Management of Distributed Renewable Energy Resources with the Help of a Wireless Sensor Network
Photovoltaic (PV) and wind energy are widely considered eco-friendly renewable energy resources. However, due to the unpredictable oscillations in solar and wind power production, efficient management to meet load demands is often hard to achieve. As a result, precise forecasting of PV and wind ener...
Saved in:
| Published in: | Applied sciences Vol. 12; no. 14; p. 6908 |
|---|---|
| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Basel
MDPI AG
01.07.2022
|
| Subjects: | |
| ISSN: | 2076-3417, 2076-3417 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | Photovoltaic (PV) and wind energy are widely considered eco-friendly renewable energy resources. However, due to the unpredictable oscillations in solar and wind power production, efficient management to meet load demands is often hard to achieve. As a result, precise forecasting of PV and wind energy production is critical for grid managers to limit the impact of random fluctuations. In this study, the kernel recursive least-squares (KRLS) algorithm is proposed for the prediction of PV and wind energy. The wireless sensor network (WSN) typically adopted for data collection with a flexible configuration of sensor nodes is used to transport PV and wind production data to the monitoring center. For efficient transmission of the data production, a link scheduling technique based on sensor node attributes is proposed. Different statistical and machine learning (ML) techniques are examined with respect to the proposed KRLS algorithm for performance analysis. The comparison results show that the KRLS algorithm surpasses all other regression approaches. For both PV and wind power feed-in forecasts, the proposed KRLS algorithm demonstrates high forecasting accuracy. In addition, the link scheduling proposed for the transmission of data for the management of distributed renewable energy resources is compared with a reference technique to show its comparable performance. The efficacy of the proposed KRLS model is better than other regression models in all assessment events in terms of an RMSE value of 0.0146, MAE value of 0.00021, and R2 of 99.7% for PV power, and RMSE value of 0.0421, MAE value of 0.0018, and R2 of 88.17% for wind power. In addition to this, the proposed link scheduling approach results in 22% lower latency and 38% higher resource utilization through the efficient scheduling of time slots. |
|---|---|
| AbstractList | Photovoltaic (PV) and wind energy are widely considered eco-friendly renewable energy resources. However, due to the unpredictable oscillations in solar and wind power production, efficient management to meet load demands is often hard to achieve. As a result, precise forecasting of PV and wind energy production is critical for grid managers to limit the impact of random fluctuations. In this study, the kernel recursive least-squares (KRLS) algorithm is proposed for the prediction of PV and wind energy. The wireless sensor network (WSN) typically adopted for data collection with a flexible configuration of sensor nodes is used to transport PV and wind production data to the monitoring center. For efficient transmission of the data production, a link scheduling technique based on sensor node attributes is proposed. Different statistical and machine learning (ML) techniques are examined with respect to the proposed KRLS algorithm for performance analysis. The comparison