SolarFlux Predictor: A Novel Deep Learning Approach for Photovoltaic Power Forecasting in South Korea
AI-ready brief
: We present SolarFlux Predictor, a novel deep-learning model designed to revolutionize photovoltaic (PV) power forecasting in South Korea. This model uses a self-attention-based temporal convolutional network (TCN) to process and predict PV outputs with high precision.
Author abstract
: We present SolarFlux Predictor, a novel deep-learning model designed to revolutionize photovoltaic (PV) power forecasting in South Korea. This model uses a self-attention-based temporal convolutional network (TCN) to process and predict PV outputs with high precision. We perform meticulous data preprocessing to ensure accurate data normalization and outlier rectification, which are vital for reliable PV power data analysis. The TCN layers are crucial for capturing temporal patterns in PV energy data; we complement them with the teacher forcing technique during the training phase to significantly enhance the sequence prediction accuracy. By optimizing hyper- parameters with Optuna, we further improve the model’s performance. Our model incorporates multi-head self-attention mechanisms to focus on the most impactful temporal features, thereby improving forecasting accuracy. In validations against datasets from nine regions in South Korea, SolarFlux outperformed conventional methods. The results indicate that SolarFlux is a robust tool for optimizing PV systems’ management and operational efficiency and can contribute to South Korea’s pursuit of sustainable energy solutions.
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The key contribution is predictive modeling, so the page emphasizes task setting, target behavior, and downstream planning or operational value.
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Citation-ready BibTeX
@article{noh2024solarfluxpredictoranovel,
title = {SolarFlux Predictor: A Novel Deep Learning Approach for Photovoltaic Power Forecasting in South Korea},
author = {H. Min and S. Hong and J. Song and B. Son and B. Noh and J. Moon},
year = {2024},
journal = {Electronics , 13(11), 2071 (2024).}
}