The study, published in Neural Computing and Applications, was conducted by R. J. Kuo and Yun-Tung Ko, who set out to tackle a problem that has long resisted conventional approaches. Classical statistical tools such as Auto-Regressive Moving Average models assume that the underlying data series behaves in a stable, consistent manner, an assumption that volatile wind speeds routinely violate. Deep learning models like Long Short-Term Memory networks, or LSTMs, have fared far better because their gating mechanisms allow information to persist across long stretches of time, but even these architectures have limits when faced with the non-stationary, noisy signals that characterize real wind farms.

The researchers’ solution layers three innovations on top of one another. First, they applied Variational Mode Decomposition, a signal-processing technique that breaks a turbulent wind power series into several narrow, smoother sub-components called intrinsic mode functions. Unlike earlier decomposition methods such as Empirical Mode Decomposition, VMD manages the bandwidth of each mode and incorporates Wiener filtering, which suppresses noise and avoids the spectral aliasing that can corrupt downstream predictions. Kuo and Ko did not simply accept default settings: they systematically selected the number of modes by minimizing an index of orthogonality, ensuring the decomposed components did not overlap, and chose the penalty factor by checking how faithfully the sum of the modes reconstructed the original signal.

Second, the team built a forecasting engine that fuses two complementary deep learning architectures. The first is a Temporal Convolutional Network, a convolutional design that uses dilated causal convolutions stacked in residual blocks. The dilation structure lets the network look far back into the historical record without piling on depth, and because convolutions can be computed in parallel, TCNs train faster and use less memory than recurrent alternatives. The second component is a Nested LSTM, a newer variant in which the memory cell of a standard LSTM is itself replaced by another full LSTM cell. This hierarchy allows the network to selectively access deeper memory, retaining and organizing information over extended periods, which is precisely the kind of capability needed to track slow-moving weather patterns superimposed on rapid fluctuations.

Rather than choosing between these two architectures, the researchers combined them. The TCN extracts local temporal features through its convolutional operations while the NLSTM captures long-range sequential dependencies. The output features from both networks are concatenated and passed through a single-layer artificial neural network that acts as a fusion layer, enabling nonlinear interaction between the two representations before the final forecast is issued. Earlier work had suggested that simply bolting a TCN onto an LSTM does not fully exploit what the hidden layers learn; the shared fusion layer in the new design was conceived specifically to address that shortcoming. The model also ingests weather variables alongside power data, with features such as wind speed, temperature, and solar radiation screened by mutual information scores that capture both linear and nonlinear relationships with the target series.

The third innovation concerns how the model is tuned. Deep architectures are notoriously sensitive to their hyperparameters, and the population-based optimizers commonly used to set them, including the Genetic Algorithm, can get trapped in local minima or converge prematurely. The authors turned to the Driving Training-Based Optimization algorithm, a recently proposed metaheuristic inspired by how student drivers learn: first from an instructor, then by imitating skilled drivers, and finally through personal practice. In each iteration, the algorithm evaluates candidate solutions, promotes the best performers to the role of trainers, and lets the rest of the population learn from them, with the pool of trainers gradually shrinking as the search progresses.

Kuo and Ko’s key twist was to inject mutation into this driving metaphor. Borrowing from evolutionary computation, they added a mixed mutation strategy in which Gaussian, Cauchy, power, or single-point mutations are applied to candidate solutions with equal probability. The purpose is to maintain diversity within the population and give the search a way to escape local optima, especially in later iterations when a standard optimizer would settle into a rut. The single-point mutation, in particular, alters only one component of a candidate’s position, echoing the idea that a learner might refine a single driving skill rather than overhauling everything at once. The objective function guiding the whole process was simple: minimize the Root Mean Square Error between forecast and actual wind power.

To test the framework, the team used five years of daily data from 2018 to 2022, covering 1,826 days from two European electricity bidding zones, DK2 and DE. Power generation figures came from the ENTSO-E Transparency Platform and weather data from Visual Crossing. After cleaning missing values, converting wind direction to radians to eliminate the discontinuity between 0 and 360 degrees, and normalizing all features to the zero-to-one range, the researchers split the data so that four years trained the models and the final year tested them. Every experiment was repeated thirty times to account for the natural variability of neural network training, and the hybrid TCN-NLSTM consistently produced lower average errors and smaller standard deviations than standalone TCN, NLSTM, or ARMA models.

The optimizer comparisons proved equally decisive. When the same hybrid architecture was paired with the Genetic Algorithm, the original DTBO, and the mutation-enhanced version, the improved DTBO delivered the lowest RMSE on both datasets with the greatest stability across repeated runs. Convergence curves showed DTBO-based methods improving rapidly at first and continuing to refine after ten iterations, with the enhanced variant exhibiting a characteristic zigzag pattern that signaled ongoing exploration rather than stagnation. Statistical testing backed up the visual evidence: a Kruskal-Wallis test confirmed significant differences among the compared models, and pairwise Mann-Whitney U-tests showed the hybrid architecture significantly outperforming ARMA, NLSTM, and TCN, with all p-values below 0.05. The gap between DTBO and its mutation-enhanced cousin, however, proved dependent on the dataset at hand.

The authors are candid about the framework’s limits. The current study forecasts only one day ahead with a fixed input window, and the fused architecture carries more computational weight than a single network, which could complicate deployment in resource-constrained, real-time settings. Some parameters were also fixed from prior literature or pilot experiments rather than optimized adaptively. Still, the consistent performance across two independent real-world datasets suggests the approach could find practical use in wind energy management and broader renewable integration efforts. As wind and solar are expected to account for roughly 95 percent of capacity expansion among clean energy sources according to the International Energy Agency, tools that shrink the fog of uncertainty around generation are likely to become as vital to the grid as the turbines themselves.

Subject of Research: Hybrid machine learning and optimization for short-term wind power forecasting

Article Title: Using hybrid machine learning model with an improved driving training-based optimization algorithm for wind power forecasting

Article References: Kuo, R. J., & Ko, Y.-T. (2026). Using hybrid machine learning model with an improved driving training-based optimization algorithm for wind power forecasting. Neural Computing and Applications, 38(17), Article 723. https://doi.org/10.1007/s00521-026-12401-8

Image Credits: AI Generated

DOI: 10.1007/s00521-026-12401-8

Keywords: wind power forecasting, machine learning, Temporal Convolutional Network, Nested LSTM, Variational Mode Decomposition, Driving Training-Based Optimization, metaheuristics, mutation operator, renewable energy, hyperparameter tuning, deep learning, smart grids