光伏发电在能源领域中具有重要地位。为了准确量化光伏发电功率的不确定性和波动范围，并提高区间预测的综合性能，提出了一种基于特征挖掘与改进TCN-BiGRU的光伏功率区间概率预测方法。首先，利用最大信息系数和符号传递熵因果分析，对气象特征进行筛选，剔除冗余信息，并构造全球水平辐射趋势特征、季节性特征和天气聚类特征以提供更多有效信息。随后，结合时间模式注意力机制和分位数回归方法对TCN-BiGRU模型进行改进，构建组合模型进行区间预测。最后，采用散度度量半极差优化经验带宽选择的核密度估计（kernel density estimation, KDE）方法生成概率预测结果。通过真实光伏电站数据进行分析，验证了所提方法在光伏功率区间概率预测中具有较高的可靠性和适用性。

Photovoltaic power generation has an important place in the energy sector. In order to accurately quantify the uncertainty and fluctuation range of PV(photovoltaic) power and to improve the comprehensiveness of interval forecasts, a probabilistic prediction method for PV power intervals based on feature mining with improved TCN-BiGRU was proposed. First, the maximum information coefficient and symbolic transfer entropy causal analysis were utilized to screen the meteorological features, remove redundant information, and construct global horizontal radiation trend features, seasonal features, and weather clustering features to provide more effective information. Subsequently, the TCN-BiGRU model was improved by combining the temporal pattern attention mechanism and quantile regression methods to construct a combined model for interval prediction. Finally, the probabilistic prediction results are generated using the KDE method of empirical bandwidth selection with scatter measure semi-polar optimization. The proposed method is analyzed by real PV plant data, which verifies the high reliability and applicability of the proposed method in PV power interval probability prediction.