results show that the KRLS algorithm surpasses all other regression approaches. For both PV and wind power feed-in forecasts, the proposed KRLS algorithm demonstrates high forecasting accuracy. In addition, the link scheduling proposed for the transmission of data for the management of distributed renewable energy resources is compared with a reference technique to show its comparable performance. The efficacy of the proposed KRLS model is better than other regression models in all assessment events in terms of an RMSE value of 0.0146, MAE value of 0.00021, and R2 of 99.7% for PV power, and RMSE value of 0.0421, MAE value of 0.0018, and R2 of 88.17% for wind power. In addition to this, the proposed link scheduling approach results in 22% lower latency and 38% higher resource utilization through the efficient scheduling of time slots. |
| Author | Nengroo, Sarvar Hussain Jin, Hojun Lee, Sangkeum |
| Author_xml | – sequence: 1 givenname: Sarvar Hussain orcidid: 0000-0002-9651-9282 surname: Nengroo fullname: Nengroo, Sarvar Hussain – sequence: 2 givenname: Hojun orcidid: 0000-0002-3751-4983 surname: Jin fullname: Jin, Hojun – sequence: 3 givenname: Sangkeum orcidid: 0000-0001-6918-124X surname: Lee fullname: Lee, Sangkeum |
| BookMark | eNptUV1LHTEQDWKhan3qHwj0UW6bj3U3eSxWq2AVVPAxzCaTa65rsia5XPz3jb2lSHFeZhjOOfNx9sluTBEJ-czZVyk1-wbzzAXves3UDtkTbOgXsuPD7pv6IzksZcVaaC4VZ3vE_IIIS3zCWGny9EcoNYdxXdHRG4y4gXFCehoxL19ao6R1tljoJtQHWh-QnuM0v_KA3oeME5ZCbzGWlOkV1k3Kj5_IBw9TwcO_-YDcnp3enZwvLq9_Xpx8v1xY2Xd1AR3j0DtUsmfAe-asGABR2E6DEBq1cwPYceRa8ZEPSuCo0CpU3tteygNysVV1CVZmzuEJ8otJEMyfRspLA7kGO6Hho1eg2gO8c50Dppzq_LHDkfkOwdmm9WWrNef0vMZSzapdHdvyRvRaatmeLRrqaIuyOZWS0f-bypl5tcO8saOh-X9oGyrUkGLNEKZ3Ob8BHN2QzA |
| CitedBy_id | crossref_primary_10_1109_ACCESS_2024_3381859 crossref_primary_10_3390_en18185017 crossref_primary_10_3390_app12147315 crossref_primary_10_1016_j_eswa_2024_124759 crossref_primary_10_3390_en15228573 crossref_primary_10_1007_s11276_022_03186_4 crossref_primary_10_1016_j_est_2024_112489 crossref_primary_10_3390_app13021080 crossref_primary_10_1109_ACCESS_2024_3524097 crossref_primary_10_3390_electronics12204304 crossref_primary_10_3390_en16010096 crossref_primary_10_3389_fevo_2023_1132678 crossref_primary_10_3390_en15218042 |
| Cites_doi | 10.1016/j.rser.2015.07.024 10.3390/en12030446 10.1109/TSP.2004.830985 10.1016/j.ijepes.2015.07.039 10.1109/ACCESS.2015.2496117 10.1109/ACCESS.2019.2936863 10.1109/WCNC.2009.4917961 10.1155/2022/1575303 10.1109/JSEN.2017.2746183 10.1002/9781119482260 10.1609/aaai.v32i1.11635 10.1109/JSEN.2020.3035846 10.3390/app12094786 10.1109/72.914517 10.1109/TBC.2011.2105611 10.1016/j.jclepro.2020.122808 10.1109/TPWRS.2015.2418333 10.1016/j.enconman.2017.04.077 10.1016/j.solener.2011.11.013 10.3390/electronics7090177 10.1016/j.renene.2014.08.006 10.1049/rpg2.12195 10.1016/j.procs.2011.07.099 10.3390/electronics8020176 10.3390/su13126681 10.1088/1741-2560/11/1/016003 10.1002/er.7201 10.3390/en11051122 10.1109/ICRERA52334.2021.9598765 10.1007/978-3-319-29504-6_20 10.1016/j.rser.2016.11.026 10.3390/en12020215 10.1016/j.isci.2021.103286 10.1007/978-3-642-17994-5_14 10.3390/app10175975 10.3390/s17010122 10.1109/NAPS.2014.6965389 10.1109/TPWRS.2006.873018 10.3390/s21165668 10.1016/j.enbuild.2019.03.026 10.4018/978-1-7998-3222-5.ch009 10.1007/s00521-017-3225-z 10.1109/TITS.2018.2797991 10.1109/ACCESS.2019.2942012 10.1016/j.proenv.2011.12.196 10.1016/j.apenergy.2020.115098 10.1109/JSEN.2012.2188792 10.1016/j.comcom.2017.05.014 10.1109/TII.2017.2771256 10.1109/TVT.2018.2805189 10.1016/j.renene.2012.03.003 10.1155/2022/7952860 10.1109/ICPES53652.2021.9683905 10.1109/ACCESS.2019.2923006 10.1109/DSP-SPE.2013.6642587 10.1016/j.enconman.2019.111799 10.1016/j.renene.2004.09.020 10.1016/j.adhoc.2015.07.004 10.1109/TCNS.2021.3059851 10.1080/07474938.2010.481556 10.1016/j.comnet.2008.04.002 10.1109/ACCESS.2020.2969460 10.1109/TMC.2012.161 10.2172/919131 10.1109/TIE.2009.2039455 |
