On the public citation graph

Town Gas Daily Load Forecasting Based on Bp-Ceemdan-Gru: A Case Study in North China

https://doi.org/10.2139/ssrn.4677480
0 signed · 27 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 See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.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/s11356-022-25080-4openalex-extracted
Predicting the monthly consumption and production of natural gas in the USA by using a new hybrid forecasting model based on two-layer decomposition
inferredcites →https://doi.org/10.1016/0893-6080(88)90007-xopenalex-extracted
Generalization of backpropagation with application to a recurrent gas market model
inferredcites →https://doi.org/10.1016/j.apenergy.2019.114486openalex-extracted
Day-ahead high-resolution forecasting of natural gas demand and supply in Germany with a hybrid model
inferredcites →https://doi.org/10.1016/j.apenergy.2021.117623openalex-extracted
Day-ahead city natural gas load forecasting based on decomposition-fusion technique and diversified ensemble learning model
inferredcites →https://doi.org/10.1016/j.asoc.2017.01.022openalex-extracted
A new class of MODWT-SVM-DE hybrid model emphasizing on simplification structure in data pre-processing: A case study of annual electricity consumptions
inferredcites →https://doi.org/10.1016/j.asoc.2021.107079openalex-extracted
CELOF: Effective and fast memory efficient local outlier detection in high-dimensional data streams
inferredcites →https://doi.org/10.1016/j.egyr.2019.06.003openalex-extracted
Short-term load forecasting of urban gas using a hybrid model based on improved fruit fly optimization algorithm and support vector machine
inferredcites →https://doi.org/10.1016/j.enbuild.2012.11.010openalex-extracted
Multiple regression model for fast prediction of the heating energy demand
inferredcites →https://doi.org/10.1016/j.energy.2016.12.033openalex-extracted
Day-ahead natural gas demand forecasting based on the combination of wavelet transform and ANFIS/genetic algorithm/neural network model
inferredcites →https://doi.org/10.1016/j.energy.2019.04.167openalex-extracted
A hybrid hourly natural gas demand forecasting method based on the integration of wavelet transform and enhanced Deep-RNN model
inferredcites →https://doi.org/10.1016/j.energy.2020.119118openalex-extracted
Predicting monthly natural gas production in China using a novel grey seasonal model with particle swarm optimization
inferredcites →https://doi.org/10.1016/j.energy.2021.121036openalex-extracted
Forecasting the daily natural gas consumption with an accurate white-box model
inferredcites →https://doi.org/10.1016/j.energy.2022.124993openalex-extracted
Multi-step ahead forecasting of daily urban gas load in Chengdu using a Tanimoto kernel-based NAR model and Whale optimization
inferredcites →https://doi.org/10.1016/j.epsr.2021.107746openalex-extracted
Short-term load forecasting considering improved cumulative effect of hourly temperature
inferredcites →https://doi.org/10.1016/j.eswa.2022.117252openalex-extracted
Modal decomposition-based hybrid model for stock index prediction
inferredcites →https://doi.org/10.1016/j.jngse.2021.104175openalex-extracted
Daily natural gas load forecasting based on the combination of long short term memory, local mean decomposition, and wavelet threshold denoising algorithm
inferredcites →https://doi.org/10.1016/j.strueco.2021.01.005openalex-extracted
Institutional constraints to China's low carbon transition: A case study of China's coal-to-gas program
inferredcites →https://doi.org/10.1016/s1876-3804(23)60404-5openalex-extracted
Forecast of natural gas supply and demand in China under the background of “Dual Carbon Targets”
inferredcites →https://doi.org/10.1080/00908310490256626openalex-extracted
Artificial Neural Network Modeling for Forecasting Gas Consumption
inferredcites →https://doi.org/10.1098/rspa.1998.0193openalex-extracted
The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis
inferredcites →https://doi.org/10.1109/access.2019.2918156openalex-extracted
A Novel Hybrid Prediction Model for Hourly Gas Consumption in Supply Side Based on Improved Whale Optimization Algorithm and Relevance Vector Machine
inferredcites →https://doi.org/10.1109/icassp.2011.5947265openalex-extracted
A complete ensemble empirical mode decomposition with adaptive noise
inferredcites →https://doi.org/10.1109/ickii55100.2022.9983572openalex-extracted
Natural Gas Load Forecasting Based on Improved Genetic Algorithm and BP Neural Network
inferredcites →https://doi.org/10.1126/science.109.2823.103openalex-extracted
Energy from Fossil Fuels
inferredcites →https://doi.org/10.1287/mnsc.20.4.604openalex-extracted
Estimation of Rural Demand for Natural Gas
inferredcites →https://doi.org/10.3390/en11071625openalex-extracted
Combining Grey Model and Self-Adapting Intelligent Grey Model with Genetic Algorithm and Annual Share Changes in Natural Gas Demand Forecasting
inferredcites →https://doi.org/10.3390/en11082008openalex-extracted
Short-Term Load Forecasting of Natural Gas with Deep Neural Network Regression †
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.