On the public citation graph

Power Distribution Networks Load Forecasting Using Deep Belief Networks: The South African Case

https://doi.org/10.1109/jeeit.2019.8717434
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.
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 See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.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/10.1007/0-387-23471-3_12openalex-snapshot
Load Forecasting
inferredcites →https://doi.org/10.1109/access.2014.2325029openalex-snapshot
Big Data Deep Learning: Challenges and Perspectives
inferredcites →https://doi.org/10.1109/afrcon.1999.821906openalex-snapshot
Improved load forecasting techniques for the newly electrified areas
inferredcites →https://doi.org/10.1109/epec.2014.45openalex-snapshot
Performance Evaluation of New and Advanced Neural Networks for Short Term Load Forecasting
inferredcites →https://doi.org/10.1109/iccnc.2017.7876196openalex-snapshot
Short term power load forecasting using Deep Neural Networks
inferredcites →https://doi.org/10.1109/iciip.2017.8313796openalex-snapshot
Soft biometric thermal face recognition using FWT and LDA feature extraction method with RBM DBN and FFNN classifier algorithms
inferredcites →https://doi.org/10.1109/idap.2017.8090344openalex-snapshot
Randomized feed-forward artificial neural networks in estimating short-term power load of a small house: A case study
inferredcites →https://doi.org/10.1109/ihmsc.2017.50openalex-snapshot
Short-Term Power Load Forecasting with Deep Belief Network and Copula Models
inferredcites →https://doi.org/10.1109/ijcnn.2014.6889898openalex-snapshot
Forecasting electricity consumption in South Africa: ARMA, neural networks and neuro-fuzzy systems
inferredcites →https://doi.org/10.1109/ijcnn.2017.7966378openalex-snapshot
Improving load forecasting based on deep learning and K-shape clustering
inferredcites →https://doi.org/10.1109/ijcnn.2017.7966398openalex-snapshot
Monthly energy consumption forecast: A deep learning approach
inferredcites →https://doi.org/10.1109/isgt.2017.8085971openalex-snapshot
Load forecasting using deep neural networks
inferredcites →https://doi.org/10.1109/lsp.2018.2871419openalex-snapshot
A Deep Learning Loss Function Based on the Perceptual Evaluation of the Speech Quality
inferredcites →https://doi.org/10.1109/mis.2017.23openalex-snapshot
Deep Learning-Based Document Modeling for Personality Detection from Text
inferredcites →https://doi.org/10.1109/peci.2018.8334980openalex-snapshot
Long term forecasting using machine learning methods
inferredcites →https://doi.org/10.1109/pedes.2018.8707876openalex-snapshot
South African Distribution Networks Load Forecasting Using ANFIS
inferredcites →https://doi.org/10.1109/smc.2013.383openalex-snapshot
Univariate Modelling of Electricity Consumption in South Africa: Neural Networks and Neuro-fuzzy Systems
inferredcites →https://doi.org/10.1109/smc.2017.8123012openalex-snapshot
Long short term memory networks for short-term electric load forecasting
inferredcites →https://doi.org/10.1109/tmi.2016.2528120openalex-snapshot
AggNet: Deep Learning From Crowds for Mitosis Detection in Breast Cancer Histology Images
inferredcites →https://doi.org/10.1109/tpwrd.2011.2166566openalex-snapshot
Short-Term Multinodal Load Forecasting Using a Modified General Regression Neural Network
inferredcites →https://doi.org/10.1109/tpwrs.2006.883666openalex-snapshot
Monthly Electric Energy Demand Forecasting Based on Trend Extraction
inferredcites →https://doi.org/10.1109/tpwrs.2007.908438openalex-snapshot
Toward Estimating Autonomous Neural Network-Based Electric Load Forecasters
inferredcites →https://doi.org/10.1109/tpwrs.2009.2030426openalex-snapshot
Short-Term Load Forecasting: Similar Day-Based Wavelet Neural Networks
inferredcites →https://doi.org/10.1109/tpwrs.2015.2390132openalex-snapshot
Neural Network-Based Model Design for Short-Term Load Forecast in Distribution Systems
inferredcites →https://doi.org/10.1109/tpwrs.2017.2688178openalex-snapshot
Short-Term Residential Load Forecasting Based on Resident Behaviour Learning
inferredcites →https://doi.org/10.1162/neco.2006.18.7.1527openalex-snapshot
A Fast Learning Algorithm for Deep Belief Nets
inferredcites →https://doi.org/10.48550/arxiv.1702.01923openalex-snapshot
Comparative Study of CNN and RNN for Natural Language Processing

