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Accurate PV power forecasting can improve the penetration of PV power in the grid. However, it is pretty challenging to predict PV power in short\u2010term under precious future meteorological information absence conditions. To address this problem, this study proposes the hybrid Contrastive Learning and Temporal Convolutional Network (CL\u2010TCN), and this forecasting approach consists of two parts, including model training and adaptive processes of forecasting models. In the model training stage, this forecasting method firstly trains 18 TCN models for 18 time points from 9:00\u2009a.m. to 17:30\u2009p.m. These TCN models are trained by only using historical PV power data samples, and each model is used to predict the next half\u2010hour power output. The adaptive process of models means that, in a practical forecasting stage, PV power samples from historical data are firstly evaluated and scored by a CL based data scoring mechanism to search for the most similar data samples to current measured samples. Then these similar samples are further applied to training a single above\u2010mentioned well\u2010trained TCN model to improve its performance in forecasting the next half\u2010hour PV power. The experimental results tested at the time resolution of 30\u2009min demonstrate that the proposed approach has superior performance in forecasting accuracy not only in smooth PV power samples but also in fluctuating PV power samples. Moreover, the proposed CL based data scoring mechanism can filter useless data samples effectively accelerating the forecasting process.<\/jats:p>","DOI":"10.1111\/coin.12606","type":"journal-article","created":{"date-parts":[[2023,10,25]],"date-time":"2023-10-25T05:57:25Z","timestamp":1698213445000},"update-policy":"http:\/\/dx.doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Short\u2010term photovoltaic power forecasting using hybrid contrastive learning and temporal convolutional network under future meteorological information absence"],"prefix":"10.1111","volume":"40","author":[{"given":"Xiaoyang","family":"Lu","sequence":"first","affiliation":[{"name":"School of Physics and Information Engineering, and Institute of Micro\u2010Nano Devices and Solar Cells Fuzhou\u00a0University Fuzhou China"},{"name":"Changzhou University Advanced Catalysis and Green Manufacturing Collaborative Innovation Center Changzhou China"},{"name":"Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia"}]},{"given":"Yandang","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Economics and Management Fuzhou University Fuzhou China"}]},{"given":"Qibin","family":"Li","sequence":"additional","affiliation":[{"name":"School of Physics and Information Engineering, and Institute of Micro\u2010Nano Devices and Solar Cells Fuzhou\u00a0University Fuzhou China"},{"name":"Changzhou University Advanced Catalysis and Green Manufacturing Collaborative Innovation Center Changzhou China"}]},{"given":"Pingping","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Physics and Information Engineering, and Institute of Micro\u2010Nano Devices and Solar Cells Fuzhou\u00a0University Fuzhou China"},{"name":"Changzhou University Advanced Catalysis and Green Manufacturing Collaborative Innovation Center Changzhou China"}]}],"member":"311","published-online":{"date-parts":[[2023,10,24]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2017.02.003"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.enconman.2017.06.058"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jobe.2019.100822"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-12-397177-7.00008-5"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.enconman.2017.03.056"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/TEC.2005.845454"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.solener.2015.06.017"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2022.3225267"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3038899"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.matcom.2015.05.010"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2016.01.130"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.120766"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.119937"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2023.119644"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.seta.2020.100670"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1049\/iet-gtd.2018.5847"},{"key":"e_1_2_8_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSYST.2020.3048817"},{"key":"e_1_2_8_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.renene.2020.12.021"},{"key":"e_1_2_8_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.enconman.2018.10.015"},{"key":"e_1_2_8_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSTE.2018.2881531"},{"key":"e_1_2_8_22_1","unstructured":"WangX QiG\u2010J.Contrastive learning with stronger augmentations. arXiv Preprint arXiv:2104.07713 (2021)."},{"key":"e_1_2_8_23_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58568-6_43"},{"key":"e_1_2_8_24_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108422"},{"key":"e_1_2_8_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.03.011"},{"key":"e_1_2_8_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPWRS.2017.2762599"},{"key":"e_1_2_8_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2019.114216"},{"key":"e_1_2_8_28_1","unstructured":"KingmaDP JimmyB. 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