| ContentType | Journal Article |
| Copyright | 2022 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: 2022 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 ABUWG AFKRA AZQEC BENPR CCPQU DWQXO PHGZM PHGZT PIMPY PKEHL PQEST PQQKQ PQUKI PRINS DOA |
| DOI | 10.3390/app12146908 |
| DatabaseName | CrossRef ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials ProQuest Central ProQuest One Community College ProQuest Central Korea ProQuest Central Premium ProQuest One Academic (New) Publicly Available Content Database (Proquest) ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest One Academic Eastern Edition ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest Central China ProQuest Central ProQuest One Academic UKI Edition ProQuest Central Korea ProQuest Central (New) ProQuest One Academic ProQuest One Academic (New) |
| DatabaseTitleList | CrossRef Publicly Available Content Database |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Sciences (General) |
| EISSN | 2076-3417 |
| ExternalDocumentID | oai_doaj_org_article_1bf8a8009fdd4da08d84f5deb0f4eadc 10_3390_app12146908 |
| GroupedDBID | .4S 2XV 5VS 7XC 8CJ 8FE 8FG 8FH AADQD AAFWJ AAYXX ADBBV ADMLS AFFHD AFKRA AFPKN AFZYC ALMA_UNASSIGNED_HOLDINGS APEBS ARCSS BCNDV BENPR CCPQU CITATION CZ9 D1I D1J D1K GROUPED_DOAJ IAO IGS ITC K6- K6V KC. KQ8 L6V LK5 LK8 M7R MODMG M~E OK1 P62 PHGZM PHGZT PIMPY PROAC TUS ABUWG AZQEC DWQXO PKEHL PQEST PQQKQ PQUKI PRINS PUEGO |
| ID | FETCH-LOGICAL-c364t-a401a6de8360a160dc27aee2c49a229e9dd7acbb1981b1782eb8ec8e8ffc633 |
| IEDL.DBID | DOA |
| ISICitedReferencesCount | 14 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000831882300001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2076-3417 |
| IngestDate | Tue Oct 14 19:07:40 EDT 2025 Thu Sep 11 11:41:42 EDT 2025 Sat Nov 29 07:10:36 EST 2025 Tue Nov 18 20:56:27 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 14 |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c364t-a401a6de8360a160dc27aee2c49a229e9dd7acbb1981b1782eb8ec8e8ffc633 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0002-9651-9282 0000-0001-6918-124X 0000-0002-3751-4983 |
| OpenAccessLink | https://doaj.org/article/1bf8a8009fdd4da08d84f5deb0f4eadc |
| PQID | 2693933392 |
| PQPubID | 2032433 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_1bf8a8009fdd4da08d84f5deb0f4eadc proquest_journals_2693933392 crossref_primary_10_3390_app12146908 crossref_citationtrail_10_3390_app12146908 |
| PublicationCentury | 2000 |
| PublicationDate | 2022-07-01 |
| PublicationDateYYYYMMDD | 2022-07-01 |
| PublicationDate_xml | – month: 07 year: 2022 text: 2022-07-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | Basel |
| PublicationPlace_xml | – name: Basel |
| PublicationTitle | Applied sciences |
| PublicationYear | 2022 |
| Publisher | MDPI AG |
| Publisher_xml | – name: MDPI AG |
