针对现有区域分布式光伏功率预测依赖气象数据、运维成本高、数据质量差及结果可信性不足的问题，提出一种光伏功率电量联合可信预测方法。首先，基于电能表功率采集与日电量冻结数据，进行联合筛选、融合与归一化处理以提升数据质量；其次，构建多时间尺度高精度序列到序列（sequence to sequence，Seq2Seq）预测框架，结合区域集中式光伏电站历史与预测数据，通过计及功率与电量的多时间尺度损失函数来优化预测精度；最后，采用承诺-证明简洁非交互零知识证明（commit-and-prove succinct non-interactive argument of knowledge，cp-SNARKs）技术构造模型完整性验证方案，在保障结果可信的同时保护模型机密性。基于华北某城市实测数据的实验验证表明，该方法能显著降低功率电量预测误差，提高光伏功率预测精度。所提方法无需气象数据和系统改造，具有数据质量高、预测精度优、运维成本低和可验证性强的特点，可拓展至负荷预测、风电出力等时间序列预测场景。

To address the issues of existing regional distributed photovoltaic (PV) power forecasting, such as heavy reliance on meteorological data, high operation and maintenance costs, poor data quality and insufficient result credibility,a joint credible forecasting method for PV power and energy is proposed. First, power measurements from smart meters and daily frozen energy data are jointly filtered, fused, and normalized to enhance data set quality. Second, a multi-time-scale, high-accuracy sequence-to-sequence (Seq2Seq) forecasting framework is developed, integrating historical and forecast data from centralized regional PV plants; a multi-time-scale loss function that jointly accounts for both power and energy is employed to optimize prediction accuracy. Finally, a model integrity verification scheme based on commit-and-prove succinct non-interactive argument of knowledge (cp-SNARKs) is designed to ensure result credibility while preserving model confidentiality. Experimental validation using real-world data from a city in North China demonstrates that the proposed method significantly reduces forecasting errors for both power and energy, thereby improving PV power prediction accuracy. Requiring no meteorological inputs or system modifications, the approach features high data quality, superior prediction accuracy, low operational cost, and strong verifiability, making it readily extensible to other time-series forecasting tasks such as load forecasting and wind power prediction.