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http://dx.doi.org/10.7836/kses.2019.39.5.041

Development of a Prediction Model of Solar Irradiances Using LSTM for Use in Building Predictive Control  

Jeon, Byung-Ki (Department of Architectural Engineering, Graduate School, Inha University)
Lee, Kyung-Ho (Department of Solar Thermal Convergence Lab, Korea Institute of Energy Research)
Kim, Eui-Jong (Department of Architectural Engineering, Inha University)
Publication Information
Journal of the Korean Solar Energy Society / v.39, no.5, 2019 , pp. 41-52 More about this Journal
Abstract
The purpose of the work is to develop a simple solar irradiance prediction model using a deep learning method, the LSTM (long term short term memory). Other than existing prediction models, the proposed one uses only the cloudiness among the information forecasted from the national meterological forecast center. The future cloudiness is generally announced with four categories and for three-hour intervals. In this work, a daily irradiance pattern is used as an input vector to the LSTM together with that cloudiness information. The proposed model showed an error of 5% for learning and 30% for prediction. This level of error has lower influence on the load prediction in typical building cases.
Keywords
Prediction of solar irradiance; LSTM(Long-term short-term memory); Model Predictive control; Deep Neural Network;
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