Work

Ultra-Short-Term Multi-Step Photovoltaic Power Forecasting Based on Similarity-Based Daily Clustering

Year: 2025

Type: article

Abstract: Photovoltaic (PV) power generation is inherently intermittent and volatile, complicating power system operation and control. Accurate forecasting is crucial for proactive grid responses and optimal energy resource scheduling. This study proposes a novel hybrid forecasting model that achieves high-precision PV power forecasting by integrating similar-day clustering, generating extreme weather samples, and optimizing the Bidirectional Temporal Convolutional Network (BiTCN) and Bidirectional Gated Recurrent Unit (BiGRU) model via the Animated Oat Optimization (AOO) algorithm. The proposed method outperforms other models in the three evaluation metrics of mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). The innovations lie in the integration of similar-day clustering with deep learning and the application of AOO for hyperparameter optimization, which significantly enhances forecasting accuracy and robustness.

Source: Energies

DOI: 10.3390/en19010029

Authors: Yongcheng Jin, Zhichao Sun, Dongliang Lv, Weicheng Gao, Fengze Liu, Qinghua Yu

Institution: Wuhan University of Technology

Language: English

Topic: Solar Radiation and Photovoltaics

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDGs: Affordable and clean energy, Climate action, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $3,137