Article

Short-Term Load Prediction for Medium-Voltage Electricity Networks Using Machine Learning: A Comparative Study

2026-01-13

Abstract excerpt

The rising uncertainties in electric load behaviour owing to human, technological and so-cio-economic events present a need to improve the accuracy and efficiency of current short-term load prediction (STLP) models. This paper compares the performance of four hybrid models for short-term Amp load prediction: Adaptive Neuro-Fuzzy Inference Sys-tem (ANFIS) integrated with Genetic Algorithm (GA) and Particle Swarm Op...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: a2fc6616-a68a-5148-9305-30f43c752433
DOI: 10.20944/preprints202601.0837.v1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Short-Term Load Prediction for Medium-Voltage Electricity Networks Using Machine Learning: A Comparative StudyDOI 10.20944/preprints202601.0837.v1Read

Impact of the COVID-19 Lockdown on the Electricity System of Great Britain: A Study on Energy Demand, Generation, Pricing and Grid StabilitySemantic proximity Read

Bayesian belief network modelling of the challenges associated with hybrid solar-diesel electricity from the end users’ perspective in Bugala Island in UgandaSemantic proximity Read

Artificial Intelligence Applications in Modern Power Systems: Load Forecasting, Fault Detection, Predictive Maintenance, Grid Optimization, Renewable Integration, and Stability AssessmentSemantic proximity Read

Conventional time series, machine and deep learning approaches for rainfall forecasting in a bimodal climate in Embu County, Kenya: A comparative study of SARIMA, ANN, LSTM, CNN and hybrid modelsSemantic proximity Read

Nonlinear Optimization of Recurrent Neural Networks in the Prediction of Air QualitySemantic proximity Read

Energy consumption prediction using the GRU-MMattention-LightGBM model with features of Prophet decomposition.Semantic proximity Read

Interdependencies between electricity consumption and transit and their potential to forecast transit demand in urban settingsSemantic proximity Read

Machine Learning for Lithium-Ion Battery Voltage Modeling: Insights from NARX-RNN, Deep Koopman, and LSTM-NARXSemantic proximity Read

Forecasting the Fuel Consumption and Price for Future Pandemic Outbreak: A case study in the USA under COVID-19Semantic proximity Read

PM2.5 concentration prediction using weighted CEEMDAN and improved LSTM neural network.Semantic proximity Read

Forecasting CO2 Emissions Due To Electricity Power Generation In BahrainSemantic proximity Read

Crested Porcupine Optimized Long Short-Term Memory based Multi Fault Diagnosis of Wind TurbinesSemantic proximity Read