FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3044019043> ?p ?o ?g. }

• W3044019043 endingPage "108250" @default.
• W3044019043 startingPage "108250" @default.
• W3044019043 abstract "Abstract A novel short-term solar power prediction model is presented in this work, by utilizing the learning ability of Long-Shot-Term-Memory network (LSTM) based deep learning (DL) technique and the concept of wavelet transform (WT). In this proposed WT-LSTM model, the WT is used to decompose the recorded solar energy time-series data into different frequency series followed by the statistical feature extraction process. The LSTM with dropout based DL model is proposed to predict the futuristic value of solar energy generation in different time-horizon (hourly and day basis), where the statistical WT based features combined with several other meteorological factors such as temperature, wind speed, pressure, cloudy-index, humidity and altimeter index are modelled as input to the LSTM model. The efficiency of the suggested WT-LSTM model has been proved by comparing statistical performance measures in terms of RMSE, MAPE, MAE and R2 score, with other contemporary machine learning and deep-learning based models." @default.
• W3044019043 created "2020-07-29" @default.
• W3044019043 creator A5023695569 @default.
• W3044019043 creator A5044493626 @default.
• W3044019043 creator A5044832580 @default.
• W3044019043 creator A5083141183 @default.
• W3044019043 creator A5084502652 @default.
• W3044019043 date "2020-12-01" @default.
• W3044019043 modified "2023-09-29" @default.
• W3044019043 title "Deep learning and wavelet transform integrated approach for short-term solar PV power prediction" @default.
• W3044019043 cites W1720804347 @default.
• W3044019043 cites W1966080301 @default.
• W3044019043 cites W1993859423 @default.
• W3044019043 cites W1995384696 @default.
• W3044019043 cites W2002404570 @default.
• W3044019043 cites W2005683380 @default.
• W3044019043 cites W2021358089 @default.
• W3044019043 cites W2024032348 @default.
• W3044019043 cites W2043944628 @default.
• W3044019043 cites W2044451649 @default.
• W3044019043 cites W2046580977 @default.
• W3044019043 cites W2049839711 @default.
• W3044019043 cites W2052043503 @default.
• W3044019043 cites W2061482009 @default.
• W3044019043 cites W2064675550 @default.
• W3044019043 cites W2075687602 @default.
• W3044019043 cites W2150355110 @default.
• W3044019043 cites W2210596467 @default.
• W3044019043 cites W2313570876 @default.
• W3044019043 cites W231446412 @default.
• W3044019043 cites W2327473694 @default.
• W3044019043 cites W2413306771 @default.
• W3044019043 cites W2465082089 @default.
• W3044019043 cites W2469734051 @default.
• W3044019043 cites W2471744513 @default.
• W3044019043 cites W2474623423 @default.
• W3044019043 cites W2487125635 @default.
• W3044019043 cites W2495466567 @default.
• W3044019043 cites W2529692997 @default.
• W3044019043 cites W2548664203 @default.
• W3044019043 cites W2562861021 @default.
• W3044019043 cites W2569349941 @default.
• W3044019043 cites W2587586954 @default.
• W3044019043 cites W2751698537 @default.
• W3044019043 cites W2755953816 @default.
• W3044019043 cites W2763128055 @default.
• W3044019043 cites W2763749451 @default.
• W3044019043 cites W2773629498 @default.
• W3044019043 cites W2792253101 @default.
• W3044019043 cites W2910849319 @default.
• W3044019043 cites W2912623183 @default.
• W3044019043 cites W2914530224 @default.
• W3044019043 cites W2950072808 @default.
• W3044019043 cites W2966479253 @default.
• W3044019043 cites W2983079756 @default.
• W3044019043 cites W2991802886 @default.
• W3044019043 cites W3022530230 @default.
• W3044019043 cites W3033542075 @default.
• W3044019043 cites W1653138571 @default.
• W3044019043 doi "https://doi.org/10.1016/j.measurement.2020.108250" @default.
• W3044019043 hasPublicationYear "2020" @default.
• W3044019043 type Work @default.
• W3044019043 sameAs 3044019043 @default.
• W3044019043 citedByCount "78" @default.
• W3044019043 countsByYear W30440190432020 @default.
• W3044019043 countsByYear W30440190432021 @default.
• W3044019043 countsByYear W30440190432022 @default.
• W3044019043 countsByYear W30440190432023 @default.
• W3044019043 crossrefType "journal-article" @default.
• W3044019043 hasAuthorship W3044019043A5023695569 @default.
• W3044019043 hasAuthorship W3044019043A5044493626 @default.
• W3044019043 hasAuthorship W3044019043A5044832580 @default.
• W3044019043 hasAuthorship W3044019043A5083141183 @default.
• W3044019043 hasAuthorship W3044019043A5084502652 @default.
• W3044019043 hasConcept C119599485 @default.
• W3044019043 hasConcept C121332964 @default.
• W3044019043 hasConcept C127413603 @default.
• W3044019043 hasConcept C154945302 @default.
• W3044019043 hasConcept C163258240 @default.
• W3044019043 hasConcept C196216189 @default.
• W3044019043 hasConcept C39432304 @default.
• W3044019043 hasConcept C41008148 @default.
• W3044019043 hasConcept C41291067 @default.
• W3044019043 hasConcept C47432892 @default.
• W3044019043 hasConcept C61797465 @default.
• W3044019043 hasConcept C62520636 @default.
• W3044019043 hasConceptScore W3044019043C119599485 @default.
• W3044019043 hasConceptScore W3044019043C121332964 @default.
• W3044019043 hasConceptScore W3044019043C127413603 @default.
• W3044019043 hasConceptScore W3044019043C154945302 @default.
• W3044019043 hasConceptScore W3044019043C163258240 @default.
• W3044019043 hasConceptScore W3044019043C196216189 @default.
• W3044019043 hasConceptScore W3044019043C39432304 @default.
• W3044019043 hasConceptScore W3044019043C41008148 @default.
• W3044019043 hasConceptScore W3044019043C41291067 @default.
• W3044019043 hasConceptScore W3044019043C47432892 @default.
• W3044019043 hasConceptScore W3044019043C61797465 @default.
• W3044019043 hasConceptScore W3044019043C62520636 @default.

• next