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Whale Optimization Algorithm-Enhanced LSTM for Accurate Wind Power Generation Forecasting in Offshore Farms.

Najaad OubeBlika
Volume 5

Abstract

Accurate wind power generation forecasting is a fundamental requirement for the reliable integration of offshore wind farms into modern power systems. Offshore wind energy offers high generation potential due to stronger and more consistent wind resources compared with many onshore locations; however, its output remains highly variable because of complex meteorological dynamics, wake effects, wind speed fluctuations, atmospheric pressure changes, turbulence intensity, marine boundary-layer behavior, and seasonal weather patterns. These uncertainties create operational challenges for grid scheduling, reserve allocation, energy market bidding, storage coordination, and power system stability. Traditional statistical forecasting models and conventional machine learning approaches often struggle to capture nonlinear temporal dependencies and long-range variations in offshore wind power data. To address these limitations, this paper proposes a Whale Optimization Algorithm-enhanced Long Short-Term Memory network for accurate wind power generation forecasting in offshore wind farms.

The proposed framework combines the sequential learning capability of LSTM networks with the global search and exploitation–exploration balance of the Whale Optimization Algorithm (WOA). The LSTM model is employed to learn temporal dependencies from historical wind power generation and meteorological time-series data, including wind speed, wind direction, air temperature, atmospheric pressure, humidity, turbine rotor speed, nacelle orientation, turbulence indicators, and previous power output. WOA is integrated as an intelligent hyperparameter optimization mechanism to automatically tune critical LSTM parameters, including the number of hidden units, learning rate, dropout ratio, batch size, number of recurrent layers, sequence length, optimizer configuration, and regularization coefficients. By replacing manual trial-and-error tuning and exhaustive grid search, the WOA-enhanced strategy improves convergence efficiency, forecasting accuracy, and generalization under fluctuating offshore operating conditions.

A comprehensive preprocessing pipeline is applied to wind farm operational data, including missing value imputation, outlier detection, noise filtering, normalization, temporal alignment, feature selection, and construction of sliding-window input sequences. To enhance robustness, the framework incorporates weather-aware feature engineering, lagged power variables, wind ramp event indicators, seasonal decomposition, and correlation analysis between meteorological variables and generated power. The proposed model can be evaluated using supervisory control and data acquisition data from offshore wind farms, numerical weather prediction outputs, and publicly available renewable energy datasets. Forecasting tasks are formulated across multiple horizons, including ultra-short-term, short-term, and day-ahead wind power prediction, depending on grid operation and market requirements.

Experimental evaluation is conducted using standard forecasting metrics such as Mean Absolute Error, Root Mean Square Error, Mean Absolute Percentage Error, normalized RMSE, coefficient of determination, and forecasting skill score. Comparative analysis is performed against persistence models, ARIMA, support vector regression, random forest, XGBoost, standalone LSTM, GRU, CNN-LSTM, and other metaheuristic-optimized deep learning models. The proposed WOA-LSTM framework is expected to achieve superior forecasting performance by effectively capturing temporal wind power dynamics while optimizing model parameters to reduce prediction error. Particular attention is given to challenging scenarios such as rapid wind ramp events, high-turbulence periods, seasonal transitions, and wake-influenced production variability, where accurate forecasting is crucial for grid reliability.

For practical offshore wind farm management, the forecasting outputs are integrated into decision-support applications for power dispatch scheduling, reserve planning, battery energy storage coordination, preventive maintenance planning, and electricity market participation. In addition, model interpretability is enhanced through feature importance analysis, temporal sensitivity assessment, and error decomposition to identify the meteorological and operational factors most strongly influencing forecasting performance. These analytical components help grid operators and wind farm managers understand prediction behavior and improve operational decision-making.

The proposed WOA-enhanced LSTM framework offers a scalable, accurate, and computationally efficient solution for offshore wind power forecasting. By combining metaheuristic optimization with deep sequential modeling, the system improves forecasting reliability, supports renewable energy integration, reduces operational uncertainty, and contributes to more stable and economical smart grid management. Its applicability to multi-horizon forecasting and offshore wind farm operations makes it a promising tool for next-generation renewable energy management systems.

 

Keywords

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