Photovoltaic Power Forecasting Using a Hybrid CNN–Transformer–BiLSTM Neural Network

This dataset consists of high-frequency time-series operational data recorded at 15-minute intervals from January 1 to January 7, 2019, for a photovoltaic (PV) power generation system. The data encompasses two primary categories of variables: environmental and system performance metrics. Specifically, it includes environmental and meteorological parameters such as module temperature, ambient temperature, atmospheric pressure, humidity, global horizontal irradiance, direct normal irradiance, diffuse horizontal irradiance, along with the target output variable—actual power generation. In terms of data collection methodology, this is a field-measured dataset acquired automatically via a sensor network. The temporal continuity of timestamps and the synchronous recording of environmental conditions and power output indicate that the data were collected by a monitoring system installed at the PV plant, capable of real-time, automated acquisition of both system operational status and ambient environmental conditions. The relationship between this dataset and the present study lies in its role as a foundational resource for modeling in photovoltaic power forecasting or performance analysis. The environmental variables—particularly various components of solar irradiance and temperature—are critical predictors influencing PV power output, while the "actual power generation" serves as the target variable for model training or the ground truth for validation. Consequently, this dataset can be employed to train and evaluate machine learning or deep learning models—such as regression algorithms or time series models—to quantify the impact of environmental factors on power generation efficiency or to achieve accurate short-term forecasting of power output. Such capabilities are of significant research value for optimizing the operational efficiency of PV plants and facilitating stable integration into the power grid.本数据集包含某光伏（photovoltaic，PV）发电系统在2019年1月1日至1月7日期间，以15分钟为间隔采集的高频时序运行数据。数据涵盖两类核心变量：环境变量与系统性能指标。具体而言，数据集包含环境与气象参数，如组件温度、环境温度、大气压强、相对湿度、总水平辐照度、直接法向辐照度、散射水平辐照度，以及作为目标输出变量的实际发电量。 从数据采集方式来看，本数据集为通过传感器网络自动获取的现场实测数据。时间戳的时序连续性、环境条件与发电量的同步记录表明，该数据由光伏电站部署的监控系统采集，该系统可实时自动采集系统运行状态与周边环境参数。 本数据集与本研究的关联在于，它可作为光伏功率预测或性能分析建模的基础资源。环境变量——尤其是各类太阳辐照度分量与温度——是影响光伏出力的关键预测因子，而“实际发电量”可作为模型训练的目标变量，或验证环节的真实标签。据此，本数据集可用于训练与评估机器学习或深度学习模型，如回归算法、时序模型等，以量化环境因素对发电效率的影响，或实现发电量的精准短期预测。此类应用对于优化光伏电站运行效率、推动光伏电力稳定并网具有重要研究价值。