Work

Research on Photovoltaic Long-Term Power Prediction Model Based on Superposition Generalization Method

Year: 2025

Type: article

Abstract: The integration of renewable energy sources, specifically photovoltaic generation, into the grid at a large scale has significantly heightened the volatility and unpredictability of the power system. Consequently, this presents formidable challenges to ensuring the reliable operation of the grid. This study introduces a novel stacked model for photovoltaic power prediction, integrating multiple conventional data processing methods as base learners, including Group Method of Data Handling (GMDH), Least Squares Support Vector Machine (LSSVM), Radial Basis Function Neural Network (RBFNN), and Emotional Neural Network (ENN). A Backpropagation Neural Network (BPNN) serves as the meta-learner, utilizing the outputs of the base learners as input features to enhance overall prediction accuracy by mitigating individual model errors. To assess the model’s effectiveness, five evaluation metrics are employed: Bayesian Information Criterion (BIC), Percent Mean Average Relative Error (PMARE), Legates and McCabe Index (LM), Mean Absolute Deviation (MAD), and Root Mean Square Error (RMSE), ensuring long-term stability in photovoltaic power output forecasting. Additionally, the model’s effectiveness and accuracy are validated using operational data from photovoltaic power plants in a particular province of China. The results indicate that the stacked model, after training, testing, and validation on multiple performance metrics, surpasses baseline single models in performance.

Source: Processes

DOI: 10.3390/pr13051263

Authors: Yun Chen, Jilei Liu, Bei Liu, Shipeng Liu, Dongdong Zhang

Institutions: State Grid Corporation of China (China), Nanjing Institute of Technology

Language: English

Topic: Solar Radiation and Photovoltaics

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

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

Open Access status: gold

Estimated APC paid: $2,896