Key Takeaways & Executive Findings
- •• • The SCO-BiLSTM model achieves a 10.313% reduction in eMAPE compared with PSO-BiLSTM, directly addressing the local optima stagnation that plagues conventional metaheuristic optimizers in renewable forecasting. This improvement translates to more reliable day-ahead scheduling and reduced imbalance penalties in electricity markets. • • Joint wind-PV power forecasting under SCO-BiLSTM lowers the eRMSE by 27.443% relative to standalone PV power forecasting. This magnitude of error reduction is industrially significant because it enables grid operators to lower reserve margins, thereby decreasing curtailment and improving asset utilization. • • Forecasting accuracy exhibits a monotonic relationship with wind-solar complementarity: stronger complementarity yields higher joint forecasting precision. This empirical threshold effect implies that site selection and portfolio pairing should prioritize negative Pearson correlation coefficients to maximize forecasting gains. • • Extending the forecasting horizon degrades joint forecasting accuracy, establishing a clear operational boundary for the method's applicability. For short-term dispatch (e.g., minutes to hours), the complementarity benefit is maximized; beyond this window, uncertainty propagation dominates and the advantage diminishes.
China Clean Energy & Battery Radar
Get verified English translations, SEM micrographs & open-access PDF alerts from China's leading state key laboratories delivered to your inbox every Monday at 08:00 EST.
Abstract
The inherent spatiotemporal complementarity between wind and solar resources offers a theoretical basis for improving renewable power forecasting accuracy. This study proposes a joint wind-photovoltaic (PV) power forecasting strategy that explicitly exploits this complementarity. A bidirectional long short-term memory (BiLSTM) neural network serves as the baseline forecasting model, and a novel sorting and comparative optimization (SCO) algorithm is developed to optimize the model's hyperparameters. The SCO algorithm ranks individuals in ascending order and compares adjacent fitness values to escape local optima, a known deficiency in conventional metaheuristics such as genetic algorithms and particle swarm optimization. For wind farms and PV plants exhibiting significant complementarity, the joint forecasting strategy first aggregates their power outputs, normalizes the combined signal, and then feeds it into the optimized BiLSTM model. Experimental results demonstrate that the proposed SCO-BiLSTM model reduces the eMAPE by 10.313% compared with PSO-BiLSTM. Furthermore, joint forecasting under SCO-BiLSTM lowers the eRMSE by 27.443% relative to standalone PV power forecasting. The study also establishes that forecasting accuracy improves with stronger wind-solar complementarity but degrades as the forecasting horizon extends. These findings confirm that exploiting complementarity in joint forecasting substantially enhances predictive performance for renewable energy integration.
1. Introduction
Wind and solar power forecasting remains a critical bottleneck for high-penetration renewable integration. Existing commercial approaches predominantly treat wind and PV power as independent time series, ignoring the spatiotemporal complementarity that can smooth aggregate output and reduce uncertainty. This oversight stems from legacy SCADA and forecasting architectures designed for single-source plants, where separate models are trained and dispatched without cross-source information exchange. As a result, forecast errors remain elevated, forcing grid operators to procure costly ancillary services and curtail renewable generation during periods of high uncertainty.
The proposed joint forecasting strategy directly addresses this fragmentation by aggregating wind and PV power outputs from sites with significant complementarity, then forecasting the combined signal using a BiLSTM network optimized by the SCO algorithm. Unlike prior hybrid models that merely concatenate meteorological inputs, this approach exploits the physical smoothing effect of complementarity to reduce the stochasticity of the target series. The SCO algorithm's sorting-and-comparison mechanism provides a deterministic escape from local optima, a failure mode that limits genetic algorithms and particle swarm optimization in high-dimensional hyperparameter spaces. By coupling complementarity-aware data fusion with robust optimization, the method achieves a 27.443% eRMSE reduction over standalone PV forecasting, establishing a new benchmark for joint renewable power prediction.
