Summary of Powermamba: a Deep State Space Model and Comprehensive Benchmark For Time Series Prediction in Electric Power Systems, by Ali Menati et al.
PowerMamba: A Deep State Space Model and Comprehensive Benchmark for Time Series Prediction in Electric Power Systems
by Ali Menati, Fatemeh Doudi, Dileep Kalathil, Le Xie
First submitted to arxiv on: 9 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Systems and Control (eess.SY)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel multivariate time series prediction model is introduced in this paper, combining traditional state space models with deep learning methods to predict the underlying dynamics of multiple time series. The proposed approach incorporates high-resolution external forecasts into sequence-to-sequence prediction models, achieving negligible increases in size while maintaining accuracy. A comprehensive dataset spanning five years of load, electricity price, ancillary service price, and renewable generation is released alongside an open-access toolbox including the proposed model and several state-of-the-art prediction models for benchmarking advanced machine learning approaches. The findings demonstrate that the proposed model outperforms existing models across various prediction tasks, improving state-of-the-art prediction error by an average of 7% while decreasing model parameters by 43%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The electricity grid is becoming more unpredictable due to changes in demand and renewable energy integration. To manage this volatility, advanced time series prediction models are needed. This paper presents a new approach that combines traditional methods with deep learning techniques to predict multiple time series. The method includes external forecasts and achieves accuracy without increasing size. A dataset of five years of load, price, and generation data is released along with an open-source toolbox for comparing different prediction models. |
Keywords
» Artificial intelligence » Deep learning » Machine learning » Time series
