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Wind power (WP) is a Renewable Energy Source (RES) that has experienced remarkable growth as society continues to seek environmentally friendly energy alternatives. This study primarily explores Predictive Maintenance (PM) of Wind Turbines (WT). It evaluates the potential advantages of Wind Energy (WE) when integrated with the Internet of Things (IoT) and Wireless Sensor Networks (WSN). The research proposes an IoT-WSN framework for PM, structured into three key stages: the initial stage involves data acquisition via sensors; the second stage involves data transmission via an internet connection; and the third stage involves data analytics performed in a cloud computing environment. For PM analytics, this work presents a Predictive Maintenance Convolutional Long Short-Term Memory (PM-C-LSTM) model that integrates the spatial feature-extraction strengths of a Convolutional Neural Network with the time-series modeling capabilities of LSTM networks. The PM-C-LSTM model combines a CNN for spatial pattern detection with LSTM networks for sequential data processing, without compromising the precision of WT-PM. Additionally, a Failure Sample Generator model is incorporated to quantify soft failure and hard failure elements, thereby enhancing the predictive performance of the Machine Learning (ML) approach. The model was deployed on a Wind Farm (WF) on the Qinghai-Tibet Plateau, using data collected over 16 months. Comparative evaluations against several conventional models, based on multiple performance metrics, demonstrate that the PM-C-LSTM model delivers superior PM capabilities. The experimental results suggest that the combined use of IoT and ML has strong potential to advance PM strategies for WT, ultimately contributing to greater efficiency and sustainability in WE production.

Energy efficiency in industrial systems remains a major challenge, as conventional data-driven models are often constrained by limited precision and insufficient data. Integrating the physical laws that govern energy systems can enhance performance and ensure greater physical plausibility; however, such models often struggle with loss computation and often overlook dynamic interactions among subsystems, leading to excessive simplification and reduced real-world utility. Accordingly, this study explores a theoretical framework for constructing a conservation-law-guided neural network to improve energy-efficiency forecasting in industrial processes. The framework embeds physical principles directly into floating nodes built upon a long short-term memory (LSTM) architecture, enabling the model to capture relationships among process variables more effectively. At the same time, gradient aggregation enhances both fluidity and interpretability. Performance was assessed through two large-scale industrial case studies—vinyl chloride monomer production and detergent powder manufacturing—where the proposed model achieved notable gains in prediction accuracy and overall reliability, delivering test prediction improvements of 12.2% and 5.87% compared to previously published approaches. Compared with methods that rely on network architecture modifications, the proposed model demonstrated superior reliability and reproducibility in energy-efficiency forecasts. Furthermore, the model effectively identified energy inefficiencies, resulting in a 4.21% decrease in energy use and a corresponding reduction of 377.35 tons in carbon emissions.