Short-Term Prediction of Rural Photovoltaic Power Generation Based on Improved Dung Beetle Optimization Algorithm

Sustainability, Journal Year: 2024, Volume and Issue: 16(13), P. 5467 - 5467

Published: June 27, 2024

Addressing the challenges of randomness, volatility, and low prediction accuracy in rural low-carbon photovoltaic (PV) power generation, along with its unique characteristics, is crucial for sustainable development energy. This paper presents a forecasting model that combines variational mode decomposition (VMD) an improved dung beetle optimization algorithm (IDBO) kernel extreme learning machine (KELM). Initially, Gaussian mixture (GMM) used to categorize PV data, separating analogous samples during different weather conditions. Afterwards, VMD applied stabilize initial sequence extract numerous consistent subsequences. These subsequences are then employed develop individual KELM models, their nuclear regularization parameters optimized by IDBO. Finally, predictions from various aggregated produce overall forecast. Empirical evidence via case study indicates proposed VMD-IDBO-KELM achieves commendable across diverse conditions, surpassing existing models affirming efficacy superiority. Compared traditional VMD-DBO-KELM algorithms, mean absolute percentage error on sunny days, cloudy days rainy reduced 2.66%, 1.98% 6.46%, respectively.

Language: Английский

Short-Term Power Load Forecasting Based on CEEMDAN-WT-VMD Joint Denoising and BiTCN-BiGRU-Attention

Electronics, Journal Year: 2025, Volume and Issue: 14(9), P. 1871 - 1871

Published: May 4, 2025

Short-term power load forecasting is crucial for safe grid operation. To address the insufficiency of traditional decomposition methods in suppressing high-frequency noise within multi-source noisy time series, this study proposes a hybrid model integrating CEEMDAN-WT-VMD joint denoising with BiTCN-BiGRU-Attention architecture. The methodology comprises three stages: (1) CEEMDAN raw data to mitigate mode mixing and extract stationary IMF components; (2) wavelet threshold filter interference while preserving reconstructing low-frequency signals; (3) secondary feature using Variational Mode Decomposition (VMD) enhance stability. A architecture combines Bidirectional Temporal Convolutional Network (BiTCN) long-term dependency capture, Gated Recurrent Unit (BiGRU) dynamic extraction, an attention mechanism key pattern emphasis. final value generated by progressively accumulating predictions decomposed components. Empirical analysis based on from region Australia demonstrates that, through horizontal vertical comparative experiments, proposed method significant improvements both accuracy stability compared other frontier models.

Language: Английский

Enhancing Short-Term Load Forecasting Accuracy in High-Volatility Regions Using LSTM-SCN Hybrid Models

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(24), P. 11606 - 11606

Published: Dec. 12, 2024

Short-Term Load Forecasting (STLF) is essential for the efficient management of power systems, as it improves forecasting accuracy while optimizing scheduling efficiency. Despite significant recent advancements in STLF models, high-volatility regions remains a key challenge. To address this issue, paper introduces hybrid load model that integrates Long Memory Network (LSTM) with Stochastic Configuration (SCN). We first verify Universal Approximation Property SCN through experiments on two regression datasets. Subsequently, we reconstruct features and input them into LSTM feature extraction. These extracted vectors are then used inputs SCN-based STLF. Finally, evaluate performance LSTM-SCN against other baseline models using Australian Electricity dataset. also select five test set to validate model’s advantages such scenarios. The results show achieved an RMSE 56.970, MAE 43.033, MAPE 0.492% set. Compared next best model, reduced errors by 6.016, 8.846, 0.053% RMSE, MAE, MAPE, respectively. Additionally, consistently outperformed across all analyzed. findings highlight its contribution improved system management, particularly challenging

Language: Английский