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    Advanced Short-Term Wind Power Forecasting Based on CNN-BiLSTM - Lightweight Self-Attention (LWSA)
    (2025-01-01)
    Huang, Zhibin
    ;
    Jiriwibhakorn, Somchat
    Accurate short-term wind power forecasting is critical for maintaining grid stability and enhancing energy dispatch. However, the nonlinear, volatile, and uncertain nature of wind power poses significant challenges to traditional and deep learning models. To address this, a hybrid model named CNN-BiLSTM-LWSA is proposed, which integrates Convolutional Neural Networks (CNN) for local pattern extraction, Bidirectional Long Short-Term Memory (BiLSTM) networks for temporal modeling, and a Lightweight Self-Attention (LWSA) mechanism based on Lin-former. The LWSA module applies low-rank projections to reduce attention complexity from O(n²) to O(n), enabling efficient long-sequence learning while preserving global dependencies. Experi-ments were conducted using a full-year dataset (35,040 records at 15-minute intervals) from the Mahuangshan First Wind Farm in Ningxia, China. The model was tested under various input win-dow lengths (1h, 3h, 12h, 24h, and 32h). Results show that CNN-BiLSTM-LWSA consistently out-performs CNN-BiLSTM and CNN-BiLSTM-Attention in both accuracy and efficiency. Under a 24-hour input, it achieves an RMSE of 53.4 kW, MAE of 23.2 kW, and R<sup>2</sup> of 0.955 while reducing training and testing time by 54.8% and 47.1%, respectively, compared to the attention-based base-line. Even with a 32-hour input, the model maintains low prediction errors and stable R<sup>2</sup>, validating its scalability. The experimental results fully confirm that CNN-BiLSTM-LWSA effectively balances forecasting accuracy and computational cost across different temporal settings, offering a robust, efficient, and practical solution for short-term wind power forecasting applications.
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    Quantitative Assessment of Retrieval Strategies in RAG Architectures: A Comparative Study Across Multiple Knowledge Domains Using Standardized Performance Metrics
    (2025-01-01)
    Aromsuk, Tinnarat
    ;
    Nootyaskool, Supakit
    ;
    Netisopakul, Ponrudee
    This study conducts a systematic quantitative assessment of four distinct retrieval methodologies within Retrieval-Augmented Generation (RAG) frameworks: baseline implementation, hybrid dual-paradigm approach, hierarchical parent-child structure, and contextual compression. Through rigorous experimental evaluation spanning six distinct knowledge domains, we employ established metrics including ROUGE [1], BLEU [2], and computational timing measurements to characterize performance profiles. Our findings reveal that sophisticated retrieval approaches deliver substantial computational efficiency gains (4-5 × acceleration) alongside varied performance patterns across quality assessment dimensions. The hierarchical parent-child methodology demonstrates superior BLEU performance (0.1046 mean score) coupled with optimal retrieval speeds (0.0124 s), whereas hybrid approaches excel in ROUGE metrics (0.0317 mean score). Domain-specific analysis indicates pronounced performance disparities: medical/health domains (COVID19 pandemic) achieve highest aggregate scores (0.1198 mean ROUGE), while specialized technical and legal domains present distinct retrieval complexities. This research establishes empirical foundations for evidence-based retrieval method selection, identifying clear efficiency-quality relationships and domain-dependent optimization strategies for production RAG deployments.