On the public citation graph

An Operational Open-Data Digital Twin for Power Systems: Hybrid Forecasting, Data Quality Integration, and Real-Time Bidirectional Feedback

https://doi.org/10.2139/ssrn.6477797
0 signed · 15 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 See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.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/10.1007/s10207-023-00784-xopenalex-snapshot
A review on digital twins for power generation and distribution
inferredcites →https://doi.org/10.1016/j.adapen.2020.100004openalex-snapshot
Theory-guided deep-learning for electrical load forecasting (TgDLF) via ensemble long short-term memory
inferredcites →https://doi.org/10.1016/j.apenergy.2018.11.097openalex-snapshot
Open Power System Data – Frictionless data for electricity system modelling
inferredcites →https://doi.org/10.1016/j.apenergy.2021.116983openalex-snapshot
Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark
inferredcites →https://doi.org/10.1016/j.enconman.2020.112766openalex-snapshot
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.ijforecast.2015.11.011openalex-snapshot
Probabilistic electric load forecasting: A tutorial review
inferredcites →https://doi.org/10.1049/dgt2.12020openalex-snapshot
On future power system digital twins: A vision towards a standard architecture
inferredcites →https://doi.org/10.1109/iotm.0001.1800022openalex-snapshot
A Standard-Based Open Source IoT Platform: FIWARE
inferredcites →https://doi.org/10.1109/tpwrd.2020.3017355openalex-snapshot
Digital Twin of Distribution Power Transformer for Real-Time Monitoring of Medium Voltage From Low Voltage Measurements
inferredcites →https://doi.org/10.1145/3514228openalex-snapshot
Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems
inferredcites →https://doi.org/10.1201/9781003143376-15openalex-snapshot
Physics-Guided Neural Networks (PGNN): An Application in Lake Temperature Modeling
inferredcites →https://doi.org/10.3390/app112110126openalex-snapshot
Deep Learning for Short-Term Load Forecasting—Industrial Consumer Case Study
inferredcites →https://doi.org/10.3390/en18010008openalex-snapshot
Physics-Informed Neural Networks for Heat Pump Load Prediction
inferredcites →https://doi.org/10.3390/su15065259openalex-snapshot
Digital Twins for the Future Power System: An Overview and a Future Perspective
inferredcites →https://doi.org/10.5194/essd-13-2259-2021openalex-snapshot
Sub-seasonal forecasts of demand and wind power and solar power generation for 28 European countries
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.