# GCN-Informer: A Novel Framework for Mid-Term Photovoltaic Power Forecasting
###### Abstract  
Predicting photovoltaic (PV) power generation is a crucial step in the field of clean energy. Achieving high-accuracy PV power prediction requires addressing two challenges in current deep learning methods: (1) In photovoltaic power generation prediction, traditional deep learning methods often generate predictions for long sequences one by one, significantly impacting the efficiency of model predictions. As the scale of photovoltaic power stations expands and the demand for predictions increases, this sequential prediction approach may lead to slow prediction speeds, making it difficult to meet real-time prediction requirements. (2) Feature extraction is a crucial step in photovoltaic power generation prediction. However, traditional feature extraction methods often focus solely on surface features, and fail to capture the inherent relationships between various influencing factors in photovoltaic power generation data, such as light intensity, temperature, and more. To overcome these limitations, this paper proposes a mid-term PV power prediction model that combines Graph Convolutional Network (GCN) and Informer models. This fusion model leverages the multi-output capability of the Informer model to ensure the timely generation of predictions for long sequences. Additionally, it harnesses the feature extraction ability of the GCN model from nodes, utilizing graph convolutional modules to extract feature information from the 'query' and 'key' components within the attention mechanism. This approach provides more reliable feature information for mid-term PV power prediction, thereby ensuring the accuracy of long sequence predictions. Results demonstrate that the GCN-Informer model significantly reduces prediction errors while improving the precision of power generation forecasting compared to the original Informer model. Overall, this research enhances the prediction accuracy of PV power generation and contributes to advancing the field of clean energy. photovoltaic power generation; informer; deep learning; graph convolution network +
Footnote †: journal: Applied Sciences  
1 The first author is a member of the National Energy Commission under the contract No. 10 inherent to photovoltaic systems: their unpredictable stability and output cycles present significant hurdles to the seamless deployment of the power grid. Precise forecasting in the photovoltaic power generation sector aims to provide a secure and reliable electricity usage environment for various clientele, catering to diverse power scenarios and consumption needs [5]. However, due to the unique nature of electrical energy, generated power cannot be directly stored over extended periods. Hence, a dynamic equilibrium between generation and consumption must be sought [6]. Given the uncertainties of renewable energy generation environments, predicting power output has emerged as a pivotal challenge. In recent years, substantial strides have been made in photovoltaic power prediction models, with forecast accuracy hinging on photovoltaic generation data. Consequently, for accurate photovoltaic power prediction, the reliability of the data themselves and the selection of their attributes directly influence the precision of the forecasts. Photovoltaic power prediction involves comprehensive consideration of natural factors, including meteorological conditions, temporal elements, weather patterns, regional context, and seasonal influences. It also encompasses operational characteristics of the photovoltaic power station, such as system performance, infrastructure development, and equipment conditions. By leveraging a predictive model and analyzing historical data, the interconnections within the data sources are explored. Striving to meet a certain level of accuracy, this process computes forthcoming power generation data under specific conditions. In this era of modern technology, the advancement of information technology unfolds in a continuous and swift manner.