Neural network interpretability for forecasting of aggregated renewable generation

Yucun Lu, Ilgiz Murzakhanov, Spyros Chatzivasileiadis

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

    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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