DOI QR코드

DOI QR Code

The Development of the Short-Term Predict Model for Solar Power Generation

태양광발전 단기예측모델 개발

  • Kim, Kwang-Deuk (New and Renewable Research Division, Korea Institute of Energy Research)
  • 김광득 (한국에너지기술연구원 신재생에너지 연구본부)
  • Received : 2013.10.29
  • Accepted : 2013.12.19
  • Published : 2013.12.30

Abstract

In this paper, Korea Institute of Energy Research, building integrated renewable energy monitoring system that utilizes solar power generation forecast data forecast model is proposed. Renewable energy integration of real-time monitoring system based on monitoring data were building a database and the database of the weather conditions and to study the correlation structure was tailoring. The weather forecast cloud cover data, generation data, and solar radiation data, a data mining and time series analysis using the method developed models to forecast solar power. The development of solar power in order to forecast model of weather forecast data it is important to secure. To this end, in three hours, including a three-day forecast today Meteorological data were used from the KMA(korea Meteorological Administration) site offers. In order to verify the accuracy of the predicted solar circle for each prediction and the actual environment can be applied to generation and were analyzed.

Keywords

References

  1. Kim,Yun Jai, "Study on the Application of Next-Generation Satellite Data(III)", Research Report KMA, 2007.
  2. http://www.mmm.ucar.edu
  3. Chang Gu Kang, "Compare Analysis for Time Series Forecasting Methods", Quarterly National Account, Vol.3 No.26, pp.80-105, 2006.
  4. Sung Duck Lee etal. "Kalman-Filter Estimation and Prediction for a Spatial Time Series Model", CSAM(Communications for Statistical Applications and Methods), Vol.18 No.1, pp.78-87, 2011. https://doi.org/10.5351/CKSS.2011.18.1.079
  5. Sung Duck Lee etal, "A Comparison on Forecasting Performance of STARMA and STBL Models with Application to Mumps Data", The Korean Journal of Applied Statistics, Vol. 20 No.1, pp.91-102, 2007. https://doi.org/10.5351/KJAS.2007.20.1.091

Cited by

  1. Modeling of Photovoltaic Power Systems using Clustering Algorithm and Modular Networks vol.65, pp.2, 2016, https://doi.org/10.5370/KIEEP.2016.65.2.108
  2. Development of PV Power Prediction Algorithm using Adaptive Neuro-Fuzzy Model vol.64, pp.4, 2015, https://doi.org/10.5370/KIEEP.2015.64.4.246
  3. Development of Fault Diagnosis Algorithm using Correlation Analysis and ELM vol.65, pp.3, 2016, https://doi.org/10.5370/KIEEP.2016.65.3.204
  4. Development of Daily PV Power Forecasting Models using ELM vol.64, pp.3, 2015, https://doi.org/10.5370/KIEEP.2015.64.3.164
  5. 사우디아라비아 태양광 발전 시스템의 성능 분석 vol.37, pp.1, 2013, https://doi.org/10.7836/kses.2017.37.1.081
  6. 일기 예보와 예측 일사 및 일조를 이용한 태양광 발전 예측 vol.21, pp.6, 2013, https://doi.org/10.12673/jant.2017.21.6.643
  7. RNN-LSTM을 이용한 태양광 발전량 단기 예측 모델 vol.22, pp.3, 2013, https://doi.org/10.12673/jant.2018.22.3.233
  8. An Improved Photovoltaic System Output Prediction Model under Limited Weather Information vol.13, pp.5, 2018, https://doi.org/10.5370/jeet.2018.13.5.1874
  9. 태양광 발전시스템 효율향상을 위한 스마트 모니터링 시스템 vol.19, pp.1, 2013, https://doi.org/10.7236/jiibc.2019.19.1.219