Loading authentic research manuscript (Pages 1–5)...
YUE Qian, REN Guorui, WANG Wei (2026). Joint Forecasting of Wind and Photovoltaic Power Considering Complementarity. Acta Energiae Solaris Sinica. https://doi.org/10.19912/j.0254-0096.tynxb.202608_9735
Research & Educational Purpose Only: The translations, structured abstracts, analytical annotations, and data reports provided by SinoGreenTechare intended exclusively for academic research, internal corporate R&D, and educational benchmarking. They do not constitute formal engineering, chemical safety, legal, or professional advice.
Copyright & Intellectual Property Notice: Original copyright of the underlying source articles and experimental data remains with the respective authors, institutions, and original publishing journals. SinoGreenTech claims intellectual property only over its proprietary translations, analytical syntheses, and AEO structured enhancements in accordance with international fair use and academic citation principles.
Frequently Asked Questions
What specific failure mechanism of conventional optimizers does the SCO algorithm overcome, and what is the empirical evidence?
Conventional metaheuristics such as genetic algorithms and particle swarm optimization are prone to premature convergence into local optima when optimizing high-dimensional BiLSTM hyperparameters. The SCO algorithm mitigates this by first sorting all individuals in ascending order of fitness, then comparing adjacent individuals to generate the next generation, which creates multiple evolutionary directions. Empirically, SCO-BiLSTM reduces eMAPE by 10.313% compared with PSO-BiLSTM, confirming that the sorting-comparison mechanism effectively escapes local optima and improves forecasting accuracy.
How does the joint forecasting strategy achieve a 27.443% eRMSE reduction, and what are the operational limits?
The reduction arises from aggregating wind and PV power outputs from complementary sites, which smooths the combined power signal and reduces its stochasticity. The BiLSTM model then forecasts this less volatile series, yielding lower errors. However, the benefit is bounded: forecasting accuracy degrades as the forecasting horizon extends, because uncertainty propagation dominates over the smoothing effect. For short-term horizons (minutes to hours), the complementarity gain is maximized; beyond this window, the advantage diminishes, necessitating a trade-off between horizon length and forecast reliability.
What is the quantitative relationship between wind-solar complementarity strength and joint forecasting accuracy?
The study establishes a monotonic relationship: stronger complementarity, quantified by a more negative Pearson correlation coefficient between wind and PV power, yields higher joint forecasting precision. This implies that site pairing should prioritize locations with significant negative correlation. The empirical results show that when complementarity is significant, the joint forecasting error is substantially lower than standalone forecasting, with the eRMSE reduction reaching 27.443% under the SCO-BiLSTM model. This threshold effect provides a clear criterion for selecting wind-PV pairs in hybrid plants.
How does the SCO algorithm's computational cost compare with PSO and GA, and is it scalable for large wind-PV clusters?
The paper does not report explicit computational complexity metrics, but the SCO algorithm's sorting and adjacent comparison operations are O(n log n) per iteration, comparable to standard evolutionary algorithms. The key advantage is not raw speed but solution quality: SCO-BiLSTM achieves a 10.313% eMAPE reduction over PSO-BiLSTM, which translates to fewer iterations needed to reach a target accuracy. For large clusters, the algorithm can be parallelized across individuals, and the deterministic sorting step avoids the stochastic stagnation of PSO, making it scalable for multi-site joint forecasting provided the complementarity pairing is pre-filtered.
What are the data preprocessing requirements for implementing this joint forecasting method in an operational environment?
The method requires rigorous outlier handling using the interquartile range (IQR) method: values above the third quartile plus 1.5 times the IQR or below the first quartile minus 1.5 times the IQR are flagged as anomalies. Rather than deleting these points, linear interpolation fills them to preserve data continuity and integrity. The combined power signal is then normalized before being fed into the BiLSTM model, and outputs are denormalized. Operational deployment must ensure that wind and PV data streams are time-synchronized and that the Pearson correlation coefficient is computed over a representative historical window to confirm significant complementarity before joint forecasting is activated.
