Inventions 2026, 11(1), 15; https://doi.org/10.3390/inventions11010015 - 5 Feb 2026
Abstract
Accurate and trustworthy forecasting of wind and photovoltaic power generation is essential for the reliable operation and planning of modern power systems. Although recent machine-learning-based forecasting solutions increasingly incorporate elements of trustworthy artificial intelligence, such as explainability, uncertainty quantification, robustness, drift monitoring, and
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Accurate and trustworthy forecasting of wind and photovoltaic power generation is essential for the reliable operation and planning of modern power systems. Although recent machine-learning-based forecasting solutions increasingly incorporate elements of trustworthy artificial intelligence, such as explainability, uncertainty quantification, robustness, drift monitoring, and machine learning operations, these components are typically introduced in a fragmented manner and remain weakly integrated at the architectural level, which limits their applicability in real operational environments. This paper presents a systematic review of 59 peer-reviewed journal articles published between 2019 and 2025, conducted in accordance with the PRISMA 2020 guidelines. The review includes studies focused on wind and photovoltaic power forecasting that report system architectures, frameworks, or end-to-end pipelines incorporating at least one trust-related attribute. The literature search was performed using Scopus, IEEE Xplore, MDPI, and ScienceDirect. Using a narrative and architectural synthesis, the review identifies six structural gaps hindering industrial deployment: the absence of semantic data models, shallow model-centric explainability, drift monitoring without governance mechanisms, lack of automated model lifecycle management, insufficient robustness to real-world data defects, and the absence of integrated end-to-end architectures. The evidence base is limited by the heterogeneity of architectural descriptions and the predominantly qualitative nature of reported implementations. Based on these findings, a high-level reference architecture for a trustworthy AI-based forecasting system is proposed. The architecture formalizes trustworthiness as a system-level property and integrates semantic, technological, and functional trust layers within a unified data and model lifecycle, supporting reproducible, interpretable, and operationally reliable forecasting for both wind and photovoltaic power plants.
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(This article belongs to the Special Issue Emerging Trends and Innovations in Renewable Energy)
