🤖 AI Summary
A unified survey and benchmark for Bayesian optimization (BO) tailored to climate change applications is currently lacking, hindering its deployment on costly, black-box problems in this domain. Method: We introduce the first climate-specific BO benchmarking framework, encompassing four representative application scenarios: materials discovery, wind farm layout optimization, renewable energy control, and environmental monitoring. We propose LAQN-BO—the first environmental monitoring benchmark grounded in real-world air quality data from the London Air Quality Network (LAQN)—and systematically design four reproducible, representative benchmark tasks. The framework includes open-source code, standardized evaluation protocols, and Gaussian process modeling utilities. Contribution/Results: This work fills a critical gap in the literature, substantially enhancing comparability, reproducibility, and community adoption of BO for high-impact climate governance tasks.
📝 Abstract
Bayesian optimisation is a powerful method for optimising black-box functions, popular in settings where the true function is expensive to evaluate and no gradient information is available. Bayesian optimisation can improve responses to many optimisation problems within climate change for which simulator models are unavailable or expensive to sample from. While there have been several feasibility demonstrations of Bayesian optimisation in climate-related applications, there has been no unifying review of applications and benchmarks. We provide such a review here, to encourage the use of Bayesian optimisation in important and well-suited application domains. We identify four main application domains: material discovery, wind farm layout, optimal renewable control and environmental monitoring. For each domain we identify a public benchmark or data set that is easy to use and evaluate systems against, while being representative of real-world problems. Due to the lack of a suitable benchmark for environmental monitoring, we propose LAQN-BO, based on air pollution data. Our contributions are: a) identifying a representative range of benchmarks, providing example code where necessary; b) introducing a new benchmark, LAQN-BO; and c) promoting a wider use of climate change applications among Bayesian optimisation practitioners.
