FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3112294296> ?p ?o ?g. }

• W3112294296 endingPage "4727" @default.
• W3112294296 startingPage "4713" @default.
• W3112294296 abstract "In recent years, with the rapid growth of rooftop photovoltaic (PV) generation in distribution networks, power system operators call for accurate forecasts of the behind-the-meter (BTM) load and PV generation. However, the existing forecasting methodologies are incapable of quantifying such BTM measurements as the smart meters can merely measure the net load time series. Motivated by this challenge, this article presents the spatiotemporal BTM load and PV forecasting (ST-BTMLPVF) problem. The objective is to disaggregate the historical net loads of neighboring residential units into their BTM load and PV generation and forecast the future values of these unobservable time series. To solve ST-BTMLPVF, we model the units as a spatiotemporal graph (ST-graph) where the nodes represent the net load measurements of units and edges reflect the mutual correlation between the units. An ST-graph autoencoder (ST-GAE) is devised to capture the spatiotemporal manifold of the ST-graph, and a novel spatiotemporal graph dictionary learning (STGDL) optimization is proposed to utilize the latent features of the ST-GAE to find the most significant spatiotemporal features of the net load. STGDL utilizes the captured features to estimate the historical BTM load and PV measurements, which are further used by a deep recurrent structure to forecast the future values of BTM load and PV generation at each unit. Numerical experiments on a real-world load and PV data set show the state-of-the-art performance of the proposed model, both for the BTM disaggregation and forecasting tasks." @default.
• W3112294296 created "2020-12-21" @default.
• W3112294296 creator A5011279096 @default.
• W3112294296 creator A5019296796 @default.
• W3112294296 creator A5037009406 @default.
• W3112294296 creator A5066257732 @default.
• W3112294296 creator A5067435137 @default.
• W3112294296 date "2021-10-01" @default.
• W3112294296 modified "2023-10-15" @default.
• W3112294296 title "Spatiotemporal Behind-the-Meter Load and PV Power Forecasting via Deep Graph Dictionary Learning" @default.
• W3112294296 cites W1479651931 @default.
• W3112294296 cites W1985909556 @default.
• W3112294296 cites W2072990200 @default.
• W3112294296 cites W2080063509 @default.
• W3112294296 cites W2082868024 @default.
• W3112294296 cites W2232980359 @default.
• W3112294296 cites W2343217767 @default.
• W3112294296 cites W2408079821 @default.
• W3112294296 cites W2463113024 @default.
• W3112294296 cites W2564963919 @default.
• W3112294296 cites W2565341511 @default.
• W3112294296 cites W2585132294 @default.
• W3112294296 cites W2586240443 @default.
• W3112294296 cites W2601171548 @default.
• W3112294296 cites W2748388862 @default.
• W3112294296 cites W2750448831 @default.
• W3112294296 cites W2754252319 @default.
• W3112294296 cites W2754803257 @default.
• W3112294296 cites W2783331193 @default.
• W3112294296 cites W2786806645 @default.
• W3112294296 cites W2789937392 @default.
• W3112294296 cites W2790866531 @default.
• W3112294296 cites W2805797750 @default.
• W3112294296 cites W2807252330 @default.
• W3112294296 cites W2808611867 @default.
• W3112294296 cites W2894793845 @default.
• W3112294296 cites W2897481878 @default.
• W3112294296 cites W2902432432 @default.
• W3112294296 cites W2913651970 @default.
• W3112294296 cites W2913771521 @default.
• W3112294296 cites W2915409697 @default.
• W3112294296 cites W2920980323 @default.
• W3112294296 cites W2921485190 @default.
• W3112294296 cites W2922281988 @default.
• W3112294296 cites W2922297938 @default.
• W3112294296 cites W2939806510 @default.
• W3112294296 cites W2941407754 @default.
• W3112294296 cites W2943809873 @default.
• W3112294296 cites W2947934295 @default.
• W3112294296 cites W2952822670 @default.
• W3112294296 cites W2962717448 @default.
• W3112294296 cites W2963759574 @default.
• W3112294296 cites W2964250810 @default.
• W3112294296 cites W2964758013 @default.
• W3112294296 cites W2965206442 @default.
• W3112294296 cites W2965516457 @default.
• W3112294296 cites W2966167131 @default.
• W3112294296 cites W2972586903 @default.
• W3112294296 cites W2987189031 @default.
• W3112294296 cites W2990949310 @default.
• W3112294296 cites W2999656296 @default.
• W3112294296 cites W3000480515 @default.
• W3112294296 cites W3004606837 @default.
• W3112294296 cites W3023553797 @default.
• W3112294296 cites W3043263114 @default.
• W3112294296 cites W3098829776 @default.
• W3112294296 doi "https://doi.org/10.1109/tnnls.2020.3042434" @default.
• W3112294296 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/33326390" @default.
• W3112294296 hasPublicationYear "2021" @default.
• W3112294296 type Work @default.
• W3112294296 sameAs 3112294296 @default.
• W3112294296 citedByCount "24" @default.
• W3112294296 countsByYear W31122942962021 @default.
• W3112294296 countsByYear W31122942962022 @default.
• W3112294296 countsByYear W31122942962023 @default.
• W3112294296 crossrefType "journal-article" @default.
• W3112294296 hasAuthorship W3112294296A5011279096 @default.
• W3112294296 hasAuthorship W3112294296A5019296796 @default.
• W3112294296 hasAuthorship W3112294296A5037009406 @default.
• W3112294296 hasAuthorship W3112294296A5066257732 @default.
• W3112294296 hasAuthorship W3112294296A5067435137 @default.
• W3112294296 hasConcept C101738243 @default.
• W3112294296 hasConcept C108583219 @default.
• W3112294296 hasConcept C119599485 @default.
• W3112294296 hasConcept C124101348 @default.
• W3112294296 hasConcept C127413603 @default.
• W3112294296 hasConcept C132525143 @default.
• W3112294296 hasConcept C149782125 @default.
• W3112294296 hasConcept C154945302 @default.
• W3112294296 hasConcept C2780695315 @default.
• W3112294296 hasConcept C33923547 @default.
• W3112294296 hasConcept C41008148 @default.
• W3112294296 hasConcept C41291067 @default.
• W3112294296 hasConcept C79403827 @default.
• W3112294296 hasConcept C80444323 @default.
• W3112294296 hasConceptScore W3112294296C101738243 @default.
• W3112294296 hasConceptScore W3112294296C108583219 @default.
• W3112294296 hasConceptScore W3112294296C119599485 @default.

• next