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Predictive model for PV power generation using RNN (LSTM)

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Abstract

In recent years, advanced information technologies, such as deep learning and big data, have been actively applied in building energy management systems to improve energy efficiency. Various studies have been conducted on the prediction of renewable energy performance using machine learning techniques. In this study, a recurrent neural network (RNN) was utilized in predicting photovoltaic (PV) power generation. An RNN is an artificial neural network in which the connection between units is composed of a cyclic structure that can reflect the characteristics of time series. Therefore, to eventually incorporate a model predictive control technique for energy demand and supply matching, this study uses previously measured weather data and PV power generation data to predict the future PV power generation. Various optimization processes, such as normalization, classification of learning data, and setting of layer options, are performed to create a predictive model. Furthermore, 500 hidden neurons and 1 and 3 hidden layers are created and compared. The initial learning rate for both single and multiple-layer options was set to 0.05. As a result, the coefficient of variation of the root mean squared error, Cv(RMSE), which is a statistical index to assess the overall accuracy of the predicted values, was 13.8 % for a single layer and 13.2 % for a multiple-layer long short term memory model. Although no significant difference was observed between the two models, the multiple-layer model showed a slightly reduced error rate than the single-layer model, and the accurate prediction was confirmed to be achievable. Based on this study, a stable energy supply system can be established through linkage with energy storage control by predicting the amount of energy production through renewable energy sources and the amount of energy demand at urban scale.

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Abbreviations

S :

RNN model predicted

M :

Measured

N interval :

Number of measured data

A period :

Average of measurement period

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Acknowledgments

This study was partly supported by a Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Korean government (MOTIE) (2019271010015B, Development of Eco-friendly Energy Supply Resource Control System). This work was supported by a Korea University Grant (No. K1921431).

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Authors and Affiliations

  1. Graduate School, Department of Architecture, College of Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Korea

    Min Kyeong Park, Jong Man Lee & Won Hee Kang

  2. Department of Mechanical Engineering, Hanbat National University, 125 Dongseo-daero, Yuseong-gu, Daejeon, 34158, Korea

    Jong Min Choi

  3. Department of Architecture, College of Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Korea

    Kwang Ho Lee

Authors
  1. Min Kyeong Park
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  2. Jong Man Lee
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  3. Won Hee Kang
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Corresponding author

Correspondence to Kwang Ho Lee.

Additional information

Minkyeong Park is pursuing a Master’s degree at the Department of Architecture, College of Engineering, Korea University, Seoul, Korea.

Kwang Ho Lee is an Associate Professor of the Department of Architecture, College of Engineering, Korea University, Seoul, Korea. He received his Ph.D. from the School of Architecture from the University of Illinois at Urbana-Champaign. His research interests include advanced HVAC system control.

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Park, M.K., Lee, J.M., Kang, W.H. et al. Predictive model for PV power generation using RNN (LSTM). J Mech Sci Technol 35, 795–803 (2021). https://doi.org/10.1007/s12206-021-0140-0

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  • DOI: https://doi.org/10.1007/s12206-021-0140-0

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