Precise forecasts of photo-voltaic (PV) energy production are necessary for its planning, utilization and integration into the electrical grid. Intra-day or daily statistical models, using only the latest weather observations and power data measurements, can predict PV power for a plant-specific location and condition on time. Numerical weather prediction (NWP) systems are run every 6 h to produce free prognoses of local cloudiness with a considerable delay and usually not in operational quality. Differential polynomial neural network (D-PNN) is a novel neuro-computing technique able to model complex weather patterns. D-PNN decomposes the n-variable partial differential equation (PDE), allowing complex representation of the near-ground atmospheric dynamics, into a set of 2-input node sub-PDEs. These are converted and substituted using the Laplace transformation according to operational calculus. D-PNN produces applicable PDE components which extend, one by one, its composite models using the selected optimal inputs. The models are developed with historical spatial data from estimated daily training periods for a specific inputs- > output time-shift to predict clear-sky index. Multi-step 1–9 h and one-step 24-h PV power predictions using machine learning and regression are compared to assess the performance of their models for both of the approaches. The presented spatial models obtain a better prediction accuracy than those post-processing NWP data, using a few variables only. The daily statistical models allow prediction of full PVP cycles in one step with an adequate accuracy in the morning and afternoon hours. This is inevitable in management of PV plant energy production and consumption.

对光伏（PV）能源生产进行准确的预测对于计划，利用和集成到电网中是必要的。日内或每日统计模型仅使用最新的天气观察和功率数据测量值，可以按时预测特定工厂位置和状况的PV功率。每6个小时运行一次数值天气预报（NWP）系统，以免费预测当地的阴天，这会产生相当大的延迟，并且通常不会达到运行质量。微分多项式神经网络（D-PNN）是一种能够对复杂天气模式进行建模的新型神经计算技术。D-PNN分解n变量偏微分方程（PDE），允许将近地面大气动力学复杂表示为一组2输入节点子PDE。根据操作演算，使用拉普拉斯变换对它们进行转换和替换。D-PNN生成适用的PDE组件，这些组件使用选定的最佳输入逐一扩展其复合模型。这些模型是根据特定输入->输出时移的估计每日训练周期的历史空间数据开发的，以预测晴空指数。比较了使用机器学习和回归进行的多步1–9小时和单步24小时PV功率预测，以评估两种方法的模型性能。与仅使用一些变量的后处理NWP数据相比，本文提出的空间模型获得了更好的预测精度。每日统计模型允许您在一个早晨和下午的时间中以足够的准确性一步预测整个PVP周期。这在管理光伏电站能源生产和消耗中是不可避免的。