Related Chinese Research & Cross-Citations
Wind Turbine Gearbox Fault Diagnosis Method Based on Improved CNN-XGBoost Fusion Model Under Gramian Angular Difference Field
Gearbox failures account for 20–30% of total wind turbine faults and incur maintenance costs equivalent to 10–15% of overall turbine value. Conventional vibration diagnostic pipelines—complementary ensemble empirical mode decomposition with singular value energy spectrum, time-varying filtering empirical mode decomposition, and Teager energy spectrum analysis—remain bounded below 90% accuracy and depend on expert-driven feature engineering that is sensitive to non-stationary operating conditions and noise. This study proposes an intelligent diagnostic architecture that converts one-dimensional gearbox vibration signals into two-dimensional images via Gramian angular difference field (GADF) transformation, preserving intrinsic temporal correlation and time-frequency structure while exploiting matrix sparsity to suppress interference. An improved convolutional neural network (CNN) extracts multi-dimensional features: a convolutional block attention module (CBAM) is embedded in the convolutional layers to weight critical channels and focus on fault-sensitive spatial regions, and a modified βc-ACONC activation function replaces ReLU to mitigate neuron necrosis and enable selective activation. The extracted composite features are then fed into an XGBoost network whose hyperparameters are optimized by an improved sparrow search algorithm (ISSA). Validation on a laboratory wind turbine gearbox dataset yields diagnostic accuracy exceeding 99%, demonstrating robust fault identification capability under complex operating conditions.
Ultra-Short-Term Wind Power Forecasting Based on Fluctuation Continuation Scenario Identification
Existing ultra-short-term wind power forecasting methods exhibit limited performance due to insufficient extraction of fluctuation information and inadequate analysis of evolution patterns. This paper proposes an ultra-short-term wind power forecasting method based on fluctuation continuation scenario identification. First, the coupling mechanism of wind power fluctuations under multiple turbulence processes is investigated, and historical wind power dynamics are decoupled into a combination of nonlinear and linear fluctuation components. A fluctuation continuation concept is introduced, and the future continuation scale of wind power fluctuations is derived from nonlinear and linear decoupling parameters, thereby classifying fluctuation continuation scenarios. A sparse neural network (SNN) oriented to high-dimensional sparse features is constructed to identify historical fluctuation continuation scenarios, and ultra-short-term power forecasting is conducted separately for each scenario. Validation using measured wind speed and power data from three wind farms shows that, compared with baseline models, the proposed model improves root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) by at least 1.46%, 2.44%, and 14.67%, respectively, demonstrating superior accuracy and stability. The method addresses the limitations of signal decomposition techniques that lack physical interpretability and are sensitive to hyperparameters, and overcomes the high-dimensional sparsity challenges faced by traditional scenario identification models.
Multi-Classifier Open Adversarial Network for Rolling Bearing Fault Diagnosis in Wind Turbine Generator Systems
Rolling bearings in wind turbine generator systems operate under variable speed and load conditions that induce significant data distribution shifts between training and field data, while unknown fault modes absent from the source domain are frequently misclassified as known classes. This study proposes a multi-classifier open adversarial network (MCOAN) for open-set fault diagnosis. Within an adversarial domain adaptation framework, a K-way classifier and an additional K+1-way one-vs-all classifier independently estimate target-sample similarity to the source domain. These similarity scores drive a dynamic weighting mechanism that adaptively reweights target samples during open-set adversarial training and supplies per-sample dynamic thresholds for known/unknown discrimination, thereby promoting cross-domain alignment of shared-class features while suppressing negative transfer from unknown samples. A non-adversarial domain classifier is introduced to stabilize dynamic weight estimation. Validation on two datasets demonstrates high-precision shared-class distribution alignment and unknown-class recognition with favorable robustness. The method removes reliance on empirically preset thresholds that plague conventional open-set back-propagation approaches, where a fixed threshold of 0.5 in the binary cross-entropy adversarial loss provides no per-sample adaptivity. By coupling K-way and K+1-way similarity estimates, MCOAN achieves simultaneous known-class alignment and unknown-class separation without prior knowledge of the unknown-class cardinality, addressing a persistent bottleneck in wind turbine drivetrain condition monitoring where unanticipated bearing failure modes emerge under field conditions.