| References | Juaidi (ref_16) 2016; 55 (ref_22) 2005; 30 Lopes (ref_8) 2006; 21 Dabbaghjamanesh (ref_17) 2021; 8 Wang (ref_15) 2017; 14 ref_57 ref_56 ref_11 ref_55 ref_10 ref_54 Kim (ref_43) 2020; 21 Gad (ref_29) 2015; 74 Zhang (ref_41) 2019; 7 ref_18 Tobias (ref_52) 2012; 46 ref_61 Zhou (ref_37) 2019; 7 ref_69 Engel (ref_68) 2004; 52 ref_21 Ouyang (ref_39) 2017; 144 Yick (ref_50) 2008; 52 ref_64 Lee (ref_13) 2019; 191 Lee (ref_60) 2020; 18 Ding (ref_33) 2011; 11 Kim (ref_25) 2011; 57 ref_27 Shahid (ref_40) 2020; 269 Haghifam (ref_19) 2021; 278 Aijaz (ref_28) 2017; 17 Qayyum (ref_12) 2015; 3 Ahmed (ref_46) 2010; 29 Ali (ref_65) 2021; 24 (ref_51) 2011; 5 Wang (ref_20) 2019; 198 Schudlo (ref_67) 2013; 11 ref_34 ref_32 Das (ref_36) 2021; 39 Lee (ref_58) 2020; 8 Seo (ref_44) 2019; 7 Hong (ref_24) 2012; 12 Muller (ref_66) 2001; 12 Hu (ref_59) 2015; 35 Izgi (ref_23) 2012; 86 Mahmoud (ref_30) 2015; 31 ref_47 Hwang (ref_26) 2018; 20 ref_45 Saleh (ref_53) 2016; 74 ref_42 Ali (ref_6) 2022; 46 ref_1 Hosni (ref_63) 2017; 110 Zhang (ref_14) 2018; 67 ref_3 Wasilewski (ref_38) 2017; 69 He (ref_62) 2012; 12 ref_2 ref_49 ref_48 ref_9 Gungor (ref_31) 2010; 57 ref_5 ref_4 Lee (ref_7) 2021; 15 Mahmoud (ref_35) 2019; 31 |
| References_xml | – volume: 55 start-page: 1195 year: 2016 ident: ref_16 article-title: An overview of energy balance compared to sustainable energy in United Arab Emirates publication-title: Renew. Sustain. Energy Rev. doi: 10.1016/j.rser.2015.07.024 – ident: ref_3 doi: 10.3390/en12030446 – volume: 52 start-page: 2275 year: 2004 ident: ref_68 article-title: The kernel recursive least-squares algorithm publication-title: IEEE Trans. Signal Process. doi: 10.1109/TSP.2004.830985 – volume: 74 start-page: 384 year: 2016 ident: ref_53 article-title: A hybrid neuro-fuzzy power prediction system for wind energy generation publication-title: Int. J. Electr. Power Energy Syst. doi: 10.1016/j.ijepes.2015.07.039 – volume: 3 start-page: 2176 year: 2015 ident: ref_12 article-title: Appliance scheduling optimization in smart home networks publication-title: IEEE Access doi: 10.1109/ACCESS.2015.2496117 – volume: 7 start-page: 118776 year: 2019 ident: ref_44 article-title: Rewards prediction-based credit assignment for reinforcement learning with sparse binary rewards publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2936863 – ident: ref_61 doi: 10.1109/WCNC.2009.4917961 – ident: ref_64 doi: 10.1155/2022/1575303 – volume: 17 start-page: 6825 year: 2017 ident: ref_28 article-title: DeAMON: A decentralized adaptive multi-hop scheduling protocol for 6TiSCH wireless networks publication-title: IEEE Sens. J. doi: 10.1109/JSEN.2017.2746183 – ident: ref_45 doi: 10.1002/9781119482260 – ident: ref_47 doi: 10.1609/aaai.v32i1.11635 – volume: 21 start-page: 12379 year: 2020 ident: ref_43 article-title: Machine Learning for Advanced Wireless Sensor Networks: A Review publication-title: IEEE Sens. J. doi: 10.1109/JSEN.2020.3035846 – ident: ref_9 doi: 10.3390/app12094786 – volume: 12 start-page: 181 year: 2001 ident: ref_66 article-title: An introduction to kernel-based learning algorithms publication-title: IEEE Trans. Neural Netw. doi: 10.1109/72.914517 – ident: ref_42 – volume: 57 start-page: 307 year: 2011 ident: ref_25 article-title: A pilot symbol pattern enabling data recovery without side information in PTS-based OFDM systems publication-title: IEEE Trans. Broadcasting doi: 10.1109/TBC.2011.2105611 – volume: 278 start-page: 122808 year: 2021 ident: ref_19 article-title: Stochastic bi-level coordination of active distribution network and renewable-based microgrid considering eco-friendly Compressed Air Energy Storage system and Intelligent Parking