A Review of Solar Forecasting Techniques and the Role of Artificial Intelligence

Abstract

Solar energy forecasting is essential for the effective integration of solar power into electricity grids and the optimal management of renewable energy resources. Distinguishing itself from the existing literature, this review study provides a nuanced contribution by centering on advancements in forecasting techniques. While preceding reviews have examined factors such as meteorological input parameters, time horizons, the preprocessing methodology, optimization, and sample size, our study uniquely delves into a diverse spectrum of time horizons, spanning ultrashort intervals (1 min to 1 h) to more extended durations (up to 24 h). This temporal diversity equips decision makers in the renewable energy sector with tools for enhanced resource allocation and refined operational planning. Our investigation highlights the prominence of Artificial Intelligence (AI) techniques, specifically focusing on Neural Networks in solar energy forecasting, and we review supervised learning, regression, ensembles, and physics-based methods. This showcases a multifaceted approach to address the intricate challenges associated with solar energy predictions. The integration of Satellite Imagery, weather predictions, and historical data further augments precision in forecasting. In assessing forecasting models, our study describes various error metrics. While the existing literature discusses the importance of metrics, our emphasis lies on the significance of standardized datasets and benchmark methods to ensure accurate evaluations and facilitate meaningful comparisons with naive forecasts. This study stands as a significant advancement in the field, fostering the development of accurate models crucial for effective renewable energy planning and emphasizing the imperative for standardization, thus addressing key gaps in the existing research landscape.

Anomaly Identification for Photovoltaic Power Stations Using a Dual Classification System and Gramian Angular Field Visualization

“…Accurate photovoltaic generation forecasting is an important feature that can assist utilities and plant operators in the direction of energy management and dispatchability planning [11]. Approaches combining the weather data, PV station's physical parameters, historical generation data, and other conditions would be prioritized, then conditional factors, other than the factors of the photovoltaic power station, can be reflected in the prediction timing curve [12,13]. Digital twin technology utilizes interactive simulations among physical entities, sensors, and historical databases to establish a high-fidelity virtual mapping of real-world equipment.…”

Artificial intelligence applications in solar energy

“…Fig. 3 Application framework of soft computing in solar energy [109] Support vector machines (SVMs) are highly effective supervised learning models for classification and regression problems. SVMs work by feeding input data into a highdimensional feature space and identifying the optimum hyperplanes separating classes or showing continuous results by maximizing margins between classes; kernel functions have been employed to manage nonlinear relationships [110]- [112].…”