To address the low accuracy and poor reliability of short-term photovoltaic (PV) power forecasting under complex weather conditions, this study proposes a multi-strategy synergistic optimization framework for point-interval prediction. The methodology integrates similar day classification (SDC) via RDP-DTW-DBA-K-means, a hybrid BiTCN-MAOBiGRU-AM model with a mutation-aware mechanism, and Dream Optimization Algorithm (DOA) for global hyperparameter tuning of six key parameters. For interval prediction, an adaptive bandwidth kernel density estimation (ABKDE) dynamically adjusts bandwidth based on local error density and weather scenarios. Experiments using data from a Guangxi PV station demonstrate that the synergistic model reduces RMSE by 29.58% (cloudy) and 32.37% (overcast/rainy) versus the baseline, and cuts RMSE by 16.2–19.0% under abrupt weather events and 22.4–40.2% under non-ideal input data. At the 95% confidence level, ABKDE improves prediction interval coverage probability by 3.9–5.4 percentage points and reduces normalized average width by 20.8–23.6% compared to conventional KDE. The proposed framework significantly enhances prediction accuracy, robustness, and generalization, offering a reliable solution for PV power forecasting in highly variable meteorological scenarios.
ABSTRACT Photovoltaic (PV) power forecasting is a foundational capability for operating power systems with high shares of variable renewable generation. However, the methodological landscape is fragmented across physical-based models, statistical time-series approaches, machine learning (ML), and increasingly diverse h...
Jokūbas Jonuška, R. Damaševičius, Diana Belova-Plonienė et al.· International Journal of Gre...· 1 citation
Accurate photovoltaic (PV) power forecasting is essential for enhancing grid stability, optimizing energy management, and facilitating the large-scale integration of renewable energy resources. Although deep learning techniques have demonstrated promising results in PV forecasting, their predictive performance is highl...
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A hybrid PV forecasting framework that combines stacking ensemble learning with a targeted residual correction strategy, and demonstrates that analyzing error distribution and forecasting robustness provides valuable insights beyond conventional aggregate metrics, contributing to the development of more reliable photov...
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To address the challenges of low accuracy and poor generalization in energy time series forecasting, which stem from its nonlinear, non-stationary, and limited-sample-size nature, this study proposes a hybrid forecasting framework. The model was designed to be highly accurate and robust, thereby enhancing the relia...
Mao-Die Luo, Zuo-Min Zhu, Jin-Hai Guo et al.· Grey Systems Theory and Appl...· 0 citations
Short-term photovoltaic (PV) power forecasting faces challenges from strong randomness and non-stationarity. This paper proposes a combined ICEEMDAN–BiLSTM–Attention–IMSO forecasting framework. First, ICEEMDAN decomposes the historical PV sequence into components, which are reorganized into three entropy groups based o...
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Reliable photovoltaic power prediction is increasingly important for integrating variable solar generation into modern electricity systems, yet many operational installations have only limited historical data. This study developed a data-efficient, explainable, and uncertainty-aware machine-learning framework using 412...