Lot publication-title: J. Clean. Prod. doi: 10.1016/j.jclepro.2020.122808 – volume: 31 start-page: 960 year: 2015 ident: ref_30 article-title: Optimal distributed generation allocation in distribution systems for loss minimization publication-title: IEEE Trans. Power Syst. doi: 10.1109/TPWRS.2015.2418333 – volume: 144 start-page: 361 year: 2017 ident: ref_39 article-title: A combined multivariate model for wind power prediction publication-title: Energy Convers. Manag. doi: 10.1016/j.enconman.2017.04.077 – volume: 86 start-page: 725 year: 2012 ident: ref_23 article-title: Short–mid-term solar power prediction by using artificial neural networks publication-title: Sol. Energy doi: 10.1016/j.solener.2011.11.013 – ident: ref_48 – ident: ref_1 doi: 10.3390/electronics7090177 – volume: 74 start-page: 337 year: 2015 ident: ref_29 article-title: Development of a new temperature data acquisition system for solar energy applications publication-title: Renew. Energy doi: 10.1016/j.renene.2014.08.006 – volume: 15 start-page: 3505 year: 2021 ident: ref_7 article-title: Cooperative decentralized peer-to-peer electricity trading of nanogrid clusters based on predictions of load demand and PV power generation using a gated recurrent unit model publication-title: IET Renew. Power Gener. doi: 10.1049/rpg2.12195 – volume: 5 start-page: 749 year: 2011 ident: ref_51 article-title: Next-generation wireless sensor networks infrastructure development for monitoring applications publication-title: Procedia Comput. Sci. doi: 10.1016/j.procs.2011.07.099 – volume: 39 start-page: 1959 year: 2021 ident: ref_36 article-title: Short term forecasting of solar radiation and power output of 89.6 kWp solar PV power plant publication-title: Mater. Today: Proc. – ident: ref_57 doi: 10.3390/electronics8020176 – ident: ref_4 doi: 10.3390/su13126681 – volume: 11 start-page: 016003 year: 2013 ident: ref_67 article-title: Dynamic topographical pattern classification of multichannel prefrontal NIRS signals: II. Online differentiation of mental arithmetic and rest publication-title: J. Neural Eng. doi: 10.1088/1741-2560/11/1/016003 – volume: 46 start-page: 774 year: 2022 ident: ref_6 article-title: Early hotspot detection in photovoltaic modules using color image descriptors: An infrared thermography study publication-title: Int. J. Energy Res. doi: 10.1002/er.7201 – ident: ref_2 doi: 10.3390/en11051122 – ident: ref_11 doi: 10.1109/ICRERA52334.2021.9598765 – ident: ref_49 doi: 10.1007/978-3-319-29504-6_20 – volume: 69 start-page: 177 year: 2017 ident: ref_38 article-title: Short-term electric energy production forecasting at wind power plants in pareto-optimality context publication-title: Renew. Sustain. Energy Rev. doi: 10.1016/j.rser.2016.11.026 – ident: ref_34 doi: 10.3390/en12020215 – volume: 24 start-page: 103286 year: 2021 ident: ref_65 article-title: Kernel recursive least square tracker and long-short term memory ensemble based battery health prognostic model publication-title: Iscience doi: 10.1016/j.isci.2021.103286 – ident: ref_27 doi: 10.1007/978-3-642-17994-5_14 – ident: ref_56 doi: 10.3390/app10175975 – ident: ref_32 doi: 10.3390/s17010122 – ident: ref_55 doi: 10.1109/NAPS.2014.6965389 – volume: 21 start-page: 916 year: 2006 ident: ref_8 article-title: Defining control strategies for microgrids islanded operation publication-title: IEEE Trans. Power Syst. doi: 10.1109/TPWRS.2006.873018 – volume: 18 start-page: 77 year: 2020 ident: ref_60 article-title: Optimal Link