Balancing Accuracy and Efficiency in Wind Power Prediction: A Novel IFE-MLP Approach for Resource-Constrained Environments

Abstract

Achieving sustainable development goals and mitigating the effects of climate change necessitates meeting global energy demand from clean sources. In this context, wind energy emerges as one of the most promising sources. Due to the variable and intermittent nature of wind, accurately predicting the power generated from wind energy systems is crucial for integrating these sources into the grid. While current advanced prediction models can provide this accuracy, disadvantages such as high computational complexity, large memory footprint, and low inference speeds seriously limit the practical applicability of these models in real-time and resource-constrained environments. This study proposes a new and lightweight architecture, the Intelligent Feature-Engineered Minimal Multi-Layer Perceptron (IFE-MLP) model, which is not a variant of transfer learning but rather a feature engineering-based alternative that achieves competitive prediction accuracy without requiring any pre-trained models or knowledge transfer mechanisms. Unlike classical transfer learning paradigms that rely on parameter transfer or domain adaptation, the proposed model achieves competitive prediction accuracy through intelligent feature construction while maintaining high computational efficiency. The performance of the proposed IFE-MLP model has been comprehensively compared with two transfer learning approaches: Fine-Tuning-Based Transfer Learning (FTTL) and Feature-Based Transfer Learning (FBTL). Data obtained from a wind power plant in Bursa, Turkey, was used for performance evaluation. The prediction results revealed that the proposed model exhibited only slightly lower prediction accuracy compared to the FTTL model. However, the main advantage of the proposed IFE-MLP model is clearly evident in terms of computational efficiency and resource utilization. The proposed model achieved its prediction performance using 450 times less memory compared to the FTTL model and 2,050 times less memory compared to the FBTL model. Furthermore, it reduced the training time by 2.2 times by requiring 546 times fewer training Floating Point Operations (FLOPs) and 435 times fewer inference FLOPs, respectively. Inference latency, a critical metric for real-time applications, was measured at 0.05-0.08 ms, corresponding to a 16-100x improvement over the compared models. The conducted decomposition study confirmed that the intelligent feature engineering components in the model architecture provided incremental and meaningful contributions to performance. The findings indicate that the proposed IFE-MLP architecture offers comparable prediction accuracy alongside a significant improvement in resource usage. These characteristics suggest that the model could be a suitable alternative for resource-constrained environments and real-time applications.