On the public citation graph

BiCAL-LLM: A Bidirectional Cross-Modal LLM for Regional Building-Cluster Electric Load Forecasting

https://doi.org/10.2139/ssrn.7506437
0 signed · 23 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 See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.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/s12273-022-0887-yopenalex-snapshot
Daily power demand prediction for buildings at a large scale using a hybrid of physics-based model and generative adversarial network
inferredcites →https://doi.org/10.1016/j.apenergy.2010.12.005openalex-snapshot
Modeling and forecasting of Turkey’s energy consumption using socio-economic and demographic variables
inferredcites →https://doi.org/10.1016/j.apenergy.2017.09.060openalex-snapshot
Machine learning approaches for estimating commercial building energy consumption
inferredcites →https://doi.org/10.1016/j.apenergy.2024.124973openalex-snapshot
TimeGPT in load forecasting: A large time series model perspective
inferredcites →https://doi.org/10.1016/j.asoc.2023.110629openalex-snapshot
A short-term residential load forecasting scheme based on the multiple correlation-temporal graph neural networks
inferredcites →https://doi.org/10.1016/j.buildenv.2015.12.001openalex-snapshot
Urban building energy modeling – A review of a nascent field
inferredcites →https://doi.org/10.1016/j.egyr.2024.08.035openalex-snapshot
Research and application of short-term load forecasting based on CEEMDAN-LSTM modeling
inferredcites →https://doi.org/10.1016/j.egyr.2024.11.057openalex-snapshot
Probabilistic load forecasting for integrated energy systems based on quantile regression patch time series Transformer
inferredcites →https://doi.org/10.1016/j.enbenv.2023.07.003openalex-snapshot
Time series analysis model for forecasting unsteady electric load in buildings
inferredcites →https://doi.org/10.1016/j.enbuild.2014.07.036openalex-snapshot
Modeling heating and cooling loads by artificial intelligence for energy-efficient building design
inferredcites →https://doi.org/10.1016/j.ijforecast.2021.03.012openalex-snapshot
Temporal Fusion Transformers for interpretable multi-horizon time series forecasting
inferredcites →https://doi.org/10.1016/j.renene.2025.123313openalex-snapshot
EnergyGPT: Fine-tuning large language model for multi-energy load forecasting
inferredcites →https://doi.org/10.1016/j.rser.2008.09.033openalex-snapshot
Modeling of end-use energy consumption in the residential sector: A review of modeling techniques
inferredcites →https://doi.org/10.1016/j.rser.2012.02.049openalex-snapshot
A review on the prediction of building energy consumption
inferredcites →https://doi.org/10.1016/j.rser.2017.04.095openalex-snapshot
A review of data-driven building energy consumption prediction studies
inferredcites →https://doi.org/10.1016/j.scs.2017.08.009openalex-snapshot
Electrical load forecasting models: A critical systematic review
inferredcites →https://doi.org/10.1016/s0378-7788(00)00114-6openalex-snapshot
EnergyPlus: creating a new-generation building energy simulation program
inferredcites →https://doi.org/10.1023/a:1010933404324openalex-snapshot
Random Forests
inferredcites →https://doi.org/10.1109/tkde.2023.3342137openalex-snapshot
PromptCast: A New Prompt-Based Learning Paradigm for Time Series Forecasting
inferredcites →https://doi.org/10.1109/tpwrs.1987.4335210openalex-snapshot
The Time Series Approach to Short Term Load Forecasting
inferredcites →https://doi.org/10.1145/3719207openalex-snapshot
LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters
inferredcites →https://doi.org/10.3389/frai.2020.00004openalex-snapshot
An Introductory Review of Deep Learning for Prediction Models With Big Data
inferredcites →https://doi.org/10.52202/075280-0861openalex-snapshot
Large Language Models Are Zero-Shot Time Series Forecasters
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.