The accurate prediction of photovoltaic (PV) power generation is crucial for improving virtual power plant (VPP) efficiency and power system stability. However, short-term PV power forecasting remains highly challenging due to the significant impact of weather changes, especially the complexity of cloud motion. To this end, this paper proposes an end-to-end innovative deep learning framework for data fusion based on multimodal learning, which utilizes a new auto cross modal correlation attention (ACMCA) mechanism designed in this paper for feature extraction and fusion by combining historical PV power generation time-series data and sky image data, thereby enhancing the model’s prediction performance under complex weather conditions. In this paper, the effectiveness of the proposed model was verified through a large number of experiments, and the experimental results showed that the model’s forecast skill (FS) reached 24.2% under all weather conditions 15 min in advance, and 24.32% under cloudy conditions with the largest fluctuations. This paper also compared the model with a variety of existing unimodal and multimodal models, respectively. The experimental results showed that the model in this paper outperformed other benchmark methods in all indices under different weather conditions, demonstrating stronger adaptability and robustness.

The main purpose of this study is to generate defect images of body parts using a GAN (generative adversarial network) and compare and analyze the performance of the YOLO (You Only Look Once) v7 and v8 object detection models. The goal is to accurately judge good and defective products. Quality control is very important in the automobile industry, and defects in body parts directly affect vehicle safety, so the development of highly accurate defect detection technology is essential. This study ensures data diversity by generating defect images of car body parts using a GAN and through this, compares and analyzes the object detection performance of the YOLO v7 and v8 models to present an optimal solution for detecting defects in car parts. Through experiments, the dataset was expanded by adding fake defect images generated by the GAN. The performance experiments of the YOLO v7 and v8 models based on the data obtained through this approach demonstrated that YOLO v8 effectively identifies objects even with a smaller amount of data. It was confirmed that defects could be detected. The readout of the detection system can be improved through software calibration.

Virtual Power Plants (VPPs) are integral to modern energy systems, providing stability and reliability in the face of the inherent complexities and fluctuations of solar power data. Traditional anomaly detection methodologies often need to adequately handle these fluctuations from solar radiation and ambient temperature variations. We introduce the Memory-Enhanced Autoencoder with Adversarial Training (MemAAE) model to overcome these limitations, designed explicitly for robust anomaly detection in VPP environments. The MemAAE model integrates three principal components: an LSTM-based autoencoder that effectively captures temporal dynamics to distinguish between normal and anomalous behaviors, an adversarial training module that enhances system resilience across diverse operational scenarios, and a prediction module that aids the autoencoder during the reconstruction process, thereby facilitating precise anomaly identification. Furthermore, MemAAE features a memory mechanism that stores critical pattern information, mitigating overfitting, alongside a dynamic threshold adjustment mechanism that adapts detection thresholds in response to evolving operational conditions. Our empirical evaluation of the MemAAE model using real-world solar power data shows that the model outperforms other comparative models on both datasets. On the Sopan-Finder dataset, MemAAE has an accuracy of 99.17% and an F1-score of 95.79%, while on the Sunalab Faro PV 2017 dataset, it has an accuracy of 97.67% and an F1-score of 93.27%. Significant performance advantages have been achieved on both datasets. These results show that MemAAE model is an effective method for real-time anomaly detection in virtual power plants (VPPs), which can enhance robustness and adaptability to inherent variables in solar power generation.

COVID-19 has posed significant challenges to face-to-face sales of apartments and requires innovative solutions. In order to solve this problem, this paper proposes a model of a metaverse apartment sales promotion system. The proposed system utilizes the Zep metaverse platform to provide customers with virtual model houses and comprehensive real estate information. It provides various views, including satellite maps and 360° video, allowing customers to remotely explore the location of their apartments. Customers can explore the interior of the apartment via avatar, access 2D drawings and immersive 360° 3D videos. In addition, detailed sales conditions can be conveniently checked through a YouTube link, and customer convenience has been enhanced through online consultation with a sales agent. The proposed system aims to enhance the apartment buying and selling process by providing an immersive and convenient metaverse experience.

This paper highlights the critical need for national functional certifications designed explicitly for generative AI ogy, addressing the challenges its rapid advancement poses. The current educational and accreditation systems are struggling to keep pace with the fast-evolving demands of the AI field, resulting in a gap between the skills required by industries and those possessed by the workforce. To close this gap, the paper proposes a structured certification program that aligns educational curricula with industry needs, ensuring that professionals gain both theoretical knowledge and practical skills in generative AI. A key element of this certification program is fostering collaboration between industry and academia. This partnership is vital for developing educational content that is not only relevant but also practical, directly preparing students for real-world applications. By closely aligning academic training with industry requirements, the program aims to produce professionals adept at implementing generative AI technologies across various sectors, effectively bridging the gap between education and practice. The paper also emphasizes the importance of maintaining the quality and credibility of the certification system through continuous assessment and regular updates. Such evaluations are essential to ensure the certification remains valuable and respected nationally and internationally. Implementing this certification program is expected to cultivate a highly skilled workforce, stimulate the job market, and enhance national competitiveness in the global economy. Additionally, ongoing research is recommended to monitor advancements in AI and update the certification program accordingly, ensuring it stays aligned with the latest industry trends and technological developments.