Neural network interpretability for forecasting of aggregated renewable generation

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Abstract

With the rapid growth of renewable energy, lots of small photovoltaic (PV) prosumers emerge. Due to the uncertainty of solar power generation, there is a need for aggregated prosumers to predict solar power generation and whether solar power generation will be larger than load. This paper presents two interpretable neural networks to solve the problem: one binary classification neural network and one regression neural network. The neural networks are built using TensorFlow. The global feature importance and local feature contributions are examined by three gradient-based methods: Integrated Gradients, Expected Gradients, and DeepLIFT. Moreover, we detect abnormal cases when predictions might fail by estimating the prediction uncertainty using Bayesian neural networks. Neural networks, which are interpreted by the gradient-based methods and complemented with uncertainty estimation, provide robust and explainable forecasting for decision-makers.
Original languageEnglish
Title of host publicationProceedings of 2021 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids
PublisherIEEE
Publication date2021
Pages282-288
ISBN (Print)9781665415026
DOIs
Publication statusPublished - 2021
Event2021 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids - Aachen , Germany
Duration: 25 Oct 202128 Oct 2021
https://ieeexplore.ieee.org/xpl/conhome/9631985/proceeding

Conference

Conference2021 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids
Country/TerritoryGermany
CityAachen
Period25/10/202128/10/2021
Internet address

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