# Hybrid AI Models for Short-Term Photovoltaic Forecasting: A Systematic Review of Architectures, Performance, and Deployment Challenges

## TL;DR

This paper reviews hybrid AI models for predicting solar energy output, highlighting their structures, performance, and challenges in real-world use.

## Contribution

The paper introduces a novel classification of hybrid AI models and identifies key factors affecting their performance and deployment.

## Key findings

- Optimized and decomposition-based hybrids offer the best balance of effectiveness and efficiency.
- Weather and historical PV data are most commonly used inputs for forecasting.
- High computational costs and data quality issues remain major barriers to deployment.

## Abstract

The rapid incorporation of solar energy (PV) systems into electrical grids has increased the demand for accurate short-term forecasts to ensure stability and improve processes. Although hybrid artificial intelligence (AI) models are increasingly being suggested to address this challenge, there is a lack of systematic compilation of their structures, effectiveness, and readiness for use in real-world applications. This paper provides a detailed analysis of 58 peer-reviewed articles (2020–2025) focused on hybrid models for short-term (1–24 h) solar photovoltaic power forecasting. We propose an innovative classification that groups hybrids into four categories: AI-AI (28%), AI with optimization (21%), decomposition-based (17%), and image-based (7%). Our research indicates that weather conditions (34%) and historical photovoltaic energy records (32%) are the most frequent inputs, and that optimized hybrids and those using decomposition achieve the best balance between effectiveness and computational efficiency. From a geographical perspective, the study focuses mainly on the United States (29%) and China (22%), suggesting that more extensive climate validation is crucial. Essentially, we have identified ongoing obstacles to implementation, such as high computational costs, data quality issues, and gaps in interpretation. In addition, we present a plan for future research focusing on hybrid architectures that are lightweight, understandable, and interactive with the grid. This analysis provides a thorough assessment of the current landscape and a strategic framework to guide the creation of operational forecasting systems capable of supporting highly solar-integrated grids.