On the public citation graph
Analysis and Modeling for China’s Electricity Demand Forecasting Based on a New Mathematical Hybrid Method
0 signed · 48 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.
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known work and none has been retracted — and get a dated CiteStamped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.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/(sici)1099-131x(199601)15:1<49::aid-for604>3.0.co;2-2openalex-snapshot
Forecast combining with neural networks
inferredcites →https://doi.org/10.1002/for.3980080302openalex-snapshot
Forecasting with more than one model
inferredcites →https://doi.org/10.1002/for.3980080303openalex-snapshot
Invited review combining forecasts—twenty years later
inferredcites →https://doi.org/10.1016/0165-0114(93)90194-mopenalex-snapshot
Families of OWA operators
inferredcites →https://doi.org/10.1016/j.enbuild.2015.09.033openalex-snapshot
A hybrid model approach for forecasting future residential electricity consumption
inferredcites →https://doi.org/10.1016/j.enbuild.2016.05.073openalex-snapshot
Forecasting long-term electricity demand for cooling of Singapore’s buildings incorporating an innovative air-conditioning technology
inferredcites →https://doi.org/10.1016/j.energy.2009.10.018openalex-snapshot
Greek long-term energy consumption prediction using artificial neural networks
inferredcites →https://doi.org/10.1016/j.energy.2015.11.055openalex-snapshot
Analysis and long term forecasting of electricity demand trough a decomposition model: A case study for Spain
inferredcites →https://doi.org/10.1016/j.energy.2016.02.120openalex-snapshot
Climate change and electricity demand in Brazil: A stochastic approach
inferredcites →https://doi.org/10.1016/j.rser.2014.07.098openalex-snapshot
Benefits and challenges of electrical demand response: A critical review
inferredcites →https://doi.org/10.1016/j.rser.2015.03.035openalex-snapshot
Regression analysis for prediction of residential energy consumption
inferredcites →https://doi.org/10.1016/j.rser.2015.07.102openalex-snapshot
An analysis of the current and future use of natural gas-fired power plants in meeting electricity energy needs: The case of Turkey
inferredcites →https://doi.org/10.1016/j.seps.2015.12.002openalex-snapshot
Long term electricity consumption forecast in Brazil: A fuzzy logic approach
inferredcites →https://doi.org/10.1016/j.techfore.2015.09.001openalex-snapshot
Bounding US electricity demand in 2050
inferredcites →https://doi.org/10.1016/s0165-0114(96)00254-0openalex-snapshot
On the issue of obtaining OWA operator weights
inferredcites →https://doi.org/10.1109/21.87068openalex-snapshot
On ordered weighted averaging aggregation operators in multicriteria decisionmaking
inferredcites →https://doi.org/10.1186/s40064-016-1665-zopenalex-snapshot
Towards smart energy systems: application of kernel machine regression for medium term electricity load forecasting
inferredcites →https://doi.org/10.1631/jzus.a0820042openalex-snapshot
Adaptive load forecasting of the Hellenic electric grid
inferredcites →https://doi.org/10.3390/app6010020openalex-snapshot
Using GM (1,1) Optimized by MFO with Rolling Mechanism to Forecast the Electricity Consumption of Inner Mongolia
inferredcites →https://doi.org/10.3390/en4081246openalex-snapshot
Forecasting Electricity Demand in Thailand with an Artificial Neural Network Approach
inferredcites →https://doi.org/10.3390/en5125215openalex-snapshot
Classification and Clustering of Electricity Demand Patterns in Industrial Parks
inferredcites →https://doi.org/10.3390/su70912127openalex-snapshot
Bayesian Analysis of Demand Elasticity in the Italian Electricity Market
inferredcites →https://doi.org/10.3390/su8010052openalex-snapshot
Describing Long-Term Electricity Demand Scenarios in the Telecommunications Industry: A Case Study of Japan
