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

The research team, led by Manisha Galphade and colleagues, started from a deceptively simple observation: the statistical character of wind speed data changes with the seasons. The mean, variance, distribution shape, autocorrelation, and extreme values of wind records all shift as atmospheric conditions move from one regime to another. A single general-purpose model, trained on a full year of data, tends to overfit the turbulent high-wind seasons and underfit the calmer ones. Classical statistical tools such as ARIMA and seasonal ARIMA assume linearity and stationarity, assumptions that wind power data routinely violates. Machine learning methods like random forests and support vector machines capture nonlinear relationships but struggle with long-term temporal dependencies. Even the deep learning tools that have transformed other fields face difficulties when the underlying signal changes its personality every few months.

Transformers, the architecture behind the current artificial intelligence boom, have been proposed as a solution. Models such as the Informer, the Temporal Fusion Transformer, and the Autoformer bring powerful attention mechanisms to time-series forecasting, and they have shown impressive results on long sequences. But the authors point out a practical catch: wind farms rarely produce the enormous, clean datasets that transformers crave. Sensor coverage is limited, measurements are noisy, and self-attention scales quadratically with sequence length, driving up training time and hardware demands. With many hyperparameters to tune and a tendency to overfit small, messy datasets, transformers can be an expensive and fragile choice for operational wind forecasting, particularly at installations that lack cloud-scale computing.

The team’s answer is a serial hybrid that chains two recurrent architectures together. A long short-term memory network, or LSTM, first processes the raw wind power sequence. LSTMs are built around memory cells governed by three gates—an input gate, a forget gate, and an output gate—that control what information is stored, discarded, and passed forward, allowing the network to learn long-range dependencies without suffering from vanishing gradients. The output of this LSTM is then fed into a bidirectional LSTM, or BiLSTM, which reads the refined representations in both forward and backward directions. Because the BiLSTM operates on already-filtered, higher-level features rather than noisy raw signals, the combination performs a kind of progressive feature abstraction: the LSTM extracts stable sequential patterns, and the BiLSTM layers contextual understanding on top of them.

Before any training begins, the raw data undergoes careful preprocessing. Missing values are filled using imputation methods, with a random forest regression imputer chosen for one dataset because it captures nonlinear relationships without assuming a particular data distribution. Outliers are detected by standardizing each data point and flagging values that deviate too far from the mean, and the cleaned features are then rescaled to the range between zero and one using min-max normalization. These steps matter more than they might appear: wind farm records are riddled with sensor dropouts, negative power readings caused by measurement artifacts, and spikes from unusual atmospheric events, and a forecasting model fed uncleaned data will happily learn the noise.

The researchers tested their approach on two real wind farms with very different characters. The first is the Yalova wind farm in western Turkey, a 54,000-kilowatt installation of 36 turbines whose supervisory control and data acquisition system recorded wind speed, direction, generated power, and theoretical power at ten-minute intervals throughout 2018, yielding more than 46,000 records. The second is the Sotavento wind farm in Galicia, Spain, with 24 onshore turbines and a capacity of 17,560 kilowatts, providing hourly meteorological and generation data for 2014. In both cases the data was split by season, with thirty-day windows drawn from winter, spring, summer, and autumn, and the models were evaluated using root mean squared error, mean absolute error, and the coefficient of determination.

The results reveal a striking seasonal fingerprint. At Yalova, spring and summer proved the easiest to forecast, with the hybrid model achieving coefficients of determination as high as 0.99 and its best summer performance at a lookback window of six time steps. Autumn, a transitional season mixing summer and winter behavior, produced moderate errors, while winter was hardest of all: volatility, sudden spikes and drops, and non-stationary behavior pushed the error metrics up and the explanatory power down to roughly 0.80. At Sotavento the same pattern emerged, with the optimal lookback window shifting from five steps in spring to three in summer and just one in autumn. The authors identify this systematic analysis of lookback window optimization as a central contribution, showing that no single window length serves all seasons and that adaptive, season-specific temporal context can substantially improve accuracy.

The hybrid model did not just beat its individual components. Against standalone LSTM and BiLSTM baselines, it reduced mean absolute error by 1.38 percent in spring, 12.1 percent in summer, about 4.67 percent in autumn, and 5.94 percent in winter. It also outperformed temporal convolutional networks, attention-based LSTMs, and transformer models across both datasets, and a Diebold-Mariano statistical test confirmed that most of these improvements were significant at the five percent level, with the gaps against TCN and transformer models highly significant. An ablation study reinforced the design choices: removing either component, reversing the layer order, changing the unit counts, dropping regularization, or altering the learning rate all degraded performance, sometimes dramatically, confirming that the specific architecture and its hyperparameters are genuinely well balanced rather than accidentally lucky.

Perhaps the most persuasive numbers concern efficiency. The proposed model contains just 10,913 parameters, occupies 171 kilobytes, and trained in about 43 seconds—faster than every competing model tested, including the much larger TCN with its 89,473 parameters. Its normalized accuracy of 96.34 percent topped the field, edging out the LSTM at 95.11 percent and the transformer at 92.03 percent. The authors attribute this to a favorable bias-variance trade-off: transformers and attention models carry representational capacity that moderate-sized wind datasets cannot exploit, so their extra parameters mostly buy overfitting risk, while the LSTM’s gating mechanism acts as an inherent noise filter that attention mechanisms sometimes lack. With linear computational complexity in sequence length rather than quadratic, the architecture is well suited to real-time forecasting systems and edge deployments where memory and compute are scarce.

The implications reach well beyond two Spanish and Turkish wind farms. Accurate seasonal forecasting underpins grid planning, reserve operation, and maintenance scheduling, and as wind penetration grows, the cost of forecast error grows with it. The study is candid about its limits: winter remains difficult for every model tested, and the authors suggest that incorporating external meteorological variables, attention mechanisms, and ensemble methods could push performance further. But the core message is a useful corrective to the prevailing enthusiasm for ever-bigger architectures. For seasonal wind power prediction on realistic, medium-sized datasets, a thoughtfully composed pair of recurrent networks—one reading time forward, one reading it both ways—can beat the giants while running on hardware that fits in a wind farm’s back pocket.

Subject of Research: Seasonal wind power forecasting using a hybrid LSTM-BiLSTM deep learning model tested on two wind farms

Article Title: Seasonal wind power forecasting using data-driven and computational intelligence techniques

Article References: Galphade, M., Dande, A., More, N., Nikam, V., & Hatkar, V. (2026). Seasonal wind power forecasting using data-driven and computational intelligence techniques. Results in Engineering, 32, Article 113122. https://doi.org/10.1016/j.rineng.2026.113122

Image Credits: AI Generated

DOI: 10.1016/j.rineng.2026.113122

Keywords: wind power forecasting, LSTM, BiLSTM, deep learning, seasonal variability, renewable energy, time series forecasting, transformer models, grid stability, Yalova wind farm, Sotavento wind farm, computational intelligence