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Deep Learning-Enabled Solar Irradiance Forecasting Using Spatiotemporal Attention Mechanisms for Smart Grid Management.

Najaad OubeBlika
Volume 1

Abstract

Solar irradiance forecasting is a critical component of modern renewable energy management, particularly as photovoltaic power generation becomes increasingly integrated into smart grids, microgrids, and distributed energy systems. The intermittent and weather-dependent nature of solar energy introduces significant uncertainty into power generation, affecting grid stability, energy dispatch, reserve scheduling, battery storage management, and demand–supply balancing. Conventional statistical forecasting models and traditional machine learning methods often struggle to capture the nonlinear, non-stationary, and highly dynamic behavior of solar irradiance under rapidly changing meteorological conditions such as cloud movement, aerosol variation, humidity fluctuation, and seasonal transitions. To address these challenges, this paper proposes a deep learning-enabled solar irradiance forecasting framework based on spatiotemporal attention mechanisms for accurate and reliable smart grid management.

The proposed framework integrates heterogeneous data sources, including historical global horizontal irradiance, direct normal irradiance, diffuse horizontal irradiance, ambient temperature, humidity, wind speed, atmospheric pressure, cloud cover, solar zenith angle, satellite imagery, sky-camera observations, and photovoltaic generation records. A comprehensive preprocessing pipeline is applied to handle missing meteorological measurements, normalize continuous features, remove outliers, align multi-resolution temporal data, and extract spatial contextual information from geographically distributed solar monitoring stations. The forecasting problem is formulated as a multi-horizon time-series prediction task, supporting very-short-term, short-term, and day-ahead solar irradiance forecasts required for real-time grid operation and energy planning.

The core model combines convolutional layers, recurrent neural networks, and spatiotemporal attention modules to learn both local weather-driven patterns and long-range temporal dependencies. Convolutional components extract spatial features from satellite or sky-image data, while LSTM, GRU, or temporal transformer layers model sequential dependencies in irradiance and meteorological time series. The spatiotemporal attention mechanism dynamically assigns higher weights to the most relevant time intervals, weather variables, and neighboring spatial locations, enabling the model to focus on critical factors such as moving cloud fields, abrupt irradiance drops, seasonal patterns, and regional weather correlations. This attention-guided design improves forecasting accuracy, robustness, and interpretability compared with conventional deep learning architectures that treat all temporal and spatial inputs uniformly.

The proposed model can be evaluated using publicly available solar and meteorological datasets such as NREL solar resource data, NASA POWER data, local photovoltaic plant measurements, and satellite-derived irradiance datasets. Performance is assessed using forecasting metrics including Root Mean Square Error, Mean Absolute Error, Mean Absolute Percentage Error, normalized RMSE, coefficient of determination, prediction interval coverage probability, and computational latency. Comparative experiments are conducted against persistence models, ARIMA, support vector regression, random forest, XGBoost, standalone LSTM, CNN-LSTM, and transformer-based baselines. The proposed spatiotemporal attention model is expected to achieve improved forecasting performance, particularly under unstable cloudy conditions and high-variability weather scenarios where accurate prediction is most valuable for grid reliability.

For smart grid management, the forecasting outputs are integrated into decision-support modules for photovoltaic power scheduling, battery energy storage control, demand response, reserve allocation, and grid congestion mitigation. The model also supports uncertainty-aware forecasting by producing prediction intervals that allow grid operators to quantify forecast confidence and plan operational reserves more effectively. Furthermore, attention visualization is incorporated to provide interpretability by identifying the meteorological variables, spatial regions, and time steps that contribute most strongly to each forecast. This transparency enhances operator trust and supports the practical deployment of artificial intelligence-based forecasting systems in energy control centers.

The proposed framework offers a scalable, accurate, and interpretable solution for solar irradiance forecasting in smart grid environments. By combining deep learning with spatiotemporal attention mechanisms, the system can improve photovoltaic integration, reduce renewable energy curtailment, optimize storage utilization, and enhance the reliability and economic efficiency of grid operations. Its ability to process multi-source meteorological and spatial data makes it particularly suitable for next-generation renewable energy management systems, where accurate forecasting is essential for achieving secure, flexible, and sustainable power system operation.

Keywords

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