Clustering based day-ahead and hour-ahead bus load forecasting models-Reference-Cited by-同舟云学术

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Clustering based day-ahead and hour-ahead bus load forecasting models

Published:2016-09 Issue: Volume:80 Page:171-178
ISSN:0142-0615
Container-title:International Journal of Electrical Power & Energy Systems
language:en
Short-container-title:International Journal of Electrical Power & Energy Systems

Author:

Panapakidis Ioannis P.

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Energy Engineering and Power Technology

Reference30 articles.

1. Electric load forecasting methods: tools for decision making;Hahn;Euro J Oper Res,2009

2. Market operations in electric power systems: forecasting, scheduling, and risk management;Shahidehpour,2002

3. Electrical load forecasting: modeling and model construction;Soliman,2010

4. Neural networks for short-term load forecasting: a review and evaluation;Hippert;IEEE Trans Power Sys,2001

5. Load forecasting;Feinberg,2005

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1. An Overview of Short-Term Load Forecasting for Electricity Systems Operational Planning: Machine Learning Methods and the Brazilian Experience;Energies;2023-11-04

2. Short-term load forecasting using neural networks and global climate models: An application to a large-scale electrical power system;Applied Energy;2023-10

3. Using Clustering To Reduce Models Required For Medium Term Load Forecasting;2023 International Aegean Conference on Electrical Machines and Power Electronics (ACEMP) & 2023 International Conference on Optimization of Electrical and Electronic Equipment (OPTIM);2023-09-01

4. Short-Term Load Forecasting of the Greek Electricity System;Applied Sciences;2023-02-20

5. Gated spatial-temporal graph neural network based short-term load forecasting for wide-area multiple buses;International Journal of Electrical Power & Energy Systems;2023-02

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