Photovoltaic Power Generation Forecasting Utilizing Long Short Term Memory

Authors

  • Nguyen- Duc Tuyen School of Electrical Engineering
  • Vu Huu Xuan Son Hanoi University of Science and Technology
  • Le Viet Thinh Hanoi University of Science and Technology

Keywords:

PV power forecasting, Genetic algorithm, Long Short Term Memory, RMSE, MAPE

Abstract

The modernization of the world has considerably reduced the prime sources of energy, such as coal, diesel and gas. Thus, alternative energy
sources based on renewable energy have been the main concentration nowadays to address the world’s energy demand and at the same time to
restrict global warming. Among these renewable energies, solar energy is the main source used to generate electricity through photovoltaic
(PV) systems. However, the output of PV power is highly intermittent. Thus accurately forecasting the output of PV systems is an important
requirement to ensure the stability and reliability of the grid. This study develops and validates a short-term PV power forecasting model
by using the combination of a genetic algorithm (GA) and Long Short Term Memory (LSTM). The performance of the proposed model is
compared with LSTM baseline model by three errors (Root mean square error(RMSE), Mean absolute error (MAE), and Mean absolute
percentage error (MAPE)) in two case studies.

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Published

2021-05-01

How to Cite

Tuyen, N.-D., Vu Xuan Son, H., & Le Viet, T. (2021). Photovoltaic Power Generation Forecasting Utilizing Long Short Term Memory. Measurement, Control, and Automation, 1(2). Retrieved from https://mca-journal.org/index.php/mca/article/view/21

Issue

Section

General

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