Summary of Electricity Price Prediction Using Multi-kernel Gaussian Process Regression Combined with Kernel-based Support Vector Regression, by Abhinav Das et al.
Electricity Price Prediction Using Multi-Kernel Gaussian Process Regression Combined with Kernel-Based Support Vector Regression
by Abhinav Das, Stephan Schlüter, Lorenz Schneider
First submitted to arxiv on: 28 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Probability (math.PR)
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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 proposes a novel hybrid model for predicting German electricity prices by combining Gaussian Process Regression (GPR) and Support Vector Regression (SVR). The GPR component excels at learning stochastic patterns within data and interpolation, but its out-of-sample performance is limited. To mitigate this issue, the authors use SVR, which leverages margin-based optimization to handle non-linear processes and outliers. Both predictions are combined using a performance-based weight assignment method. Experimental results on historic German power prices demonstrate that this approach outperforms benchmarks such as autoregressive exogenous models, naive approaches, and long short-term memory models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to predict electricity prices in Germany. They combine two different methods: one that learns patterns within the data (Gaussian Process Regression) and another that’s good at dealing with noise and outliers (Support Vector Regression). By using both approaches together, they can make more accurate predictions. They tested their method on historic power price data and found it performed better than other popular prediction methods. |
Keywords
» Artificial intelligence » Autoregressive » Optimization » Regression
