Improvement of Short-Term BIPV Power Predictions Using Feature Engineering and a Recurrent Neural Network

Improvement of Short-Term BIPV Power Predictions Using Feature Engineering and a Recurrent Neural Network

The time resolution and prediction accuracy of the power generated by building-integrated photovoltaics are important for managing electricity demand and formulating a strategy to trade power with the grid. This study presents a novel approach to improve short-term hourly photovoltaic power output p...

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Bibliographic Details
Main Authors: Dongkyu Lee, Jinhwa Jeong, Sung Hoon Yoon, Young Tae Chae
Format: Article
Language:English
Published: MDPI AG 2019-08-01
Series:Energies
Subjects:
building-integrated photovoltaic (BIPV)
feature engineering
recurrent neural network (RNN)
variable importance
short-term predictions
Online Access:https://www.mdpi.com/1996-1073/12/17/3247

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https://www.mdpi.com/1996-1073/12/17/3247

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