Matches in SemOpenAlex for { <https://semopenalex.org/work/W4296901416> ?p ?o ?g. }
- W4296901416 endingPage "119964" @default.
- W4296901416 startingPage "119964" @default.
- W4296901416 abstract "The randomness and volatility of solar irradiance pose a challenge to efficient solar energy development and utilization across the world, which increases the necessity of developing an efficient solar irradiance forecasting model. However, previous studies rarely emphasized the importance of transfer learning and nonlinear feature selection, especially for the studies associated with newly-built photovoltaic plants. Moreover, multi-step ahead forecasting research is limited, despite its significance to dispatching efficiency improvement of photovoltaic system. Aiming to address the research gaps, a novel hybrid deep learning framework (HDLF), consisting of the modules of feature selection, feature convolution, forecasting, self-attention, transfer learning, performance evaluation, and performance analysis, is newly proposed in this paper to perform multi-step ahead solar irradiation forecasting. Specifically, the HDLF is pre-trained based on the studied datasets of global horizontal irradiation in the source domains, and the abstract information obtained from pre-training is transferred to the HDLF of the target domain to enhance its performance. To validate the effectiveness of the proposed HDLF, two simulation experiments are carried out based on the datasets from California, USA, with a resolution of 1 h. The corresponding experimental results from the modules of performance evaluation and performance analysis indicate that the maximum improvements in mean absolute error reach 80.19% and 35.67% in two experiments, respectively, thereby confirming the superiority and feasibility of the HDLF." @default.
- W4296901416 created "2022-09-24" @default.
- W4296901416 creator A5003799076 @default.
- W4296901416 creator A5025394425 @default.
- W4296901416 creator A5027235926 @default.
- W4296901416 creator A5032621861 @default.
- W4296901416 date "2022-11-01" @default.
- W4296901416 modified "2023-10-06" @default.
- W4296901416 title "A hybrid deep learning framework integrating feature selection and transfer learning for multi-step global horizontal irradiation forecasting" @default.
- W4296901416 cites W1120922828 @default.
- W4296901416 cites W1500895378 @default.
- W4296901416 cites W1749036211 @default.
- W4296901416 cites W1964097006 @default.
- W4296901416 cites W1966080588 @default.
- W4296901416 cites W1981706820 @default.
- W4296901416 cites W1981986847 @default.
- W4296901416 cites W1982348007 @default.
- W4296901416 cites W2013643426 @default.
- W4296901416 cites W2058752779 @default.
- W4296901416 cites W2076256832 @default.
- W4296901416 cites W2084691488 @default.
- W4296901416 cites W2103496339 @default.
- W4296901416 cites W2469734051 @default.
- W4296901416 cites W2615529263 @default.
- W4296901416 cites W2762044196 @default.
- W4296901416 cites W2791043861 @default.
- W4296901416 cites W2791648058 @default.
- W4296901416 cites W2884486887 @default.
- W4296901416 cites W2889323772 @default.
- W4296901416 cites W2907029015 @default.
- W4296901416 cites W2910029420 @default.
- W4296901416 cites W2937053415 @default.
- W4296901416 cites W2963918665 @default.
- W4296901416 cites W2963928450 @default.
- W4296901416 cites W3008397052 @default.
- W4296901416 cites W3037712939 @default.
- W4296901416 cites W3090232294 @default.
- W4296901416 cites W3131610946 @default.
- W4296901416 cites W3135477757 @default.
- W4296901416 cites W3159397852 @default.
- W4296901416 cites W3166893749 @default.
- W4296901416 cites W3170329587 @default.
- W4296901416 cites W3198269957 @default.
- W4296901416 cites W3207462098 @default.
- W4296901416 cites W4206098628 @default.
- W4296901416 cites W4280529332 @default.
- W4296901416 cites W4283736942 @default.
- W4296901416 cites W4284896404 @default.
- W4296901416 doi "https://doi.org/10.1016/j.apenergy.2022.119964" @default.
- W4296901416 hasPublicationYear "2022" @default.
- W4296901416 type Work @default.
- W4296901416 citedByCount "6" @default.
- W4296901416 countsByYear W42969014162022 @default.
- W4296901416 countsByYear W42969014162023 @default.
- W4296901416 crossrefType "journal-article" @default.
- W4296901416 hasAuthorship W4296901416A5003799076 @default.
- W4296901416 hasAuthorship W4296901416A5025394425 @default.
- W4296901416 hasAuthorship W4296901416A5027235926 @default.
- W4296901416 hasAuthorship W4296901416A5032621861 @default.
- W4296901416 hasBestOaLocation W42969014161 @default.
- W4296901416 hasConcept C105795698 @default.
- W4296901416 hasConcept C108583219 @default.
- W4296901416 hasConcept C119599485 @default.
- W4296901416 hasConcept C119857082 @default.
- W4296901416 hasConcept C121332964 @default.
- W4296901416 hasConcept C125112378 @default.
- W4296901416 hasConcept C127413603 @default.
- W4296901416 hasConcept C138885662 @default.
- W4296901416 hasConcept C148483581 @default.
- W4296901416 hasConcept C150899416 @default.
- W4296901416 hasConcept C153294291 @default.
- W4296901416 hasConcept C154945302 @default.
- W4296901416 hasConcept C2776401178 @default.
- W4296901416 hasConcept C33923547 @default.
- W4296901416 hasConcept C41008148 @default.
- W4296901416 hasConcept C41291067 @default.
- W4296901416 hasConcept C41895202 @default.
- W4296901416 hasConcept C46423501 @default.
- W4296901416 hasConcept C62520636 @default.
- W4296901416 hasConcept C9695528 @default.
- W4296901416 hasConceptScore W4296901416C105795698 @default.
- W4296901416 hasConceptScore W4296901416C108583219 @default.
- W4296901416 hasConceptScore W4296901416C119599485 @default.
- W4296901416 hasConceptScore W4296901416C119857082 @default.
- W4296901416 hasConceptScore W4296901416C121332964 @default.
- W4296901416 hasConceptScore W4296901416C125112378 @default.
- W4296901416 hasConceptScore W4296901416C127413603 @default.
- W4296901416 hasConceptScore W4296901416C138885662 @default.
- W4296901416 hasConceptScore W4296901416C148483581 @default.
- W4296901416 hasConceptScore W4296901416C150899416 @default.
- W4296901416 hasConceptScore W4296901416C153294291 @default.
- W4296901416 hasConceptScore W4296901416C154945302 @default.
- W4296901416 hasConceptScore W4296901416C2776401178 @default.
- W4296901416 hasConceptScore W4296901416C33923547 @default.
- W4296901416 hasConceptScore W4296901416C41008148 @default.
- W4296901416 hasConceptScore W4296901416C41291067 @default.
- W4296901416 hasConceptScore W4296901416C41895202 @default.
- W4296901416 hasConceptScore W4296901416C46423501 @default.