Generating power system states that have similar distribution and dependency to the historical ones is essential for the tasks of system planning and security assessment, especially when the historical data is insufficient. In this paper, we described a generative model for load profiles of industrial and commercial customers, based on the conditional variational autoencoder (CVAE) neural network architecture, which is challenging due to the highly variable nature of such profiles. Generated contextual load profiles were conditioned on the month of the year and typical power exchange with the grid. Moreover, the quality of generations was both visually and statistically evaluated. The experimental results demonstrate our proposed CVAE model can capture temporal features of historical load profiles and generate ‘realistic’ data with satisfying univariate distributions and multivariate dependencies.
|Title of host publication||Proceedings of 2022 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2022|
|Number of pages||6|
|Publication status||Published - 2022|
|Event||2022 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe) - Novi Sad, Serbia|
Duration: 10 Oct 2022 → 12 Oct 2022
|Name||IEEE PES Innovative Smart Grid Technologies Conference Europe|
|Conference||2022 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe)|
|Period||10/10/22 → 12/10/22|
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- generative model
- load profiles
- synthetic data