# Representative period selection for power system planning using autoencoder-based dimensionality reduction

arXiv: https://arxiv.org/abs/2204.13608 · HTML: https://mlsanity.com/paper/2204.13608/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 70 references that are in the map.
- First posted on arXiv 28 Apr 2022.

## Builds on (most cited first, 4 of 4)

- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Interactive Supercomputing on 40,000 Cores for Machine Learning and Data Analysis](https://mlsanity.com/paper/1807.07814/) (2018)
- [Extreme events in time series aggregation: A case study for optimal residential energy supply systems](https://mlsanity.com/paper/2002.03059/) (2020)
- [Enhanced Representative Days and System States Modeling for Energy Storage Investment Analysis](https://mlsanity.com/paper/1810.09539/) (2018)

## Built on by (most cited first, 2 of 2)

- [Graph Representation Learning for Energy Demand Data: Application to Joint Energy System Planning under Emissions Constraints](https://mlsanity.com/paper/2209.12035/) (2022)
- [Learning Spatio-Temporal Aggregations for Large-Scale Capacity Expansion Problems](https://mlsanity.com/paper/2303.08996/) (2023)

## Abstract (arXiv)

Power sector capacity expansion models (CEMs) that are used for studying future low-carbon grid scenarios must incorporate detailed representation of grid operations. Often CEMs are formulated to model grid operations over representative periods that are sampled from the original input data using clustering algorithms. However, such representative period selection (RPS) methods are limited by the declining efficacy of the clustering algorithm with increasing dimensionality of the input data and do not consider the relative importance of input data variations on CEM outcomes. Here, we propose a RPS method that addresses these limitations by incorporating dimensionality reduction, accomplished via neural network based autoencoders, prior to clustering. Such dimensionality reduction not only improves the performance of the clustering algorithm, but also facilitates using additional features, such as estimated outputs produced from parallel solutions of simplified versions of the CEM for each disjoint period in the input data (e.g. 1 week). The impact of incorporating dimensionality reduction as part of RPS methods is quantified through the error in outcomes of the corresponding reduced-space CEM vs. the full space CEM. Extensive numerical experimentation across various networks and range of technology and policy scenarios establish the superiority of the dimensionality-reduction based RPS methods.