Enhancing Electricity Load Forecasting with Machine Learning and Deep Learning
The electricity load forecasting handles the process of determining how much electricity will be available at a given time while maintaining the balance and stability of the power grid. The accuracy of electricity load forecasting plays an important role in ensuring safe operation and improving the...
Uloženo v:
| Vydáno v: | Technologies (Basel) Ročník 13; číslo 2; s. 59 |
|---|---|
| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Basel
MDPI AG
01.02.2025
|
| Témata: | |
| ISSN: | 2227-7080, 2227-7080 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | The electricity load forecasting handles the process of determining how much electricity will be available at a given time while maintaining the balance and stability of the power grid. The accuracy of electricity load forecasting plays an important role in ensuring safe operation and improving the reliability of power systems and is a key component in the operational planning and efficient market. For many years, a conventional method has been used by using historical data as input parameters. With swift progress and improvement in technology, which shows more potential due to its accuracy, different methods can be applied depending on the identified model. To enhance the forecast of load, this paper introduces and proposes a framework developed on graph database technology to archive large amounts of data, which collects measured data from electrical substations in Pristina, Kosovo. The data includes electrical and weather parameters collected over a four-year timeframe. The proposed framework is designed to handle short-term load forecasting. Machine learning Linear Regression and deep learning Long Short-Term Memory algorithms are applied to multiple datasets and mean absolute error and root mean square error are calculated. The results show the promising performance and effectiveness of the proposed model, with high accuracy in load forecasting. |
|---|---|
| AbstractList | The electricity load forecasting handles the process of determining how much electricity will be available at a given time while maintaining the balance and stability of the power grid. The accuracy of electricity load forecasting plays an important role in ensuring safe operation and improving the reliability of power systems and is a key component in the operational planning and efficient market. For many years, a conventional method has been used by using historical data as input parameters. With swift progress and improvement in technology, which shows more potential due to its accuracy, different methods can be applied depending on the identified model. To enhance the forecast of load, this paper introduces and proposes a framework developed on graph database technology to archive large amounts of data, which collects measured data from electrical substations in Pristina, Kosovo. The data includes electrical and weather parameters collected over a four-year timeframe. The proposed framework is designed to handle short-term load forecasting. Machine learning Linear Regression and deep learning Long Short-Term Memory algorithms are applied to multiple datasets and mean absolute error and root mean square error are calculated. The results show the promising performance and effectiveness of the proposed model, with high accuracy in load forecasting. |
| Audience | Academic |
| Author | Minkovska, Daniela Hinov, Nikolay Perçuku, Arbër |
| Author_xml | – sequence: 1 givenname: Arbër surname: Perçuku fullname: Perçuku, Arbër – sequence: 2 givenname: Daniela orcidid: 0000-0003-3095-6954 surname: Minkovska fullname: Minkovska, Daniela – sequence: 3 givenname: Nikolay orcidid: 0000-0002-0308-4858 surname: Hinov fullname: Hinov, Nikolay |
