Predicting the behavior of renewable energy systems requires models capable of generating accurate forecasts from limited historical data, a challenge that becomes especially pronounced when commissioning new facilities where operational records are scarce. This review aims to synthesize recent progress in data-efficient deep learning approaches for addressing such “cold-start” forecasting problems. It primarily covers three interrelated domains—solar photovoltaic (PV), wind power, and electrical load forecasting—where data scarcity and operational variability are most critical, while also including representative studies on hydropower and carbon emission prediction to provide a broader systems perspective. To this end, we examined trends from over 150 predominantly peer-reviewed studies published between 2019 and mid-2025, highlighting advances in zero-shot and few-shot meta-learning frameworks that enable rapid model adaptation with minimal labeled data. Moreover, transfer learning approaches combined with spatiotemporal graph neural networks have been employed to transfer knowledge from existing energy assets to new, data-sparse environments, effectively capturing hidden dependencies among geographic features, meteorological dynamics, and grid structures. Synthetic data generation has further proven valuable for expanding training samples and mitigating overfitting in cold-start scenarios. In addition, large language models and explainable artificial intelligence (XAI)—notably conversational XAI systems—have been used to interpret and communicate complex model behaviors in accessible terms, fostering operator trust from the earliest deployment stages. By consolidating methodological advances, unresolved challenges, and open-source resources, this review provides a coherent overview of deep learning strategies that can shorten the data-sparse ramp-up period of new energy infrastructures and accelerate the transition toward resilient, low-carbon electricity grids.

The increased accessibility of social networking services (SNSs) has facilitated communication and information sharing among users. However, it has also heightened concerns about digital safety, particularly for children and adolescents who are increasingly exposed to online grooming crimes. Early and accurate identification of grooming conversations is crucial in preventing long-term harm to victims. However, research on grooming detection in South Korea remains limited, as existing models trained primarily on English text and fail to reflect the unique linguistic features of SNS conversations, leading to inaccurate classifications. To address these issues, this study proposes a novel framework that integrates optical character recognition (OCR) technology with KcELECTRA, a deep learning-based natural language processing (NLP) model that shows excellent performance in processing the colloquial Korean language. In the proposed framework, the KcELECTRA model is fine-tuned by an extensive dataset, including Korean social media conversations, Korean ethical verification data from AI-Hub, and Korean hate speech data from HuggingFace, to enable more accurate classification of text extracted from social media conversation images. Experimental results show that the proposed framework achieves an accuracy of 0.953, outperforming existing transformer-based models. Furthermore, OCR technology shows high accuracy in extracting text from images, demonstrating that the proposed framework is effective for online grooming detection. The proposed framework is expected to contribute to the more accurate detection of grooming text and the prevention of grooming-related crimes.