Prediction Interval in Renewable Energy Forecasting: A Comprehensive Review of Uncertainty Quantification Methods
Nazmus Sakib, Mohammad Anwar Hosen, Burhan Khan, Bruce Gunn, Michael Johnstone
Abstract
Robust and reliable forecasting of renewable energy generation is essential for managing grid stability, optimising energy resources, and meeting global climate targets, such as the European Union’s goal of achieving Net-Zero greenhouse gas emissions by 2050. Renewable energy sources, including solar, wind, and tidal, are inherently variable and difficult to predict, while hydro is comparatively more stable but still subject to seasonal and climatic fluctuations. This variability poses significant challenges to grid operators and policymakers striving to integrate more renewable energy into the power system. Prediction Interval (PI), which quantifies uncertainty by providing a probabilistic range of potential outcomes, offer a solution to this problem by enhancing the reliability and accuracy of renewable energy forecasts. This paper reviews the methodologies for estimating prediction interval, including statistical, machine learning, and deep learning approaches, and demonstrates how these techniques can address the key challenges in renewable energy forecasting. By offering a structured comparison of prediction interval estimation methods across different renewable energy sectors, the paper highlights their potential to reduce uncertainty, improve decision-making for grid integration, and assist policymakers in developing strategies to mitigate risks from energy variability. Moreover, the review identifies critical gaps in current research and proposes future directions that could further refine Prediction Interval estimation methods for greater accuracy and applicability. The insights provided in this review can aid scientists, engineers, and decision-makers in advancing renewable energy forecasting techniques, thereby accelerating the transition toward a more sustainable and resilient energy system.