| BookMark | eNp9kU9vVCEUxV9MTay138DFS1xP5T8Pd02dapNRN7omF7jMMHmFkUdj-u1lHDXGGLkLyOH-Tric58NZLhmH4SUlV5wb8rqh3-Uyl23ChXLCCJHmyXDOGNMrTSZy9sf52XC5LHvSl6F8UvJ8-LjOO8g-5e24ntG3mnxqj-OmQBhvS0UPSztefkttN34Av0sZxw1CzUcVchjfIh5-Ky-GpxHmBS9_7hfDl9v155v3q82nd3c315uVF5y1FWOeiADRcTHpQI0KjGupneOaEEcBjQFEpUEoH6hkkgonlZYCIiglkV8MdyffUGBvDzXdQ320BZL9IZS6tVBb8jNaLwVxcWLecSVcYG7yqBSlkUYio4vd69XJ61DL1wdcmt2Xh5r78y2nmjJuJjX1rqtT1xa6acqxtAq-V8D75HskMXX9emLGGDFx0YE3J8DXsiwVo-0_Cy2V3ME0W0rsMT_7r_w6LP6Cf834X-w7AhWkRg |
| CitedBy_id | crossref_primary_10_3390_computation13030075 crossref_primary_10_3390_electronics14142820 crossref_primary_10_3390_en18051144 crossref_primary_10_3390_technologies13080321 |
| Cites_doi | 10.3390/en16031480 10.1057/palgrave.jors.2601589 10.1016/j.apenergy.2021.117594 10.3390/forecast3010006 10.3390/en13020391 10.1016/j.ijepes.2017.10.032 10.1109/ACCESS.2021.3060290 10.1109/TPWRS.2004.835679 10.3390/technologies11030070 10.3390/electronics13091722 10.1109/TSTE.2017.2762435 10.3390/en12224349 10.1016/j.apenergy.2020.116177 10.3390/en15062158 10.1016/j.epsr.2015.09.001 10.1109/ACCESS.2021.3060654 10.1016/j.ejor.2009.01.062 10.1109/UPEC.2007.4469121 10.1186/s42162-022-00213-8 10.1162/neco.1997.9.8.1735 10.1186/s42162-021-00172-6 10.3390/su12166364 10.1007/978-3-319-55252-1 10.1016/j.egypro.2019.01.952 10.3390/en14102737 10.1007/s42979-021-00592-x 10.1186/s43067-020-00021-8 10.3390/en14134046 10.3390/su14073984 |
| ContentType | Journal Article |
| Copyright | COPYRIGHT 2025 MDPI AG 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: COPYRIGHT 2025 MDPI AG – notice: 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| DBID | AAYXX CITATION 8FD 8FE 8FG ABJCF ABUWG AFKRA ARAPS AZQEC BENPR BGLVJ CCPQU COVID D1I DWQXO F28 FR3 GNUQQ HCIFZ JQ2 K7- KB. L6V M7S P5Z P62 PDBOC PHGZM PHGZT PIMPY PKEHL PQEST PQGLB PQQKQ PQUKI PRINS PTHSS DOA |
| DOI | 10.3390/technologies13020059 |
| DatabaseName | CrossRef Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection Materials Science & Engineering Collection ProQuest Central (Alumni) ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Essentials AUTh Library subscriptions: ProQuest Central Technology collection ProQuest One Community College Coronavirus Research Database ProQuest Materials Science Collection ProQuest Central ANTE: Abstracts in New Technology & Engineering Engineering Research Database ProQuest Central Student SciTech Premium Collection ProQuest Computer Science Collection Computer Science Database Materials Science Database ProQuest Engineering Collection Engineering Database Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection Materials Science Collection ProQuest Central Premium ProQuest One Academic Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China Engineering collection DOAJ Open Access Full Text |
| DatabaseTitle | CrossRef Publicly Available Content Database Computer Science Database ProQuest Central Student Technology Collection Technology Research Database ProQuest One Academic Middle East (New) ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection Materials Science Collection ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Central China ProQuest Central ProQuest One Applied & Life Sciences ProQuest Engineering Collection ProQuest Central Korea Materials Science Database ProQuest Central (New) Engineering Collection ANTE: Abstracts in New Technology & Engineering ProQuest Materials Science Collection Advanced Technologies & Aerospace Collection Engineering Database ProQuest One Academic Eastern Edition Coronavirus Research Database ProQuest Technology Collection ProQuest SciTech Collection Advanced Technologies & Aerospace Database ProQuest One Academic UKI Edition Materials Science & Engineering Collection Engineering Research Database ProQuest One Academic ProQuest One Academic (New) |
