• 대한전기학회
Mobile QR Code QR CODE : The Transactions of the Korean Institute of Electrical Engineers
  • COPE
  • kcse
  • 한국과학기술단체총연합회
  • 한국학술지인용색인
  • Scopus
  • crossref
  • orcid

References

1 
Chen Biaowei, Dec 2020, Very-Short-Term Power Prediction for PV Power Plants Using a Simple and Effective RCCLSTM Model Based on Short Term Multivariate Historical DatasetsDOI
2 
Seul-Gi Kim, Mar 2019, A Two-Step Approach to Solar Power Generation Prediction Based on Weather Data Using Machine LearningDOI
3 
M. Rizwan, Sep 2017, Generalized Neural Network Approach for Global Solar Energy Estimation in India, [2012.07] Maria Malvon et al., “Forecasting of PV Power Generation using weather input data‐preprocessing techniques”DOI
4 
Rodríguez Fermín, Mar 2018, Predicting solar energy generation through artificial neural networks using weather forecasts for microgrid controlDOI
5 
Ramaswamy Swaroop, 2017, Forecasting PV Power from Solar Irradiance and Temperature using Neural NetworksDOI
6 
Akylas C. Stratigakos, Oct 2019, A Suitable Flexibility Assessment Approach for the Pre-Screening Phase of Power System Planning Applied on the Greek Power SystemDOI
7 
Soteris A. Kalogirou, 2014, Solar Energy EngineeringGoogle Search
8 
Korea Meteorological Administration, Mar 2021, https://data.kma.go.kr/cmmn/main.doGoogle Search
9 
Public Data Portal, 2021, https://www.data.go.kr/data/15065368/fileData.doGoogle Search
10 
The Ministry of Trade, Industry and Energy, Dec 2020, The 9th Basic Plan for Long-Term Electricity Supply and Demand (2020–2034)Google Search
11 
National Statistics Portal, 2021, https://kosis.krGoogle Search
12 
Korea Energy Economics Institute, 2015, Analysis of Efficient Power Supply and Demand Impact of Differential Rate System by RegionGoogle Search