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Hybrid AI Models for Short-Term Photovoltaic Forecasting: A Systematic Review of Architectures, Performance, and
Joan M Saltos1,2, M Gabriela Intriago Cedeño1,2, Ney R Balderramo Velez1,2
1Department of Electrical Engineering, University of Jaén, 23071 Jaen, Spain.
Sensors (Basel, Switzerland)
|March 28, 2026
Summary
Hybrid artificial intelligence (AI) models enhance short-term solar power forecasting for grid stability. Optimized and decomposition-based hybrids offer the best performance, but implementation challenges remain.
Area of Science:
- Renewable Energy Systems
- Artificial Intelligence in Energy
- Power Systems Engineering
Background:
- Increasing solar photovoltaic (PV) integration necessitates accurate short-term forecasting for grid stability.
- Existing research lacks a systematic overview of hybrid AI models for PV power prediction.
Purpose of the Study:
- To systematically analyze hybrid AI models for short-term (1-24h) solar PV power forecasting.
- To classify existing hybrid models and assess their readiness for real-world application.
Main Methods:
- Comprehensive literature review of 58 peer-reviewed articles (2020-2025).
- Development of a novel classification system for hybrid models (AI-AI, AI with optimization, decomposition-based, image-based).
- Analysis of input data, geographical focus, performance metrics, and implementation challenges.
Main Results:
- Hybrid models are categorized into AI-AI (28%), AI with optimization (21%), decomposition-based (17%), and image-based (7%).
- Weather conditions (34%) and historical PV data (32%) are primary inputs.
- Optimized and decomposition-based hybrids show the best effectiveness-efficiency balance.
Conclusions:
- Key implementation barriers include computational costs, data quality, and interpretability.
- Future research should focus on lightweight, interpretable, and grid-interactive hybrid architectures.
- This study provides a strategic framework for developing operational forecasting systems for solar-integrated grids.
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