Abstract
We consider the task of predicting the solar power output for the next day from previous solar power data. We propose EN-meta, a meta-learning ensemble of neural networks where the meta-learners are trained to predict the errors of the ensemble members for the new day, and these errors are used to dynamically weight the contribution of the ensemble members in the final prediction. We evaluate the performance of EN-meta on Australian solar data for two years and compare its accuracy with state-of-the-art single models, classical ensemble methods and EN-meta versions without the meta-learning component. The results showed that EN-meta was the most accurate method and thus highlight the potential benefit of using meta-learning for solar power forecasting.
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Wang, Z., Koprinska, I. (2018). Solar Power Forecasting Using Dynamic Meta-Learning Ensemble of Neural Networks. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11139. Springer, Cham. https://doi.org/10.1007/978-3-030-01418-6_52
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DOI: https://doi.org/10.1007/978-3-030-01418-6_52
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