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