Research on pre-site selection of pumped storage power stations based on artificial intelligence

Abstract

[Objective] Current pre-site selection of pumped storage power stations heavily relies on manual comparison and selection, which suffers from time-consuming processes and low-automation levels. To address these issues, a method integrating multimodal large models into the pre-site selection of pumped storage power stations is proposed. [Methods] Based on site selection criteria for pumped storage power stations, an evaluation system for potential sites was established. The fuzzy comprehensive evaluation method was employed to calculate an overall score for each site, which served as the station label. Then, specific prompts were designed to guide the GPT model in generating prompt fine-tuning data associated with remote sensing images. Based on this, prompt engineering and Low-Rank Adaptation(LoRA) fine-tuning techniques were used to train the multimodal large language model LLaVA. Subsequently, the trained model was applied to the pre-site selection of the Jixi Pumped Storage Power Station in Anhui Province, followed by a systematic evaluation of the model performance. [Results] The result showed that the model accurately scored for key indicators such as hydrology, topography, and economic factors for the Jixi Pumped Storage Power Station, yielding a comprehensive score of 84.4 that met the criteria for an ideal site. When validated on a test set of 1 091 samples, the model successfully identified 74.1% of ideal site samples and 82.4% of non-ideal site samples. The fine-tuned LLaVA model achieved an Area Under the Curve(AUC) value of 0.822, outperforming Qwen-VL-Chat, InternLM-XComposer-VL, VisualGLM, and InstructBLIP models by 0.106, 0.152, 0.205, and 0.207, respectively. [Conclusion] The findings indicate that the LLaVA model fine-tuned by the proposed method achieves significant improvements in accuracy, recall, and false detection rates for site classification compared to general-purpose multimodal models. Additionally, it demonstrates excellent site evaluation in practical applications, showing high potential for broader application. The domain-specific fine-tuning and application of the LLaVA model effectively highlight the unique advantages of multimodal large models in improving the efficiency and automation level of site selection, providing robust support for the intelligent transformation of the pumped storage industry.

Keywords

pumped storage power station / LLaVA Model / pre-site selection / prompt fine-tuning / influencing factors