | Electric power systems research |
| PublicationYear | 2008 |
| Publisher | Elsevier B.V Elsevier |
| Publisher_xml | – name: Elsevier B.V – name: Elsevier |
| References | Conejo, Contreras, Plazas, Espinola (bib8) 2005; 21 Hong, Hsiao (bib11) 2002; 149 Gonzalez, Roque, Garcia (bib15) 2005; 20 Szkuta, Sanavria, Dillon (bib10) 1999; 14 Contreras, Espínola, Nogales, Conejo (bib2) 2003; 18 Guo, Luh (bib14) 2004; 19 Shahidehpour, Yamin, Li (bib1) 2002 The MathWorks, MATLAB. Available online at the following website Zhang, Luh, Kasiviswanathan (bib13) 2003; 18 Haykin (bib17) 1999 Wang (bib16) 1997 . Nogales, Contreras, Conejo, Espínola (bib5) 2002; 17 David, Wen (bib3) 2000 Garcia, Contreras, van Akkeren, Garcia (bib9) 2005; 20 Zhou, Yan, Ni, Li, Nie (bib6) 2006; 153 Hong, Hsiao (bib12) 2001 PJM, Pennsylvania–New Jersey–Maryland market. Available online at the following website Khosravi, Barghinia, Ansarimehr (bib19) 2006 Conejo, Plazas, Espinola, Molina (bib7) 2005; 20 Hagan, Menhaj (bib18) 1994; 5 Zhang (10.1016/j.epsr.2007.12.001_bib13) 2003; 18 10.1016/j.epsr.2007.12.001_bib4 Shahidehpour (10.1016/j.epsr.2007.12.001_bib1) 2002 David (10.1016/j.epsr.2007.12.001_bib3) 2000 Conejo (10.1016/j.epsr.2007.12.001_bib7) 2005; 20 Nogales (10.1016/j.epsr.2007.12.001_bib5) 2002; 17 Szkuta (10.1016/j.epsr.2007.12.001_bib10) 1999; 14 Hong (10.1016/j.epsr.2007.12.001_bib12) 2001 Gonzalez (10.1016/j.epsr.2007.12.001_bib15) 2005; 20 Khosravi (10.1016/j.epsr.2007.12.001_bib19) 2006 10.1016/j.epsr.2007.12.001_bib20 Hong (10.1016/j.epsr.2007.12.001_bib11) 2002; 149 Guo (10.1016/j.epsr.2007.12.001_bib14) 2004; 19 Haykin (10.1016/j.epsr.2007.12.001_bib17) 1999 Garcia (10.1016/j.epsr.2007.12.001_bib9) 2005; 20 Wang (10.1016/j.epsr.2007.12.001_bib16) 1997 Conejo (10.1016/j.epsr.2007.12.001_bib8) 2005; 21 Hagan (10.1016/j.epsr.2007.12.001_bib18) 1994; 5 Contreras (10.1016/j.epsr.2007.12.001_bib2) 2003; 18 Zhou (10.1016/j.epsr.2007.12.001_bib6) 2006; 153 |
| References_xml | – start-page: 1782 year: 2006 end-page: 1788 ident: bib19 article-title: New momentum adjustment technique for Levenberg–Marquardt neural network training used in short term load forecasting publication-title: Proceedings of 21st International Power System Conference (PSC’06) – volume: 20 start-page: 13 year: 2005 end-page: 24 ident: bib15 article-title: Modeling and forecasting electricity prices with input/output hidden Markov models publication-title: IEEE Trans. Power Syst. – volume: 18 start-page: 1014 year: 2003 end-page: 1020 ident: bib2 article-title: ARIMA models to predict next-day electricity prices publication-title: IEEE Trans. Power Syst. – volume: 18 start-page: 99 year: 2003 end-page: 105 ident: bib13 article-title: Energy clearing price prediction and confidence interval estimation with cascaded neural networks publication-title: IEEE Trans. Power Syst. – volume: 153 start-page: 187 year: 2006 end-page: 195 ident: bib6 article-title: Electricity price forecasting with confidence-interval estimation through an extended ARIMA approach publication-title: IEE Proc.: Gener., Transm. Distrib. – volume: 19 start-page: 1867 year: 2004 end-page: 1876 ident: bib14 article-title: Improving market clearing price prediction by using a committee machine of neural networks publication-title: IEEE Trans. Power Syst. – start-page: 2168 year: 2000 end-page: 2173 ident: bib3 article-title: Strategic bidding in competitive electricity markets: a literature survey publication-title: Proceedings of IEEE Power Engineering Society 2000 Summer Meeting, vol. 4 – reference: PJM, Pennsylvania–New Jersey–Maryland market. Available online at the following website: – year: 2002 ident: bib1 article-title: Market Operations in Electric Power Systems, Forecasting, Scheduling and Risk Management – year: 1997 ident: bib16 article-title: A Course in Fuzzy Systems and Control – volume: 149 start-page: 621 year: 2002 end-page: 626 ident: bib11 article-title: Locational marginal price forecasting in deregulated electric markets using a recurrent neural network