Accurate forecasting of solar energy is essential for photovoltaic (PV) plants, to facilitate their participation in the energy market and for efficient resource planning. This article is dedicated to two forecasting models: (1) ARIMA (Autoregressive Integrated Moving Average) statistical approach to time series forecasting, using measured historical data, and (2) ANN (Artificial Neural Network) using machine learning techniques. The main contributions of the authors could be synthetized as follows: (1) analysis and discussion of the experimental and simulated results regarding solar radiation forecast, as well as energy production prediction and forecasting based on ARIMA and ANN models for two case studies: (a) laboratory BIPV system developed at the Polytechnic University of Bucharest and (b) large PV park placed in a specific site of the south of Romania. A variability index of solar radiation was introduced for the model improvement; (2) comparison between the ARIMA and ANN results to highlight the ARIMA model which is more efficient than the ANN one; (3) optimized method defined by the GMDH model (Group Method of Data Handling) proposed to provide a software program for calculation of the PV energy production.

中文翻译：

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基于 ARIMA 和 ANN 高级模型的光伏系统能源生产预测

太阳能的准确预测对于光伏 (PV) 发电厂至关重要，以促进其参与能源市场和有效的资源规划。本文致力于两种预测模型：（1）ARIMA（自回归综合移动平均）时间序列预测的统计方法，使用测量的历史数据，以及（2）ANN（人工神经网络）使用机器学习技术。作者的主要贡献可归纳如下：（1）对太阳辐射预测的实验和模拟结果的分析和讨论，以及基于 ARIMA 和 ANN 模型的两个案例研究的能源生产预测和预测：(a) 布加勒斯特理工大学开发的实验室 BIPV 系统和 (b) 位于罗马尼亚南部特定地点的大型光伏园区。为模型改进引入了太阳辐射的可变性指数；(2) ARIMA 和 ANN 结果之间的比较，以突出 ARIMA 模型比 ANN 模型更有效；(3) GMDH模型定义的优化方法(Group Method of Data Handling)提出，为光伏发电量的计算提供软件程序。