AutoPV: An intelligent framework for automated design of photovoltaic forecasting models

作者：Dayin Chen, Xiaodan Shi, Mingkun Jiang, Shibo Zhu, Haoran Zhang, Dongxiao Zhang, Yuntian Chen, Jinyue Yan · 发表于：Applied Energy · 年份：2026 · DOI：10.1016/j.apenergy.2026.127851 · 被引用次数：2 · 研究领域：Energy Load and Power Forecasting、Solar Radiation and Photovoltaics

Photovoltaic power forecasting (PVPF) is a critical area in time series forecasting (TSF), enabling the efficient utilization of solar energy. With advancements in machine learning and deep learning, various models have been applied to PVPF tasks. However, constructing an optimal predictive architecture for specific PVPF tasks remains challenging, as it requires cross-domain knowledge and significant labor costs. To address this challenge, we introduce AutoPV, a novel framework for the automated search and construction of PVPF models based on neural architecture search (NAS) technology. We develop a brand new NAS search space that incorporates various data processing techniques from state-of-the-art (SOTA) TSF models and typical PVPF deep learning models. The effectiveness of AutoPV is evaluated on diverse PVPF tasks using a dataset from the Daqing Photovoltaic Station in China. Experimental results demonstrate that AutoPV can complete the predictive architecture construction process in a relatively short time, and the newly constructed architecture is superior to SOTA predefined models. This work bridges the gap in applying NAS to TSF problems, assisting non-experts and industries in automatically designing effective PVPF models.