Abstract
We have mentioned the difficulty of smart grid forecast and dispatch mainly lies in the strong uncertainty, curse of dimensionality and the trouble of establishing the accurate model. Fortunately, as one of the model-free approaches, RL is able to deal with the stochastic renewable energy and user power demand through interacting with the environment in the absence of prior knowledge.
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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Li, Y., Zhao, Y., Wu, L., Zeng, Z. (2023). Prospects of Future Research Issues. In: Artificial Intelligence Enabled Computational Methods for Smart Grid Forecast and Dispatch. Engineering Applications of Computational Methods, vol 14. Springer, Singapore. https://doi.org/10.1007/978-981-99-0799-1_12
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DOI: https://doi.org/10.1007/978-981-99-0799-1_12
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