Learning Vector Quantization based predictor model selection for hourly load demand forecasting-Reference-Cited by-同舟云学术

Learning Vector Quantization based predictor model selection for hourly load demand forecasting

Published:2022-03 Issue: Volume:117 Page:108421
ISSN:1568-4946
Container-title:Applied Soft Computing
language:en
Short-container-title:Applied Soft Computing

Author:

Akarslan Emre

Publisher

Elsevier BV

Subject

Software

Reference40 articles.

1. A novel approach for residential load appliance identification;Akarslan;Sustain. Cities Soc.,2020

2. A short-term power load forecasting model based on the generalized regression neural network with decreasing step fruit fly optimization algorithm;Hu;Neurocomputing,2017

3. A new hybrid model based on data preprocessing and an intelligent optimization algorithm for electrical power system forecasting;Jiang;Math. Probl. Eng.,2015

4. A novel fuzzy-based ensemble model for load forecasting using hybrid deep neural networks;Sideratos;Electr. Power Syst. Res.,2020

5. A regional hybrid GOA-SVM model based on similar day approach for short-term load forecasting in Assam, India;Barman;Energy,2018

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