Summary of Testing the Efficacy Of Hyperparameter Optimization Algorithms in Short-term Load Forecasting, by Tugrul Cabir Hakyemez et al.
Testing the Efficacy of Hyperparameter Optimization Algorithms in Short-Term Load Forecasting
by Tugrul Cabir Hakyemez, Omer Adar
First submitted to arxiv on: 19 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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 This paper investigates the effectiveness of five hyperparameter optimization (HPO) algorithms for short-term load forecasting (STLF). The studied HPO methods are Random Search, Covariance Matrix Adaptation Evolution Strategy (CMA–ES), Bayesian Optimization, Partial Swarm Optimization (PSO), and Nevergrad Optimizer (NGOpt). The authors evaluate these algorithms using the Panama Electricity dataset with a surrogate forecasting algorithm, XGBoost. Performance metrics such as mean absolute percentage error (MAPE) and R-squared are assessed across varying sample sizes from 1,000 to 20,000. The results show significant runtime advantages for HPO algorithms over Random Search, while Bayesian optimization exhibited the lowest accuracy in univariate models. This study provides insights for optimizing XGBoost in STLF contexts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at how to make better predictions about electricity usage. It compares five different ways to find the best settings for a machine learning model called XGBoost. The authors test these methods using real data from Panama’s power grid. They look at how well each method does and how fast it works. The results show that some methods are much faster than others, but not all of them do as well as we might hope. This study helps us understand how to make better predictions about electricity usage. |
Keywords
» Artificial intelligence » Hyperparameter » Machine learning » Optimization » Xgboost