Scheduling Based on Attributes of Nodes in 6TiSCH Wireless Networks publication-title: J. Korean Inst. Inf. Technol. – ident: ref_5 doi: 10.3390/s21165668 – volume: 191 start-page: 174 year: 2019 ident: ref_13 article-title: Optimal power management for nanogrids based on technical information of electric appliances publication-title: Energy Build. doi: 10.1016/j.enbuild.2019.03.026 – ident: ref_18 doi: 10.4018/978-1-7998-3222-5.ch009 – volume: 31 start-page: 2727 year: 2019 ident: ref_35 article-title: Accurate photovoltaic power forecasting models using deep LSTM-RNN publication-title: Neural Comput. Appl. doi: 10.1007/s00521-017-3225-z – volume: 20 start-page: 374 year: 2018 ident: ref_26 article-title: Ferrite position identification system operating with wireless power transfer for intelligent train position detection publication-title: IEEE Trans. Intell. Transp. Syst. doi: 10.1109/TITS.2018.2797991 – volume: 7 start-page: 136254 year: 2019 ident: ref_41 article-title: Wind power prediction based on PSO-SVR and grey combination model publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2942012 – volume: 11 start-page: 1308 year: 2011 ident: ref_33 article-title: An ANN-based approach for forecasting the power output of photovoltaic system publication-title: Procedia Environ. Sci. doi: 10.1016/j.proenv.2011.12.196 – volume: 269 start-page: 115098 year: 2020 ident: ref_40 article-title: A novel wavenets long short term memory paradigm for wind power prediction publication-title: Appl. Energy doi: 10.1016/j.apenergy.2020.115098 – volume: 12 start-page: 2380 year: 2012 ident: ref_24 article-title: Spectrum sensing by parallel pairs of cross-correlators and comb filters for OFDM systems with pilot tones publication-title: IEEE Sens. J. doi: 10.1109/JSEN.2012.2188792 – volume: 110 start-page: 103 year: 2017 ident: ref_63 article-title: Self-healing distributed scheduling for end-to-end delay optimization in multihop wireless networks with 6TiSCh publication-title: Comput. Commun. doi: 10.1016/j.comcom.2017.05.014 – volume: 14 start-page: 2932 year: 2017 ident: ref_15 article-title: Intelligent optimal control with critic learning for a nonlinear overhead crane system publication-title: IEEE Trans. Ind. Inform. doi: 10.1109/TII.2017.2771256 – volume: 67 start-page: 5695 year: 2018 ident: ref_14 article-title: Long short-term memory recurrent neural network for remaining useful life prediction of lithium-ion batteries publication-title: IEEE Trans. Veh. Technol. doi: 10.1109/TVT.2018.2805189 – volume: 46 start-page: 169 year: 2012 ident: ref_52 article-title: Condition monitoring of wind turbines: Techniques and methods publication-title: Renew. Energy doi: 10.1016/j.renene.2012.03.003 – ident: ref_54 doi: 10.1155/2022/7952860 – ident: ref_10 doi: 10.1109/ICPES53652.2021.9683905 – volume: 7 start-page: 78063 year: 2019 ident: ref_37 article-title: Short-term photovoltaic power forecasting based on long short term memory neural network and attention mechanism publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2923006 – ident: ref_69 doi: 10.1109/DSP-SPE.2013.6642587 – volume: 198 start-page: 111799 year: 2019 ident: ref_20 article-title: A review of deep learning for renewable energy forecasting publication-title: Energy Convers. Manag. doi: 10.1016/j.enconman.2019.111799 – volume: 30 start-page: 1075 year: 2005 ident: ref_22 article-title: Forecasting based on neural network approach of solar potential in Turkey publication-title: