ABSTRACT Photovoltaic (PV) power forecasting plays a crucial role in optimizing the operation and planning of PV systems, thereby enabling efficient energy management and grid integration. However, un certainties caused by fluctuating weather conditions and complex interactions between different variables pose significant challenges to accurate PV power forecasting. In this study, we propose PV-Client (Cross-variable Linear Integrated ENhanced Transformer for Photovoltaic power forecasting) to address these challenges and enhance PV power forecasting accuracy. PV-Client employs an ENhanced Transformer module to capture complex interactions of various features in PV systems, and utilizes a linear module to learn trend information in PV power. Diverging from conventional time series-based Transformer models that use cross-time Attention to learn dependencies between different time steps, the Enhanced Transformer module integrates cross-variable Attention to capture dependencies between PV power and weather factors. Similarly, PV-Client outperforms the secondbest model SVR by 10.1% in MSE metrics and 0.2% in accuracy metrics at the Xinqingnian Station, and PV-Client exhibits superior performance compared to the second-best model SVR with enhancements of 3.4% in MSE metrics and 0.9% in accuracy metrics at the Hongxing Station. Keywords: PV power forecasting, PV-Client, Linear, Transformer, Cross-variable Attention NONMENCLATURE Abbreviations PV Photovoltaic Cross-variable Linear Integrated PV-Client ENhanced Transformer for Photovoltaic power forecasting 1. INTRODUCTION Photovoltaic (PV) power, as a clean and renewable energy source, has gained significant attention in recent years driven by its potential to curtail carbon emissions and diminish the reliance on traditional fossil fuels [1]. The efficient utilization of PV energy relies heavily on accurate forecasting of PV system output. As we navigate towards a future dominated by sustainable energy, the role of accurate PV power forecasting stands as a pivotal element in achieving a harmonious coexistence between renewable sources and the established energy infrastructure. Numerous research studies have been conducted to devise accurate and computationally efficient forecasting models for PV power generation.