EFFICIENT METHOD FOR FORECASTING SOLAR IRRADIANCE - A REVIEW
Abstract
Efficient solar irradiance forecasting is essential for optimizing solar energy systems and integrating renewable energy sources into power grids. This review aims to evaluate the effectiveness of various forecasting methods to inform energy management and grid integration strategies. It compares physical models, statistical approaches, machine learning techniques, and hybrid models, using specific criteria such as accuracy, computational efficiency, and data requirements. Physical models like Numerical Weather Prediction (NWP) provide detailed atmospheric simulations but are computationally intensive. Statistical models, such as ARIMA, are simpler yet struggle with non-linear data. Machine learning methods, particularly Artificial Neural Networks (ANNs) and Long Short-Term Memory (LSTM) networks, effectively capture complex data relationships but require substantial datasets and computing power. Hybrid models, which combine physical and machine learning approaches, achieve high accuracy and are suitable for real-time applications despite increased computational costs. One of the key findings indicates that hybrid models offer high accuracy but demand significant computational resources and offer the best balance between accuracy and computational efficiency. However, challenges such as data quality, geographic and temporal variability, and model complexity persist. Emerging technologies like artificial intelligence, big data, and quantum computing present promising solutions for enhanced irradiance forecasting. The review highlighted the models’ limitations and strengths to facilitate informed decision making and concluded with recommendation of the adoption of hybrid models, investment in data acquisition and sharing technologies, balancing model complexity with practicality as steps towards improved irradiance forecasting for grid integration and stability to ensure sustainable yet cost-effective energy solutions.
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