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.2139/ssrn.4819934openalex-extracted
Future of Residential Electricity Demand in Saudi Arabia and the Role of Energy Efficiency in 2060's Net Zero Pledge
inferred← citeshttps://doi.org/10.1016/j.dsef.2025.100086openalex-snapshot
Future of residential electricity demand in Saudi Arabia and the role of energy efficiency in 2060′s net zero pledge
inferred← citeshttps://doi.org/10.1016/j.energy.2021.119869openalex-snapshot
An effective rolling decomposition-ensemble model for gasoline consumption forecasting
inferred← citeshttps://doi.org/10.1016/j.energy.2023.127365openalex-snapshot
Sensitivity analysis for forecasting Brazilian electricity demand using artificial neural networks and hybrid models based on Autoregressive Integrated Moving Average
inferred← citeshttps://doi.org/10.1016/j.epsr.2021.107604openalex-snapshot
Saturated load forecasting based on clustering and logistic iterative regression
inferred← citeshttps://doi.org/10.1016/j.epsr.2024.110953openalex-snapshot
Saturated load forecasting based on improved logistic regression and affinity propagation
inferred← citeshttps://doi.org/10.1016/j.procs.2019.11.252openalex-snapshot
Ensemble forecasting for electricity consumption based on nonlinear optimization
inferred← citeshttps://doi.org/10.1016/j.solener.2018.08.088openalex-snapshot
Deploying effectively dispatchable PV on reservoirs: Comparing floating PV to other renewable technologies
inferred← citeshttps://doi.org/10.1029/2021gh000555openalex-snapshot
Human Activity Changes During COVID‐19 Lockdown in China—A View From Nighttime Light
inferred← citeshttps://doi.org/10.1063/1.5045172openalex-snapshot
A hybrid model for electricity price forecasting based on least square support vector machines with combined kernel
inferred← citeshttps://doi.org/10.1109/icimu49871.2020.9243578openalex-snapshot
Energy Usage Prediction for Smart Home with Regression Based Ensemble Model
inferred← citeshttps://doi.org/10.1109/senze66459.2025.11428673openalex-snapshot
Residential Demand and Efficiency in Saudi Arabia's Net Zero Journey
inferred← citeshttps://doi.org/10.30657/pea.2023.29.9openalex-snapshot
Supporting of manufacturer’s demand plans as an element of logistics coordination in the distribution network
inferred← citeshttps://doi.org/10.3390/books978-3-03921-458-7openalex-snapshot
Selected Papers from the 8th Annual Conference of Energy Economics and Management
inferred← citeshttps://doi.org/10.3390/e22121412openalex-snapshot
A Systematic Review of Statistical and Machine Learning Methods for Electrical Power Forecasting with Reported MAPE Score
inferred← citeshttps://doi.org/10.3390/en11010106openalex-snapshot
The Recurrence Interval Difference of Power Load in Heavy/Light Industries of China
inferred← citeshttps://doi.org/10.3390/en13061407openalex-snapshot
Towards Assessing the Electricity Demand in Brazil: Data-Driven Analysis and Ensemble Learning Models
inferred← citeshttps://doi.org/10.3390/en13153996openalex-snapshot
Long-Term Electricity Demand Prediction via Socioeconomic Factors—A Machine Learning Approach with Florida as a Case Study
inferred← citeshttps://doi.org/10.3390/en14102749openalex-snapshot
How to Predict Energy Consumption in BRICS Countries?
inferred← citeshttps://doi.org/10.3390/en14237859openalex-snapshot
Modeling Energy Demand—A Systematic Literature Review
inferred← citeshttps://doi.org/10.3390/en16010506openalex-snapshot
Predicting Electricity Consumption in the Kingdom of Saudi Arabia
inferred← citeshttps://doi.org/10.3390/info9030051openalex-snapshot
Forecasting Monthly Electricity Demands by Wavelet Neuro-Fuzzy System Optimized by Heuristic Algorithms
inferred← citeshttps://doi.org/10.3390/info9080204openalex-snapshot
Forecasting Electricity Consumption Using an Improved Grey Prediction Model
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.