Summary of A Flow-based Model For Conditional and Probabilistic Electricity Consumption Profile Generation and Prediction, by Weijie Xia et al.
A Flow-Based Model for Conditional and Probabilistic Electricity Consumption Profile Generation and Prediction
by Weijie Xia, Chenguang Wang, Peter Palensky, Pedro P. Vergara
First submitted to arxiv on: 3 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Systems and Control (eess.SY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces a novel flow-based generative model called Full Convolutional Profile Flow (FCPFlow), which is designed for both conditional and unconditional Residential Load Profile (RLP) generation, as well as probabilistic load forecasting. FCPFlow features two new layers: the invertible linear layer and the invertible normalization layer. The proposed architecture demonstrates three key advantages over traditional statistical and contemporary deep generative models. Specifically, it excels at generating RLPs under continuous conditions, such as varying weather and annual electricity consumption, scales well in different datasets compared to traditional statistical models, and captures complex correlations in RLPs better than deep generative models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary RLP generation and prediction are crucial for managing distribution networks. This paper develops a new model called FCPFlow that can generate both conditional and unconditional RLPs, as well as forecast loads probabilistically. The model has two special layers that help it do this job well. It’s better than other models at generating RLPs when things change over time, like the weather or how much electricity people use in a year. It also works well with different datasets and can capture complex patterns in RLPs. |
Keywords
» Artificial intelligence » Generative model