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.3998847openalex-extracted
Deep-Learning Framework for Forecasting Renewable Electricity Demands with Variational Auto-Encoder and Bidirectional Long Short-Term Memory
inferred← citeshttps://doi.org/10.1016/j.enbuild.2023.113328openalex-snapshot
Cost-optimal dimensioning and operation of a solar PV–BESS–heat pump-based on-grid energy system for a Nordic climate townhouse
inferred← citeshttps://doi.org/10.1016/j.rser.2021.110992openalex-snapshot
A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids
inferred← citeshttps://doi.org/10.1109/access.2019.2923796openalex-snapshot
Improving Load Forecasting Process for a Power Distribution Network Using Hybrid AI and Deep Learning Algorithms
inferred← citeshttps://doi.org/10.1109/access.2025.3526943openalex-snapshot
Attention-Driven Hybrid Ensemble Approach With Bayesian Optimization for Accurate Energy Forecasting in Jeju Island’s Renewable Energy System
inferred← citeshttps://doi.org/10.1109/acemp-optim44294.2019.9007211openalex-snapshot
Using Deep Learning Techniques for South African Power Distribution Networks Load Forecasting
inferred← citeshttps://doi.org/10.1109/icais56108.2023.10073714openalex-snapshot
Energy Load Forecasting with Demand Side Management in Industrial Sectors using Neuro-Fuzzy Controller and Deep Learning
inferred← citeshttps://doi.org/10.1109/ispec53008.2021.9735686openalex-snapshot
A Hybrid Model for Building-level Day Ahead Power Load Forecasting Based on Gated Recurrent Unit Networks
inferred← citeshttps://doi.org/10.1109/pesgm41954.2020.9282124openalex-snapshot
Forecasting Electric Load by Aggregating Meteorological and History-based Deep Learning Modules
inferred← citeshttps://doi.org/10.1109/pset66242.2025.11296462openalex-snapshot
Addressing corona-effect for power networks in South Africa using image classification and Machine Learning
inferred← citeshttps://doi.org/10.1109/saupec65723.2025.10944418openalex-snapshot
Distribution Network Reconfiguration via Metaheuristic Optimization Techniques
inferred← citeshttps://doi.org/10.1109/sdpc62810.2024.10707751openalex-snapshot
Fault Prediction of DC-DC Devices Based on Deep Belief Network
inferred← citeshttps://doi.org/10.1109/tpwrs.2021.3128428openalex-snapshot
Topology Detection in Power Distribution Networks: A PMU Based Deep Learning Approach
inferred← citeshttps://doi.org/10.2139/ssrn.4646481openalex-snapshot
Empowering Distribution System Operators: A Review of Distributed Energy Resource Forecasting Techniques
inferred← citeshttps://doi.org/10.22214/ijraset.2022.47392openalex-snapshot
A Systematic Review and Taxonomy on Electrical Load Forecasting for Optimizing Power Generation
inferred← citeshttps://doi.org/10.3233/jifs-190658openalex-snapshot
Effective load forecasting for large power consuming industrial customers using long short-term memory recurrent neural networks
inferred← citeshttps://doi.org/10.3390/app13074462openalex-snapshot
Short-Term Power Load Forecasting Based on an EPT-VMD-TCN-TPA Model
inferred← citeshttps://doi.org/10.3390/en15072546openalex-snapshot
Forecasting the Total South African Unplanned Capability Loss Factor Using an Ensemble of Deep Learning Techniques
inferred← citeshttps://doi.org/10.3390/en17112658openalex-snapshot
A Combined Model of Convolutional Neural Network and Bidirectional Long Short-Term Memory with Attention Mechanism for Load Harmonics Forecasting
inferred← citeshttps://doi.org/10.3390/en18164285openalex-snapshot
Hybrid Forecasting for Energy Consumption in South Africa: LSTM and XGBoost Approach
inferred← citeshttps://doi.org/10.46604/aiti.2022.9227openalex-snapshot
Effects of Data Standardization on Hyperparameter Optimization with the Grid Search Algorithm Based on Deep Learning: A Case Study of Electric Load Forecasting
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.