On the public citation graph

Distributed Photovoltaic Short-Term Power Prediction Based on Meteorological Resource Clustering and Federated Learning

https://doi.org/10.2139/ssrn.4811404
0 signed · 13 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1016/j.enconman.2020.112766openalex-extracted
A day-ahead PV power forecasting method based on LSTM-RNN model and time correlation modification under partial daily pattern prediction framework
inferredcites →https://doi.org/10.1016/j.enconman.2020.113085openalex-extracted
Improved quantile convolutional neural network with two-stage training for daily-ahead probabilistic forecasting of photovoltaic power
inferredcites →https://doi.org/10.1016/j.jclepro.2020.119966openalex-extracted
An improved moth-flame optimization algorithm for support vector machine prediction of photovoltaic power generation
inferredcites →https://doi.org/10.1016/j.renene.2019.03.020openalex-extracted
A SARIMA-RVFL hybrid model assisted by wavelet decomposition for very short-term solar PV power generation forecast
inferredcites →https://doi.org/10.1016/j.solener.2020.10.024openalex-extracted
Site-specific adjustment of a NWP-based photovoltaic production forecast
inferredcites →https://doi.org/10.1109/tasc.2021.3117750openalex-extracted
Cooperative Dispatch of Distributed Energy Storage in Distribution Network With PV Generation Systems
inferredcites →https://doi.org/10.1109/tim.2023.3244227openalex-extracted
A Novel Transmission Line Defect Detection Method Based on Adaptive Federated Learning
inferredcites →https://doi.org/10.1109/tpds.2021.3098467openalex-extracted
Multi-Task Federated Learning for Personalised Deep Neural Networks in Edge Computing
inferredcites →https://doi.org/10.1109/tste.2021.3104656openalex-extracted
A Multi-Data Driven Hybrid Learning Method for Weekly Photovoltaic Power Scenario Forecast
inferredcites →https://doi.org/10.3390/electronics12030730openalex-extracted
Novel PV Power Hybrid Prediction Model Based on FL Co-Training Method
inferredcites →https://doi.org/10.3390/en11102725openalex-extracted
Extreme Learning Machines for Solar Photovoltaic Power Predictions
inferredcites →https://doi.org/10.48550/arxiv.1602.05629openalex-extracted
Communication-Efficient Learning of Deep Networks from Decentralized Data
inferredcites →https://doi.org/10.48550/arxiv.2007.09236openalex-extracted
Multi-Task Federated Learning for Personalised Deep Neural Networks in Edge Computing
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.