Genetic algorithm selection of the weather research and forecasting model physics to support wind and solar energy integration

Sward, Jeffrey A.; Ault, Toby R.; Zhang, K. Max

doi:10.1016/j.energy.2022.124367

Cited by 20 publications

(2 citation statements)

References 55 publications

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“…In [10], their authors use GA for the quantification of meteoroid fragmentation cascades (starting from their initial masses and velocities). Weather forecasting models such as the weather research and forecasting have also benefited from the application of GA for the quantification of wind power density and solar irradiance [11]. In the field of physics of materials, GA have played a major role in the search for optimised photonic crystal-based structures [12] and in hot compression tests for aluminium alloys [13].…”

Section: Gas In Physicsmentioning

confidence: 99%

Fostering scientific methods in simulations through symbolic regressions

Llorella,

Antonio Cebrián,

Corbi

et al. 2024

Phys. Educ.

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Two-dimensional computer and tablet PC physics simulations have proved to be effective in helping students understand the fundamental principles of physics and related natural processes. However, the current approach to using these simulations tends to follow a repetitive cognitive and procedural pathway, regardless of the specific physical concepts being explored or software environment being used. This approach involves manipulating the simulation interface and collecting data through interaction with controls, widgets, or other contextual elements. Students then attempt to determine how these experimental measurements align with established laws, interactions, or mechanisms, as the teacher might have previously explained. We believe that this approach, while appropriate for education, obscures scientific processes, mainly related to the hypothetico-deductive model. To address this issue, we have developed a simple and adaptable computer environment that makes use of genetic algorithms (GAs) and symbolic regression to derive many of the basic laws of nature from the data collected by students using the popular physics education technology (PhET) simulations environment. Our proposal enables learners to observe how the order and relationships of mathematical tokens are routinely refined as new data points are added to the simulation setting. This iterative distillation technique can also be augmented with the interplay of dimensional analysis. In contrast with other more sophisticated artificial intelligence patterns, GA fit into the realm of grey box machine learning models. These type of evolutionary algorithms achieve the sought results by evolving mathematical models on each stage in an understandable way, which mimics the way scientific breakthroughs are accomplished (over the course of generations of researchers and based of prior knowledge). By implementing this innovative approach, we can provide students with a more authentic empirical experience that fosters a deeper understanding of the principles of science and scientific discovery. Field tests with students supporting this claim have also been carried out.

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Section: Gas In Physicsmentioning

confidence: 99%

Fostering scientific methods in simulations through symbolic regressions

Llorella,

Antonio Cebrián,

Corbi

et al. 2024

Phys. Educ.

View full text Add to dashboard Cite

show abstract

“…As a safe, cost-effective, environmentally friendly, and renewable energy source, solar energy is a viable alternative to enhance the overall energy structure [ 2 , 3 ]. Solar energy has extensive applications across diverse fields such as weather forecasting [ 4 ], photovoltaic power utilization [ 5 ], zero-energy building design [ 6 , 7 ], climate observation [ 8 ], and agricultural usage [ 9 ]. Improving the capabilities in hourly solar radiation modeling can directly and significantly impact the quality of applied studies.…”

Section: Introductionmentioning

confidence: 99%