publication-title: IEE Proc.: Gener., Transm. Distrib. – year: 2001 ident: bib12 article-title: Locational marginal price forecasting in deregulated electric markets using artificial intelligence publication-title: Proceedings of IEEE PES Winter Meeting – volume: 5 start-page: 989 year: 1994 end-page: 993 ident: bib18 article-title: Training feedforward networks with the Marquardt algorithm publication-title: IEEE Trans. Neural Networks – reference: The MathWorks, MATLAB. Available online at the following website: – volume: 21 start-page: 435 year: 2005 end-page: 462 ident: bib8 article-title: Forecasting electricity prices for a day-ahead pool based energy market publication-title: Int. J. Forecasting – volume: 14 start-page: 851 year: 1999 end-page: 857 ident: bib10 article-title: Electricity price short term forecasting using artificial neural networks publication-title: IEEE Trans. Power Syst. – volume: 20 start-page: 1035 year: 2005 end-page: 1042 ident: bib7 article-title: Day-ahead electricity price forecasting using the wavelet transform and ARIMA models publication-title: IEEE Trans. Power Syst. – volume: 20 start-page: 867 year: 2005 end-page: 874 ident: bib9 article-title: A GARCH forecasting model to predict day-ahead electricity prices publication-title: IEEE Trans. Power Syst. – volume: 17 start-page: 342 year: 2002 end-page: 348 ident: bib5 article-title: Forecasting next-day electricity prices by time series models publication-title: IEEE Trans. Power Syst. – reference: . – year: 1999 ident: bib17 article-title: Neural Networks (A Comprehensive Foundation) – volume: 18 start-page: 1014 issue: 3 year: 2003 ident: 10.1016/j.epsr.2007.12.001_bib2 article-title: ARIMA models to predict next-day electricity prices publication-title: IEEE Trans. Power Syst. doi: 10.1109/TPWRS.2002.804943 – volume: 17 start-page: 342 issue: 2 year: 2002 ident: 10.1016/j.epsr.2007.12.001_bib5 article-title: Forecasting next-day electricity prices by time series models publication-title: IEEE Trans. Power Syst. doi: 10.1109/TPWRS.2002.1007902 – volume: 153 start-page: 187 issue: 2 year: 2006 ident: 10.1016/j.epsr.2007.12.001_bib6 article-title: Electricity price forecasting with confidence-interval estimation through an extended ARIMA approach publication-title: IEE Proc.: Gener., Transm. Distrib. doi: 10.1049/ip-gtd:20045131 – volume: 20 start-page: 13 issue: 1 year: 2005 ident: 10.1016/j.epsr.2007.12.001_bib15 article-title: Modeling and forecasting electricity prices with input/output hidden Markov models publication-title: IEEE Trans. Power Syst. doi: 10.1109/TPWRS.2004.840412 – volume: 14 start-page: 851 issue: 3 year: 1999 ident: 10.1016/j.epsr.2007.12.001_bib10 article-title: Electricity price short term forecasting using artificial neural networks publication-title: IEEE Trans. Power Syst. doi: 10.1109/59.780895 – year: 1997 ident: 10.1016/j.epsr.2007.12.001_bib16 – volume: 21 start-page: 435 year: 2005 ident: 10.1016/j.epsr.2007.12.001_bib8 article-title: Forecasting electricity prices for a day-ahead pool based energy market publication-title: Int. J. Forecasting doi: 10.1016/j.ijforecast.2004.12.005 – start-page: 2168 year: 2000 ident: 10.1016/j.epsr.2007.12.001_bib3 article-title: Strategic bidding in competitive electricity markets: a literature survey – start-page: 1782 year: 2006 ident: 10.1016/j.epsr.2007.12.001_bib19 article-title: New momentum adjustment technique for Levenberg–Marquardt neural network training used in short term load forecasting – volume: 149 start-page: 621 year: 2002 ident: 10.1016/j.epsr.2007.12.001_bib11 article-title: Locational marginal price forecasting in deregulated electric markets using a recurrent neural network publication-title: IEE Proc.: Gener., Transm. Distrib. doi: 10.1049/ip-gtd:20020371 – ident: 10.1016/j.epsr.2007.12.001_bib20 – year: 2002 ident: 10.1016/j.epsr.2007.12.001_bib1 – year: 2001 ident: 10.1016/j.epsr.2007.12.001_bib12 article-title: Locational marginal price forecasting in deregulated electric markets using artificial intelligence – volume: 20 start-page: 1035 issue: 2 year: 2005 ident: 10.1016/j.epsr.2007.12.001_bib7 article-title: Day-ahead electricity price forecasting using the wavelet transform and ARIMA models publication-title: IEEE Trans. Power Syst. doi: 10.1109/TPWRS.2005.846054 – volume: 18 start-page: 99 issue: 1 year: 2003 ident: 10.1016/j.epsr.2007.12.001_bib13 article-title: Energy clearing price prediction and confidence interval estimation with cascaded neural networks publication-title: IEEE Trans. Power Syst. doi: 10.1109/TPWRS.2002.807062 – ident: 10.1016/j.epsr.2007.12.001_bib4 – volume: 5 start-page: 989 issue: 6 year: 1994 ident: 10.1016/j.epsr.2007.12.001_bib18 article-title: Training feedforward networks with the Marquardt algorithm publication-title: IEEE Trans. Neural Networks doi: 10.1109/72.329697 – volume: 19 start-page: 1867 issue: 4 year: 2004 ident: 10.1016/j.epsr.2007.12.001_bib14 article-title: Improving market clearing price prediction by using a committee machine of neural networks publication-title: IEEE Trans. Power Syst. doi: 10.1109/TPWRS.2004.837759 – volume: 20 start-page: 867 issue: 2 year: 2005 ident: 10.1016/j.epsr.2007.12.001_bib9 article-title: A GARCH forecasting model to predict day-ahead electricity prices publication-title: IEEE Trans. Power Syst. doi: 10.1109/TPWRS.2005.846044 – year: 1999 ident: 10.1016/j.epsr.2007.12.001_bib17 |
| SSID | ssj0006975 |
| Score | 2.1992886 |
| Snippet | Over the past 15 years most electricity supply companies around the world have been restructured from monopoly utilities to deregulated competitive electricity... |
| SourceID | pascalfrancis crossref elsevier |
| SourceType | Index Database Enrichment Source Publisher |
| StartPage | 1332 |
| SubjectTerms | Applied sciences Artificial neural networks Electrical engineering. Electrical power engineering Electrical power engineering Electricity price forecasting Exact sciences and technology Fuzzy clustering method Levenberg–Marquardt algorithm Miscellaneous Operation. Load control. Reliability Power networks and lines Sensitivity analysis |
| Title | Day-ahead price forecasting in restructured power systems using artificial neural networks |
| URI | https://dx.doi.org/10.1016/j.epsr.2007.12.001 |
| Volume | 78 |
| WOSCitedRecordID | wos000256572200003&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: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1873-2046 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0006975 issn: 0378-7796 databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1La9wwEBZL0kNLKX3S9BF06C04eCXbko5Lm9IXodA0LL0YSZaoQ-Iu8SYkOfaXd_Sw4k1oaAu9eBdhy1rP59HM7Mw3CL2yueFGKpkZVpissCLPRFHKzHGhsEqBMyZ915JPbHeXz-fi82Tyc6iFOT1kXcfPzsTiv4oaxkDYrnT2L8SdJoUB-A5ChyOIHY5_JPg38jyToGJd_T9oAZdHaLTsl6l4JVDGnrjE84XrkRbZnPutEx83cFNGWglHduk_fKp4vxLF9-1zWn1lisgdlGLM-_J7C7uj7KX_32d_O2XsyKYNIek09FFemCOfXDDbXglG8JQKFyNk16pkQmUWeKqMiUh5HRQtZxTEFsOPURMzPkIcH6lVcKTJaIue0sDIdU39h0jEAZzWHwd6ShfqjQtcpdX-4lblFgU6z7Howza-TlgpQLmvz97vzD-k_bwSnq45_YpYehWyBK_e6Xfmzd2F7OGls6FbysiE2buP7kXfA88CZh6giekeojsjRspH6FtCD_bowSP04LbDY_RgL3ocRY89evAlenBADx7Q8xh9fbuz9_pdFttvZJrSfJkZ0jRKNZXQSkw1WDFaOwJDZa1tuJKENXkD9nglTEkKXUhhp3BBqcCDrbiklD5Ba92PzjxFWJHGWMYayYktlLbcMA2uhyHUVlXOiw00HZ5arSM3vWuRclgPSYgHtXvSrmkqq6fEZWJuoK10zSIws9x4djkIo462ZbAZa8DOjddtrkgu3YoAHnJKi2f_OPFzdPvyBXqB1kB45iW6pU-XbX-8GTH4C05BrTU |
| linkProvider | Elsevier |
| 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=Day-ahead+price+forecasting+in+restructured+power+systems+using+artificial+neural+networks&rft.jtitle=Electric+power+systems+research&rft.au=Vahidinasab%2C+V.&rft.au=Jadid%2C+S.&rft.au=Kazemi%2C+A.&rft.date=2008-08-01&rft.pub=Elsevier+B.V&rft.issn=0378-7796&rft.eissn=1873-2046&rft.volume=78&rft.issue=8&rft.spage=1332&rft.epage=1342&rft_id=info:doi/10.1016%2Fj.epsr.2007.12.001&rft.externalDocID=S0378779607002362 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0378-7796&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0378-7796&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0378-7796&client=summon |