Spatiotemporal Sequence-to-Sequence Clustering for Electric Load Forecasting
Author:
Affiliation:
1. Department of Electrical Energy Engineering, Keimyung University, Daegu, South Korea
2. Department of Electrical and Electronic Engineering, Kyungpook National University, Daegu, South Korea
Funder
Project Open Innovation Research and Development
K-water
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/6287639/10005208/10013668.pdf?arnumber=10013668
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1. Trendlets: A novel probabilistic representational structures for clustering the time series data;johnpaul;Expert Syst With Appl,2020
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4. Modeling public holidays in load forecasting: a German case study
5. Power System Short Term Load Forecasting Based on Weather Factors
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