About Yize Solar Power Generation
This repository contains source code necessary to reproduce the results presented in the following paper:Model-Free Renewables Scenario Generation Using G.
Engineers need an efficient and scalable technique to capture and model the dynamics of time-series scenarios as well as spatio-temporal scenarios for renewables gene.
We used Python to implement the algorithm. Some data processing work was finished in Matlab. Specifically, we used the open-source Python package Tensorflow to train the neural n.
Datasets: To reproduce the results shown in paper, we also updated the three datasets, which corresponding to wind scenario generation, solar scenario generation and w.
To evaluate if generated scenarios are good enough, we suggest two methods to use:a) Check the loss function, which is the metric we used d.
We thank https://github.com/carpedm20/DCGAN-tensorflow for contributing the initial version of tensorflow convolutional GAN module; https://github.co.
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