Renew. Energy doi: 10.1016/j.renene.2004.09.020 – volume: 35 start-page: 127 year: 2015 ident: ref_59 article-title: Intelligent photovoltaic monitoring based on solar irradiance big data and wireless sensor networks publication-title: Ad Hoc Netw. doi: 10.1016/j.adhoc.2015.07.004 – volume: 8 start-page: 1201 year: 2021 ident: ref_17 article-title: Blockchain-based Stochastic Energy Management of Interconnected Microgrids Considering Incentive Price publication-title: IEEE Trans. Control Netw. Syst. doi: 10.1109/TCNS.2021.3059851 – volume: 29 start-page: 594 year: 2010 ident: ref_46 article-title: An empirical comparison of machine learning models for time series forecasting publication-title: Econom. Rev. doi: 10.1080/07474938.2010.481556 – volume: 52 start-page: 2292 year: 2008 ident: ref_50 article-title: Wireless sensor network survey publication-title: Comput. Netw. doi: 10.1016/j.comnet.2008.04.002 – volume: 8 start-page: 24081 year: 2020 ident: ref_58 article-title: Power management by LSTM network for nanogrids publication-title: IEEE Access doi: 10.1109/ACCESS.2020.2969460 – volume: 12 start-page: 1931 year: 2012 ident: ref_62 article-title: Energy provisioning in wireless rechargeable sensor networks publication-title: IEEE Trans. Mob. Comput. doi: 10.1109/TMC.2012.161 – ident: ref_21 doi: 10.2172/919131 – volume: 57 start-page: 3557 year: 2010 ident: ref_31 article-title: Opportunities and challenges of wireless sensor networks in smart grid publication-title: IEEE Trans. Ind. Electron. doi: 10.1109/TIE.2009.2039455 |
| SSID | ssj0000913810 |
| Score | 2.3394008 |
| Snippet | Photovoltaic (PV) and wind energy are widely considered eco-friendly renewable energy resources. However, due to the unpredictable oscillations in solar and... |
| SourceID | doaj proquest crossref |
| SourceType | Open Website Aggregation Database Enrichment Source Index Database |
| StartPage | 6908 |
| SubjectTerms | Algorithms Alternative energy sources Communication Energy resources Energy storage Forecasting Forecasting techniques Internet of Things KRLS technique Neural networks power generation uncertainty prediction accuracy PV and wind power prediction regression models renewable energy Renewable resources Scheduling Sensors Social research Wind power Wireless networks |
| SummonAdditionalLinks | – databaseName: ProQuest Central dbid: BENPR link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LS-RAEC5c3YMeXJ84vuiDB3chmKR7ku6T-BjZwzKIyuIt9FMEmYyTUf--VZmecWDFy16T6hCornfVVwBHXHWd6wqRKGtCIkTJE-VEmRi0ncaXloe2ev73T9nvy_t7dR0Tbk1sq5zqxFZRu9pSjvwkLxTH4BvN-enwOaGtUVRdjSs0vsESIZXhPV867_Wvb2ZZFkK9lFk6GczDwynVhTNaZq1ooeScKWoR-_9RyK2Vufrxv_-3BqvRv2RnkwuxDgt-sAErc6iDG7Ae5blhxxF0-ucmVB99MKwO7JLwdGkVlnfsBoneaMKK9dpBQTZN-TeMsrgMXUiG1mtI5zSjdtonVJ_sFgPkesT6kz7zLbi96t1d_E7i8oXE8kKME42Bly6cpyEPnRWps3mpvc-tUDrPlVfOldoakyl0fDP0M7yR3kovQ7AF59uwOKgHfgeY8Cmnb7QlTmmkEtZwngnvU4nGMO3ArykXKhtxyWk9xlOF8QmxrJpjWQeOZsTDCRzH52TnxM4ZCWFotw_q0UMVRbLKTJAa_WUVnBNOp9JJEbrOmzQIlC_bgf0pp6so2E31webdr1_vwXJOkxJtZ-8-LI5HL_4AvtvX8WMzOoz39B027vOi priority: 102 providerName: ProQuest |
| Title | Management of Distributed Renewable Energy Resources with the Help of a Wireless Sensor Network |
| URI | https://www.proquest.com/docview/2693933392 https://doaj.org/article/1bf8a8009fdd4da08d84f5deb0f4eadc |
| Volume | 12 |
