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Published Research
AI-Driven Forecasting: Deep Learning-Based Predictive Modeling of Wind Power Output to Optimize Clean Energy Supply
Wind Energy · LSTM · Predictive Modeling
Abstract
As global warming poses an escalating threat to global public health and ecological stability, the transition to renewable energy has become a critical imperative for large-scale carbon mitigation. Wind energy represents one of the most cost-effective and sustainable alternatives within the current energy portfolio; however, it currently accounts for only 10% of total U.S. electricity generation. This underutilization is primarily driven by the inherent intermittency of wind resources, which rely on highly variable environmental factors such as wind speed, atmospheric pressure, and ambient temperature. While utility providers increasingly pivot toward wind power to minimize operational costs, the lack of reliable forecasting creates a significant discrepancy between theoretical potential and actual grid integration, leading to inefficiencies in load balancing.
To address this reliability gap, this study presents a robust deep-learning framework utilizing Long Short-Term Memory (LSTM) networks to optimize short-term wind power generation forecasting. The choice of LSTM is foundational, as wind power data is inherently a time series characterized by strong temporal dependencies and non-stationary patterns. Unlike standard neural networks, LSTMs utilize a specialized gating mechanism to capture both short-term fluctuations, such as sudden wind gusts, and long-term periodicities like diurnal and seasonal cycles. By modeling the "memory" of previous atmospheric states, the network can account for the temporal inertia present in weather systems, which is critical for reducing prediction lag.
The research methodology integrates three primary phases: comprehensive data preprocessing and feature engineering, the development of a hybrid neural network architecture combining LSTM layers for temporal sequence modeling with Dense layers for non-linear mapping, and a rigorous performance evaluation using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE).
The proposed model achieved superior predictive accuracy, yielding an RMSE of 0.01006 and an MAE of 0.00634. These results represent a 73.06% and 64.23% improvement, respectively, over existing high-performing LSTM-based benchmarks trained on the same dataset. Furthermore, the model demonstrated exceptional computational efficiency with a total training duration of 4.02 minutes, a 30.12% reduction in latency compared to the baseline model's 5.75-minute training time. By providing a precise, computationally efficient forecasting tool, this research enables electrical utility companies to more accurately anticipate supply fluctuations. This advancement facilitates the maximization of renewable energy dispatch and the reduction of carbon-intensive reserve power, ultimately fostering a more resilient and sustainable global energy infrastructure.