Accurate short-term electrical load forecasting is of paramount importance for economic dispatch, reliable grid operation, and efficient demand-side management. However, hybrid forecasting frameworks constructed with deep learning models exhibit strong sensitivity to hyperparameter settings. Moreover, swarm-intelligence optimization algorithms are prone to premature convergence when tuning the hyperparameters of forecasting models, thereby degrading prediction performance. In addition, complex load sequences contain local fluctuations and key temporal segments that are difficult to capture using a single recurrent architecture. To address these challenges, this paper proposes a short-term electrical load forecasting method based on a BiGRU-Attention network optimized by an improved multi-strategy black-winged kite algorithm (IMBKA). The BiGRU extracts bidirectional temporal dependencies from historical load windows, while the attention module assigns adaptive weights to informative time steps and suppresses redundant historical information. To improve hyperparameter optimization, IMBKA introduces Sobol sequence initialization and adaptive elite differential mutation. Sobol sequence initialization enhances population coverage, and adaptive elite differential mutation strengthens information exchange among high-quality individuals. Experimental results on electrical load datasets from Singapore, Australia, and Belgium show that IMBKA-BiGRU-Attention achieves favorable forecasting performance among the compared models. The proposed model obtains RMSE values of 70.07 MW, 159.49 MW, 231.82 MW, and 163.43 MW in the Singapore, Australian, Belgian weekday, and Belgian weekend experiments, respectively. Compared with the best-performing model among the evaluated baselines in each experiment, the RMSE is reduced by 4.65%, 16.48%, 3.34%, and 11.39%, respectively.
Binglin Liang, Zhiwen Wang, Bo Tian et al.· Energies· 0 citations
Hybrid music generators combine the long-range planning of an autoregressive language model with the fidelity of a diffusion- or flow-based acoustic renderer. Yet renderers are trained with clean, target-derived codec tokens but deployed with imperfect language-model predictions, creating codecinterface exposure bias. Rather than treating rendering as a simple reconstruction task,we formulate it as full-context generation from an imperfect discrete plan. We introduce FullDiT, a conditional DiT that fuses eight frame-aligned RVQ streams with independently encoded captions and lyrics and uses non-causal self-attention over the complete acoustic latent sequence. During training, Error-Matched Distractor Conditioning (EMDC) matches per-codebook replacement rates to teacher-forced top-1 error rates and samples near-miss tokens from cosine-KNN neighborhoods without changing the acoustic target. At inference, four-way classifier-free guidance (4-CFG) independently scales codec, lyric, and caption guidance increments. Matched ablations show that EMDC improves ViSQOL by 0.77 under synthetic corruption and is clearly preferred in non-tied comparisons with fixed languagemodel tokens. Further ablations show gains from full-song context and renderer-side text conditioning. The complete system outperforms five commercial systems on 15 of 18 automatic metrics and ranks among the top three on the Artificial Analysis Music with Vocals Leaderboard. The demo page is available at https://selinacloudl.github.io/fulldit-demo/.
Yunjia Li, Mengli Wu, Junyu Dai et al.· 0 citations
In this report, we present Qwen-Audio-3.0-TTS, a production-oriented speech synthesis system that jointly advances content consistency, speaker similarity, prosodic naturalness, audio quality, controllability, multilingual coverage, efficiency, and robustness. It combines a 12.5~Hz low-frame-rate speech tokenizer for reduced inference latency with a five-stage progressive training paradigm for coordinated language model (LM) and flow-matching model (FM) optimization. The model provides production-level control through free-style natural-language instructions and fine-grained inline tags, while supporting 16 languages, 20 Chinese dialect regions, one-pass long-form synthesis up to 3 minutes, and robust generation from noisy, reverberant, or unclear reference speech. Across SEED-TTS-Eval, CV3-Eval, instruction-following, long-form, and acoustic-robustness evaluations, Qwen-Audio-3.0-TTS achieves state-of-the-art performance on many reported dimensions or the strongest aggregate results. It also ranks first on the independent Artificial Analysis Text-to-Speech Leaderboard. These results establish Qwen-Audio-3.0-TTS as a strong foundation for production-level speech synthesis.
Bajian Xiang, Cheng Wen, Han Zhao et al.· 3 citations