| DatabaseTitleList | Publicly Available Content Database CrossRef |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: KB. name: Materials Science Database url: http://search.proquest.com/materialsscijournals sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 2227-7080 |
| ExternalDocumentID | oai_doaj_org_article_c540bf82cb364bd2b8ce6611f1f05fbf A829994834 10_3390_technologies13020059 |
| GeographicLocations | Kosovo |
| GeographicLocations_xml | – name: Kosovo |
| GroupedDBID | 5VS 8FE 8FG AADQD AAFWJ AAYXX ABJCF ADBBV ADMLS AFFHD AFKRA AFPKN AFZYC ALMA_UNASSIGNED_HOLDINGS ARAPS BCNDV BENPR BGLVJ CCPQU CITATION CZ9 D1I GROUPED_DOAJ HCIFZ IAO ITC K6V K7- KB. KC. KQ8 L6V M7S MODMG M~E OK1 P62 PDBOC PHGZM PHGZT PIMPY PQGLB PROAC PTHSS 8FD ABUWG AZQEC COVID DWQXO F28 FR3 GNUQQ JQ2 PKEHL PQEST PQQKQ PQUKI PRINS |
| ID | FETCH-LOGICAL-c432t-22c04dafb3487d196d23757bb3700b1ae99aee67a46cd152514b56754afa665e3 |
| IEDL.DBID | DOA |
| ISICitedReferencesCount | 5 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001431884900001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2227-7080 |
| IngestDate | Fri Oct 03 12:42:51 EDT 2025 Fri Jul 25 12:14:27 EDT 2025 Tue Nov 04 18:11:15 EST 2025 Sat Nov 29 07:16:44 EST 2025 Tue Nov 18 21:44:37 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 2 |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c432t-22c04dafb3487d196d23757bb3700b1ae99aee67a46cd152514b56754afa665e3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0003-3095-6954 0000-0002-0308-4858 |
| OpenAccessLink | https://doaj.org/article/c540bf82cb364bd2b8ce6611f1f05fbf |
| PQID | 3171239868 |
| PQPubID | 2032323 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_c540bf82cb364bd2b8ce6611f1f05fbf proquest_journals_3171239868 gale_infotracacademiconefile_A829994834 crossref_citationtrail_10_3390_technologies13020059 crossref_primary_10_3390_technologies13020059 |
| PublicationCentury | 2000 |
| PublicationDate | 2025-02-01 |
| PublicationDateYYYYMMDD | 2025-02-01 |
| PublicationDate_xml | – month: 02 year: 2025 text: 2025-02-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | Basel |
| PublicationPlace_xml | – name: Basel |
| PublicationTitle | Technologies (Basel) |
| PublicationYear | 2025 |
| Publisher | MDPI AG |
| Publisher_xml | – name: MDPI AG |
| References | Farsi (ref_11) 2021; 9 Nespoli (ref_14) 2021; 3 Dudek (ref_8) 2016; 130 ref_13 ref_12 Chen (ref_4) 2001; Volume 2 ref_10 Chen (ref_9) 2004; 19 Sevlian (ref_28) 2018; 98 ref_17 ref_16 Kang (ref_1) 2004; 28 Sarker (ref_27) 2021; 2 Muzaffar (ref_21) 2019; 158 Lenders (ref_31) 2021; 4 Gigoni (ref_18) 2017; 9 Rafi (ref_15) 2021; 9 ref_23 ref_22 Fekri (ref_20) 2021; 282 ref_3 ref_2 Vinasco (ref_19) 2021; 303 ref_29 ref_26 Hochreiter (ref_24) 1997; 9 Nti (ref_32) 2020; 7 Hahn (ref_34) 2009; 199 Beichter (ref_33) 2022; 5 ref_5 ref_7 Hyndman (ref_25) 2006; 4 ref_6 Taylor (ref_30) 2003; 54 |