| WOSCitedRecordID | wos000831882300001&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: 2076-3417 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913810 issn: 2076-3417 databaseCode: DOA dateStart: 20110101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources (ISSN International Center) customDbUrl: eissn: 2076-3417 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913810 issn: 2076-3417 databaseCode: M~E dateStart: 20110101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2076-3417 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913810 issn: 2076-3417 databaseCode: BENPR dateStart: 20110101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 2076-3417 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913810 issn: 2076-3417 databaseCode: PIMPY dateStart: 20110101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3dSxtBEB9K7IM-FBMVY23YBx-qcHh3u7nbfVSb0EIbgorEp2M_QZBEklT__c7sXfTAQl_6eMfs7TE7Ox-7M78BOOFq6NxQiERZExIhSp4oJ8rEoO00vrQ8xNvzu5_lZCJnMzVttfqinLAaHrhm3HlmgtTo1ajgnHA6lU6KMHTepEEgFyxp37RUrWAq6mCVEXRVXZDHMa6n--CMmlgraiTZMkERqf-dIo7WZbwLnxq3kF3Uv9OFD37eg50WWGAPus02XLGvDVb06R5Ub-krbBHYN4LBpQ5W3rFrJHqhwig2ivV9bHNSv2J0-MrQ82NodJ5onGaUBfuIWo_dYFy7WLJJnR6-Dzfj0e3V96TpmZBYXoh1ojFe0oXzVJuhsyJ1Ni-197kVSue58sq5UltjMoX-aobugTfSW-llCLbg_AA688XcHwITPuX0jXgzKY1UwhrOM-F9KtGGpX042zCxsg2cOHW1eKwwrCCOVy2O9-HklfipRtH4O9klrcYrCUFfxxcoEFUjENW_BKIPx5u1rJr9uKryQnHFcb786H_M8Rm2cyqDiGm7x9BZL3_7L_DRPq8fVssBbF2OJtPrQRRJfJr--DW9_wMETOnM |
| linkProvider | Directory of Open Access Journals |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1ba9RAFD6UraA-tLYqbq06DxVUCOYym8w8FFHb0qXbZbFF6ovDXEUom3WzWvxP_kjPyWW7oPjWB1-Tk5Bkvnxnzh1gL5MD5wacR9KaEHFeZJF0vIgM6k7jC5uFOnr-cVSMx-LiQk7W4FdXC0NplR0n1kTtSks-8tdpLjM0vlGdv5l9i2hqFEVXuxEaDSxO_M8rNNmq_eEBru_zND06PH9_HLVTBSKb5XwRabQodO48VS_oJI-dTQvtfWq51GkqvXSu0NYYtMYTk6AC9UZ4K7wIwebk_kTCX-cE9R6sT4ank09Lnw712BRJ3JQB4qPGFIVOaHS2pPGVK4qvng_wB_3XOu1o8__6Gvdgo907s7cN2LdgzU-34e5KR8Vt2Gq5qmIv2obaL--Dus7xYWVgB9QrmMZ8ecc-oNAVVY-xw7oIknXhjIqRh5rh9pihZp7RdZpRqvAlqgZ2hsZ_OWfjJof-AZzdwEs_hN60nPpHwLiPM7pHHb4VRkhuTZYl3PtYoKKP-_CqW3Nl257rNPrjUqHtRQBRKwDpw95SeNa0Gvm72DsCz1KE-oPXB8r5F9XSjUpMEBptARmc407HwgkeBs6bOHDkDtuH3Q5XqiWtSl2Dauffp5_B7ePz05EaDccnj-FOShUhdQbzLvQW8-_-CdyyPxZfq_nT9g9h8PlmQfgbPUhRfA |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1ba9RAFD6UrYg-qK2Ka1udhwoqhOYym8w8iFS3i0vrsliR-uKQuYlQNutmbfGf-fM8J5lsFxTf-uBrMglJ5sv5zv0A7GdyYO2A80ga7SPOiyySlheRRu7UrjCZb6Lnn06KyUScncnpBvzqamEorbKTiY2gtpUhH_lBmssMjW-k8wMf0iKmw9Hr-feIJkhRpLUbp9FC5Nj9vETzrX41HuJeP0vT0dHHt--iMGEgMlnOl1GJ1kWZW0eVDGWSx9akRelcargs01Q6aW1RGq3RMk90gmTqtHBGOOG9yckVisJ_ExVynvZgczp-P_288u9Qv02RxG1JID52TBHphMZoSxpluUaCzayAP6ig4bfR3f_3y9yDO0GnZoftT7AFG262DbfXOi1uw1aQYTV7Hhptv7gP6ir3h1WeDamHMI3_cpZ9wEWXVFXGjpriSNaFOWpGnmuGajNDxp7TdSWjFOJzpAx26mZ1tWCTNrf-AZxew0s_hN6smrlHwLiLM7pHE9YVWkhudJYl3LlYoAIQ9-Flt__KhF7sNBLkXKFNRmBRa2Dpw_5q8bxtQfL3ZW8ISKsl1De8OVAtvqoghlSivSjRRpDeWm7LWFjB_cA6HXuOMsX0YbfDmArCrFZXAHv879NP4SYiT52MJ8c7cCulQpEmsXkXesvFD7cHN8zF8lu9eBJ-FgZfrheDvwHFV1o8 |
| 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=Management+of+Distributed+Renewable+Energy+Resources+with+the+Help+of+a+Wireless+Sensor+Network&rft.jtitle=Applied+sciences&rft.au=Nengroo%2C+Sarvar+Hussain&rft.au=Jin%2C+Hojun&rft.au=Lee%2C+Sangkeum&rft.date=2022-07-01&rft.issn=2076-3417&rft.eissn=2076-3417&rft.volume=12&rft.issue=14&rft.spage=6908&rft_id=info:doi/10.3390%2Fapp12146908&rft.externalDBID=n%2Fa&rft.externalDocID=10_3390_app12146908 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2076-3417&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2076-3417&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2076-3417&client=summon |