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

Dongkyu Lee, Jinhwa Jeong, Sung Hoon Yoon and Young Tae Chae
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Dongkyu Lee: Department of Architectural Engineering, Hanyang University, Seoul 04763, Korea
Jinhwa Jeong: Department of Architectural Engineering, Cheongju University, Cheongju 28503, Korea
Sung Hoon Yoon: Department of Architecture, Cheongju University, Cheongju 28503, Korea
Young Tae Chae: Department of Architectural Engineering, Cheongju University, Cheongju 28503, Korea

Energies, 2019, vol. 12, issue 17, 1-17

Abstract: 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 predictions using feature engineering and machine learning. Feature selection measured the importance score of input features by using a model-based variable importance. It verified that the normative sky index in the weather forecasted data had the least importance as a predictor for hourly prediction of photovoltaic power output. Six different machine-learning algorithms were assessed to select an appropriate model for the hourly power output prediction with onsite weather forecast data. The recurrent neural network outperformed five other models, including artificial neural networks, support vector machines, classification and regression trees, chi-square automatic interaction detection, and random forests, in terms of its ability to predict photovoltaic power output at an hourly and daily resolution for 64 tested days. Feature engineering was then used to apply dropout observation to the normative sky index from the training and prediction process, which improved the hourly prediction performance. In particular, the prediction accuracy for overcast days improved by 20% compared to the original weather dataset used without dropout observation. The results show that feature engineering effectively improves the short-term predictions of photovoltaic power output in buildings with a simple weather forecasting service.

Keywords: building-integrated photovoltaic (BIPV); feature engineering; recurrent neural network (RNN); variable importance; short-term predictions (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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