Advances in Short-Term Solar Forecasting: A Review and Benchmark of Machine Learning Methods and Relevant Data Sources

Abstract

Solar forecasting is becoming increasingly important due to the exponential growth in total global solar capacity each year. More photovoltaic (PV) penetration in the grid poses problems for grid stability due to the inherent intermittent and variable nature of PV power production. Therefore, forecasting of solar quantities becomes increasingly important to grid operators and market participants. This review presents the most recent relevant studies focusing on short-term forecasting of solar irradiance and PV power production. Recent research has increasingly turned to machine learning to address this challenge. The paper provides a discussion about building a solar forecasting model, including evaluation measures and machine learning method selection through analysed literature. Given that machine learning is data-driven, the focus of this review has been placed on data sources referenced in the literature. Open-access data sources have been compiled and explored. The main contribution of this paper is the establishment of a benchmark for assessing the performance of solar forecasting models. This benchmark utilizes the mentioned open-source datasets, offering a standardized platform for future research. It serves the crucial purpose of streamlining investigations and facilitating direct comparisons among different forecasting methodologies in the field of solar forecasting.

From home energy management systems to energy communities: methods and data

“…The interested reader can consult, for instance, the works of Alcañiz et al . 33 or Pandžić and Capuder paper 34 , and the references within. Forecasting PV power will also need the forecasting of atmospheric variables, such as solar irradiation (please see El-Amarty et al .…”

High-Accuracy Photovoltaic Power Prediction under Varying Meteorological Conditions: Enhanced and Improved Beluga Whale Optimization Extreme Learning Machine

“…Finally, the trained model is used to predict photovoltaic power. Although machine learning models have many advantages in the field of PV power prediction, model training and testing are sensitive to the setting of random parameters [20]. Ref.…”

A Combined Persistence and Physical Approach for Ultra-Short-Term Photovoltaic Power Forecasting Using Distributed Sensors

“…Short-term forecasting also enhances the security of grid operations by enabling grid operators to anticipate changes in demand and take proactive measures to maintain stability and reliability. Enhanced ultra-short-term PV forecasting increasingly utilizes machine learning methods, including CNN models, to integrate meteorological data, aligning with our focus on predictive accuracy in immediate timeframes [7,15]. Furthermore, the integration of IoT sensor data into forecasting models presents an innovative approach to enhancing prediction accuracy.…”

“…Nowcasting and ultrashort-term forecasts are particularly important for managing the variability of renewable energy generation and maintaining grid stability. Despite the critical importance of accurate and efficient forecasting methods for PV power output, the existing literature, including recent reviews, predominantly focuses on models that require extensive computational resources or rely heavily on historical data, which may not always be available or accurate for all locations [7]. Various forecast models have been developed to predict solar power production under different weather conditions, including persistence models, physical models, and a combination of both.…”