Unsupervised Automated Identification Method for Abnormal States of Wind Turbine Gearboxes
Addressing the scarcity of labeled data for training classification models in wind turbine planetary gearbox anomaly identification, this study proposes an unsupervised automated detection method. Log Mel-band energy features are extracted from raw vibration signals and fed into an unsupervised anomaly recognition model centered on a U-net autoencoder. A health-state threshold is established based on reconstruction error between model input and output, enabling anomaly identification. The method is validated using factory gearbox test data and operational data from a wind farm in Yangtouya, Shanxi. For factory gearboxes, dual validation is performed using a spectrum amplitude modulation-based signal processing method. Results demonstrate that the proposed method achieves 93.34% recognition accuracy on both factory and wind farm test sets, confirming its capability to automatically and correctly separate abnormal wind turbine gearboxes. The approach eliminates reliance on labeled fault data, offering a scalable solution for full-lifecycle health monitoring, from factory acceptance testing to in-service early anomaly detection, adaptable across different operating conditions and turbine models.
Improved Adaptive Super-Twisting Sliding Mode Control for Permanent Magnet Synchronous Motors
Adaptive super-twisting sliding mode control (ASTSMC) for permanent magnet synchronous motors (PMSM) suffers from prolonged convergence and insufficient disturbance rejection under complex operating conditions. This paper proposes an improved ASTSMC incorporating a fixed-time disturbance observer (FTDO). A power term is introduced into the adaptive super-twisting controller to accelerate convergence far from the origin, while the discontinuous sign function is replaced by a continuous h(s) function to mitigate chattering. The FTDO ensures disturbance estimation converges within a fixed time independent of initial states, overcoming the limitations of traditional and finite-time observers. The estimated disturbance is fed forward to the sliding mode controller for compensation, enhancing robustness. The FTDO design is based on an auxiliary state variable z and its derivative, with error dynamics analyzed via Lyapunov stability. Comparative simulations against conventional disturbance observers and sliding mode controllers validate the proposed strategy. The results demonstrate shorter convergence time, improved dynamic performance, and superior disturbance rejection, making the approach suitable for high-performance servo systems. The method addresses the critical need for robust, fast-response control in electric vehicles, rail transit, and aerospace applications where PMSM drives face significant uncertainties and external disturbances.
Data-Model Jointly Driven Fault Diagnosis for Wind Turbine Planetary Gearboxes
Fault diagnosis of wind turbine planetary gearboxes is severely constrained by the scarcity of high-quality fault data, as gearboxes operate predominantly in healthy states and automatic shutdowns prevent fault progression. This paper proposes a data-model jointly driven diagnosis method to address low diagnostic accuracy under limited fault samples. A high-fidelity lumped-parameter dynamic model of the planetary gearbox is constructed to generate pseudo-fault data, supplementing the training set. A domain-shared residual network feature extractor incorporating convolutional block attention modules extracts key physical features from both pseudo and measured data. Local maximum mean discrepancy aligns feature distributions at the fault-category level between pseudo and real fault data. A Kolmogorov-Arnold network module enhances the model's capacity to learn complex data relationships, enabling classification and identification of different fault types. Validation on a wind turbine planetary gearbox fault diagnosis test rig demonstrates that the proposed method achieves superior diagnostic performance under fault sample scarcity compared to classical methods. The framework offers an effective solution for known fault types, though identification of unknown and atypical faults remains a challenge for future work via open-set domain generalization.