Advanced data-driven methods for monitoring solar and wind energy systems

Renewable energy systems, including solar and wind power, are pivotal contributors to tackling global challenges, such as climate change, reducing fossil fuel dependence, and promoting sustainable development. Solar power harnesses the boundless energy radiated by the sun, offering an abundant and environmentally friendly alternative to traditional fossil fuels. Simultaneously, wind energy capitalizes on the kinetic energy of the atmosphere, converting it into electricity through technologically advanced wind turbines. These systems mitigate environmental degradation, curb greenhouse gas emissions, and contribute to resilient energy infrastructures by harnessing clean and abundant natural sources. In the face of increasing global energy needs, the shift towards renewables becomes increasingly critical for ensuring energy security, fostering innovation, and sustaining a cleaner, more equitable future.





The rapid evolution of the renewable energy market has resulted in a substantial surge in demand for photovoltaic systems and wind turbines. Despite their promising potential, the efficacy of these systems can be compromised when faults or cyber-attacks occur. In particular, disruptions in photovoltaic and wind power production can decrease overall energy output. Therefore, the precise detection and identification of faults and the prevention of cyber-attacks are crucial aspects of the operation of photovoltaic plants and wind turbines. Effectively addressing these challenges is essential to ensure that the generated power consistently meets the desired levels, contributing to the reliability and sustainability of renewable energy sources. In this context, advancing fault detection and cybersecurity technologies become paramount for the continued success and widespread adoption of photovoltaic and wind energy systems.


Over the years, operators have faced persistent challenges. Advanced instrumentation, control, and automation generate voluminous data, often unexploited, resulting in the data-rich, information-poor dilemma. Timely analysis and modeling are imperative to extract valuable information that enhances process understanding, enables online prediction, facilitates process monitoring, and supports predictive control. Employing advanced data-driven methods is vital in monitoring, modeling, and fault detection, improving the prediction accuracy and overall performance of these renewable energy systems and supporting the integration of renewable energy in the power grid.




This Research Topic is motivated by the requirements posed in the specifications of the advanced renewable energy systems (wind and solar), and new relevant concepts where machine learning can potentially be a true enabler. Artificial Intelligence (AI) methods, such as machine learning and deep learning, are critical in monitoring and optimizing solar PV and wind energy systems’ performance, reliability, and efficiency. These methods can analyze large amounts of data from the systems and identify patterns and trends that are not immediately apparent to humans. The focus is on innovative contributions related to fault detection, diagnosis, power prediction, condition monitoring, and the application of data-based methods for optimizing these renewable energy systems.



This collection extends the previous volume, “Advanced Data-driven Methods for Monitoring Solar and Wind Energy Systems.” Following a rigorous review process, eight high-quality articles contributed by 39 authors were finally accepted for their contributions to the Research Topic.



In the article “Wind power interval prediction based on variational mode decomposition and the fast gate recurrent unit,” Zhang et al. tackle the challenge of integrating large-scale wind power into the grid due to the uncertainty of wind power. They introduce a wind interval prediction model that incorporates Variational Mode Decomposition (VMD) and the Fast Gate Recurrent Unit (F-GRU), with parameter optimization accomplished via an Improved Whale Optimization Algorithm (IWOA). This approach involves decomposing the wind power series into Intrinsic Mode Function (IMF) components using VMD, constructing an interval prediction model based on lower and upper bound estimation, and optimizing F-GRU parameters with IWOA to derive the final prediction interval. Results based on wind power datasets extracted from a wind farm revealed that the hybrid model VMD-IWOA-F-GRU performs well in wind power prediction.


In the article “Probabilistic prediction of wind power based on improved Bayesian neural network” Deng et al. propose a Bayesian LSTM neural network (BNN-LSTM) constructed on Bayesian networks, incorporating a priori distributions on the LSTM network layer weight parameters. The methodology uses a temporal convolutional neural network (TCNN) to process historical time-series data for wind power prediction, aiming to extract correlation features and capture trend changes in the time-series data. Additionally, the authors apply the mutual information entropy method to analyze meteorological datasets, reducing dimensionality and simplifying the prediction model structure by eliminating variables with low correlation. Simultaneously, the Embedding structure is employed to acquire temporal classification features related to wind power. The final prediction model integrates TCNN-processed time series data, dimensionality-reduced meteorological data, and temporal classification features into the BNN-LSTM. Comparative analysis with Bayesian neural network, continuous interval method, and Temporal Fusion Transformer (TFT) demonstrates that the improved BNN-LSTM yields more accurate responses to wind power fluctuations, resulting in superior prediction outcomes.



The article by Wang and Niu, titled “Wind power output prediction: a comparative study of extreme learning machine,” addresses the need for accurate wind power prediction to mitigate the impact on power systems and alleviate scheduling challenges in wind power-integrated systems. The study introduces an improved tunicate swarm algorithm–extreme learning machine (ITSA-ELM) model, where the improved tunicate swarm algorithm (ITSA) optimizes the random parameters of the extreme learning machine (ELM) for enhanced prediction performance. ITSA overcomes the drawbacks of the tunicate swarm algorithm (TSA) by introducing a reverse learning mechanism, a non-linear self-learning factor, and a Cauchy mutation strategy. The comparative analysis with TSA-ELM demonstrates ITSA-ELM’s superiority, showing a decrease of 1.20% and 21.67% in Mean Absolute Percentage Error (MAPE) in May and December, respectively.

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