| References_xml | – ident: ref_3 doi: 10.3390/en16031480 – volume: 54 start-page: 799 year: 2003 ident: ref_30 article-title: Short-Term Electricity Demand Forecasting Using Double Seasonal Exponential Smoothing publication-title: J. Oper. Res. Soc. doi: 10.1057/palgrave.jors.2601589 – volume: 303 start-page: 117594 year: 2021 ident: ref_19 article-title: Probabilistic load forecasting considering temporal correlation: Online models for the prediction of households’ electrical load publication-title: Appl. Energy doi: 10.1016/j.apenergy.2021.117594 – volume: 3 start-page: 91 year: 2021 ident: ref_14 article-title: Electrical Load Forecast by Means of LSTM: The Impact of Data Quality publication-title: Forecasting doi: 10.3390/forecast3010006 – ident: ref_13 doi: 10.3390/en13020391 – ident: ref_26 – volume: 98 start-page: 350 year: 2018 ident: ref_28 article-title: A Scaling Law for Short Term Load Forecasting on Varying Levels of Aggregation publication-title: Int. J. Electr. Power Energy Syst. doi: 10.1016/j.ijepes.2017.10.032 – volume: Volume 2 start-page: 411 year: 2001 ident: ref_4 article-title: ANN-based short-term load forecasting in electricity markets publication-title: Proceedings of the Power Engineering Society Winter Meeting – volume: 9 start-page: 31191 year: 2021 ident: ref_11 article-title: On Short-Term Load Forecasting Using Machine Learning Techniques and a Novel Parallel Deep LSTM-CNN Approach publication-title: IEEE Access doi: 10.1109/ACCESS.2021.3060290 – volume: 19 start-page: 1821 year: 2004 ident: ref_9 article-title: Load forecasting using support vector machines: A study on EUNITE competition 2001 publication-title: IEEE Trans. Power Syst. doi: 10.1109/TPWRS.2004.835679 – volume: 4 start-page: 43 year: 2006 ident: ref_25 article-title: Another look at forecast accuracy metrics for intermittent demand publication-title: Foresight Int. J. Appl. Forecast. – ident: ref_12 doi: 10.3390/technologies11030070 – ident: ref_29 doi: 10.3390/electronics13091722 – volume: 9 start-page: 831 year: 2017 ident: ref_18 article-title: Day-Ahead Hourly Forecasting of Power Generation from Photovoltaic Plants publication-title: IEEE Trans. Sustain. Energy doi: 10.1109/TSTE.2017.2762435 – ident: ref_2 doi: 10.3390/en12224349 – volume: 282 start-page: 116177 year: 2021 ident: ref_20 article-title: Deep learning for load forecasting with smart meter data: Online Adaptive Recurrent Neural Network publication-title: Appl. Energy doi: 10.1016/j.apenergy.2020.116177 – ident: ref_17 doi: 10.3390/en15062158 – volume: 28 start-page: 1 year: 2004 ident: ref_1 article-title: Review of power system load forecasting and its development publication-title: Autom. Electr. Power Syst. – volume: 130 start-page: 139 year: 2016 ident: ref_8 article-title: Pattern-based local linear regression models for short-term load forecasting publication-title: Electr. Power Syst. Res. doi: 10.1016/j.epsr.2015.09.001 – volume: 9 start-page: 32436 year: 2021 ident: ref_15 article-title: A Short-Term Load Forecasting Method Using Integrated CNN and LSTM Network publication-title: IEEE Access doi: 10.1109/ACCESS.2021.3060654 – volume: 199 start-page: 902 year: 2009 ident: ref_34 article-title: Electric load forecasting methods: Tools for decision making publication-title: Eur. J. Oper. Res. doi: 10.1016/j.ejor.2009.01.062 – ident: ref_10 doi: 10.1109/UPEC.2007.4469121 – volume: 5 start-page: 19 year: 2022 ident: ref_33 article-title: Net load forecasting using different aggregation levels publication-title: Energy Inform. doi: 10.1186/s42162-022-00213-8 – volume: 9 start-page: 1735 year: 1997 ident: ref_24 article-title: Long short-term memory publication-title: Neural Comput. doi: 10.1162/neco.1997.9.8.1735 – volume: 4 start-page: 13 year: 2021 ident: ref_31 article-title: Comparison of short-term electrical load forecasting methods for different building types publication-title: Energy Inform. doi: 10.1186/s42162-021-00172-6 – ident: ref_5 doi: 10.3390/su12166364 – ident: ref_23 doi: 10.1007/978-3-319-55252-1 – volume: 158 start-page: 2922 year: 2019 ident: ref_21 article-title: Short-Term Load Forecast Using LSTM Networks publication-title: Energy Procedia doi: 10.1016/j.egypro.2019.01.952 – ident: ref_16 doi: 10.3390/en14102737 – volume: 2 start-page: 160 year: 2021 ident: ref_27 article-title: Machine Learning: Algorithms, Real-World Applications and Research Directions publication-title: SN Comput. Sci. doi: 10.1007/s42979-021-00592-x – volume: 7 start-page: 13 year: 2020 ident: ref_32 article-title: Electricity load forecasting: A systematic review publication-title: J. Electr. Syst. Inf. Technol. doi: 10.1186/s43067-020-00021-8 – ident: ref_6 doi: 10.3390/en14134046 – ident: ref_22 – ident: ref_7 doi: 10.3390/su14073984 |
| SSID | ssj0000913865 |
| Score | 2.314958 |
| Snippet | The electricity load forecasting handles the process of determining how much electricity will be available at a given time while maintaining the balance and... |
| SourceID | doaj proquest gale crossref |
| SourceType | Open Website Aggregation Database Enrichment Source Index Database |
| StartPage | 59 |
| SubjectTerms | Accuracy Algorithms Alternative energy sources Artificial intelligence Case studies Datasets Deep learning Electric power systems Electrical loads Electricity electricity load Forecasting Forecasting techniques Handles Kosovo linear regression algorithm long short-term memory algorithm Machine learning Methods Neural networks Parameters Prediction theory Regression analysis short-term forecasting Substations System reliability |
| SummonAdditionalLinks | – databaseName: Engineering Database dbid: M7S link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LbxMxEB6VwgEOvCsCBfmA1NOqtnfX6z2hUlJxaCMkqNSb5WeoVG3SJO3vZ8ZxQg9QDly9Xsm7M-N5ePx9AB8DhtQ2CVc5jAaqpsOc1anQVFLz2AXhuQoZMv-0m0z0xUX_rRTclqWtcrMn5o06zDzVyA_RzwnCqlP60_y6ItYoOl0tFBoP4CGhJIjcuvd9W2MhzEut2vWNuRqz-8PVpl6NaSgd2fEMUnrHI2Xg_r9tz9nnnDz739U-h6cl2mRHa_V4ATtxeAlP7mAQvoLJePhJmBvDlI0zJc6lx8Ccnc5sYMTb6e2SOqMZFWzZWW69jKygsk6ZHQL7EuN8O_Iazk_GP46_VoVkofJNLVeVlJ43wSZXY-oS0B6DrLu2c67uOHfCxr63MarONsoHIksSjWsxy2hsskq1sd6D3WE2xDfAuHRChN4GibNT0j3KW3NUEZf6kFI7gnrzo40vCOREhHFlMBMh8Zg_iWcE1fat-RqB4x_zP5MMt3MJPzsPzBZTU8zReAxUXdLSu1o1LkinfcRIRSSReJtcGsEBaYAhK8clelsuK-CHEl6WOdLoxnsqxI5gf6MBppj_0vwW_9v7H7-Dx5IIhXMb-D7srhY38T088rery-XiQ9bmX5zBANc priority: 102 providerName: ProQuest |
| Title | Enhancing Electricity Load Forecasting with Machine Learning and Deep Learning |
| URI | https://www.proquest.com/docview/3171239868 https://doaj.org/article/c540bf82cb364bd2b8ce6611f1f05fbf |
| Volume | 13 |
| WOSCitedRecordID | wos001431884900001&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: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2227-7080 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913865 issn: 2227-7080 databaseCode: DOA dateStart: 20130101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2227-7080 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913865 issn: 2227-7080 databaseCode: M~E dateStart: 20130101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: AAdvanced Technologies & Aerospace Database (subscription) customDbUrl: eissn: 2227-7080 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913865 issn: 2227-7080 databaseCode: P5Z dateStart: 20130301 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: Computer Science Database customDbUrl: eissn: 2227-7080 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913865 issn: 2227-7080 databaseCode: K7- dateStart: 20130301 isFulltext: true titleUrlDefault: http://search.proquest.com/compscijour providerName: ProQuest – providerCode: PRVPQU databaseName: Engineering Database (subscription) customDbUrl: eissn: 2227-7080 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913865 issn: 2227-7080 databaseCode: M7S dateStart: 20130301 isFulltext: true titleUrlDefault: http://search.proquest.com providerName: ProQuest – providerCode: PRVPQU databaseName: Materials Science Database customDbUrl: eissn: 2227-7080 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913865 issn: 2227-7080 databaseCode: KB. dateStart: 20130301 isFulltext: true titleUrlDefault: http://search.proquest.com/materialsscijournals providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2227-7080 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913865 issn: 2227-7080 databaseCode: BENPR dateStart: 20130301 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 2227-7080 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913865 issn: 2227-7080 databaseCode: PIMPY dateStart: 20130301 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lb9QwEB6hwgEOiKdYKCsfkDiF2s7D8bELqUC0q4iHVLhYfraVUFp1F478dmac7GoPoF64-GBNJGdm7JlxJt8H8CpgSm2TcIXDbKCoFNasrglVIVseVRCeNyFD5h-r5bI9PdX9DtUX9YSN8MCj4g48phQutdK7sqlckK71EWOKSCLxOrlEpy9XeqeYymewFkRmOf4rV2Jdf7De3FRjAUof63iGJ92JRRmy_18Hc442Rw_g_pQmssNxeQ_hVhwewb0d8MDHsOyGcwLLGM5Yl7lsLjxm1Oz40gZGhJverqilmdFNKzvJPZORTXCqZ8wOgb2L8Wo78wS-HnVf3r4vJnaEwlelXBdSel4Fm1yJNUfAjRRkqWrlXKk4d8JGrW2MjbJV4wOxHInK1VgeVDbZpqlj-RT2hsshPgPGpRMiaBskSqfUajRUy9G2LumQUj2DcqMn4yfocGKw-GGwhCDtmr9pdwbF9qmrETrjBvkFmWArS8DXeQLdwUzuYG5yhxm8JgMa2p64RG-nvwzwRQnoyhy2GH813aDOYH9jYzPt25XBbEoQImLTPv8fq3kBdyXxBecu733YW1__jC_hjv-1vlhdz-H2olv2n-bZdXH8qAoaF2_m1IH6mcbfHY59_R1l-w8n_bc_xsv75g |
| linkProvider | Directory of Open Access Journals |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwEB6VggQceBaxUMAHEKeojp2Hc0Co0K1a7XbFoUi9GT-XSii77C4g_hS_kRlvsvTA49QD18SJ4vjzeGY8_j6A5x5dahNzm1n0BrKixpjVVr7IhOKh9rnjlU-U-eN6MlFnZ827LfjRn4WhssreJiZD7WeOcuR7uM7lxFVXqdfzzxmpRtHuai-hsYbFKHz_hiHb8tXxAY7vCyEOh6dvj7JOVSBzhRSrTAjHC2-ileirewSgF7Iua2tlzbnNTWgaE0JVm6JyntSB8sKW6FYXJpqqKoPE916Bq4VUNc2rUZ1tcjrEsamqcn1CT8qG7636_DiGvbRFyBMp6oUVMAkF_Gk5SGvc4e3_7e_cgVudN8321_C_C1uhvQc3L3As3ofJsP1InCLtlA2T5M-5w8CDjWfGM9IldWZJld-MEtLsJJWWBtaxzk6ZaT07CGG-ubID7y-lQw9gu5214SEwLmye-8Z4ga1jVA3iWXGcAjY2PsZyALIfWO06hnUS-vikMdIiOOjfwWEA2eap-Zph5B_t3xBmNm2JHzxdmC2mujM32qEjbqMSzsqqsF5Y5QJ6YnnMIy-jjQN4SYjTZMXwE53pDmNgR4kPTO8rdFMaSjQPYLdHnO7M21L_gtujv99-BtePTk_Genw8GT2GG4LEk1PJ-y5srxZfwhO45r6uzpeLp2kmMfhw2eD8CSynXIE |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1LbxMxEB6VghAceCNCC_gA4rSK7X0fqqqQRFQNUQ4gVVyMn6ES2oQkgPhr_LrOOLuhBx6nHrjuelfr9efxzHj8fQDPHbrUOgiTGPQGkqzEmNUULktkxX3phOWFi5T543IyqU5P6-kO_OzOwlBZZWcTo6F2c0s58j6uc4K46oqqH9qyiOlgdLj4kpCCFO20dnIaG4ic-B_fMXxbHRwPcKxfSDkavnv9JmkVBhKbpXKdSGl55nQwKfrtDsHoZFrmpTFpybkR2te19r4odVZYR0pBIjM5utiZDroocp_ie6_A1RJjTConnOYftvkd4tusinxzWi9Na95fd7lyDIFpu5BHgtQLq2EUDfjT0hDXu9Ht__lP3YFbrZfNjjbT4i7s-OYe3LzAvXgfJsPmE3GNNDM2jFJAZxYDEjaea8dIr9TqFVWEM0pUs7ex5NSzlo12xnTj2MD7xfbKA3h_KR16CLvNvPGPgHFphHC1dhJbh1DViPOK49QwoXYh5D1Iu0FWtmVeJwGQzwojMIKG-h00epBsn1psmEf-0f4V4WfblnjD44X5cqZaM6QsOugmVNKatMiMk6ayHj00EUTgeTChBy8JfYqsG36i1e0hDewo8YSpowrdl5oS0D3Y79CnWrO3Ur-g9_jvt5_BdcSkGh9PTvbghiRN5VgJvw-76-VX_wSu2W_rs9XyaZxUDD5eNjbPAd9BZaU |
| 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=Enhancing+Electricity+Load+Forecasting+with+Machine+Learning+and+Deep+Learning&rft.jtitle=Technologies+%28Basel%29&rft.au=Arb%C3%ABr+Per%C3%A7uku&rft.au=Daniela+Minkovska&rft.au=Nikolay+Hinov&rft.date=2025-02-01&rft.pub=MDPI+AG&rft.eissn=2227-7080&rft.volume=13&rft.issue=2&rft.spage=59&rft_id=info:doi/10.3390%2Ftechnologies13020059&rft.externalDBID=DOA&rft.externalDocID=oai_doaj_org_article_c540bf82cb364bd2b8ce6611f1f05fbf |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2227-7080&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2227-7080&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2227-7080&client=summon |