公司统计学系王国长老师（第一作者）与澳门大学舒连杰教授、粟燕教授合作的论文“One-day-ahead daily power forecasting of photovoltaic systems based on partial functional linear regression models”在《Renewable Energy》期刊上发表。此期刊为新能源领域的一流期刊，在JCR分区中属于2区，影响因子为3.476。 文章的主要目标是运用太阳能每一分钟的发电量数据及天气预报数据来预测太阳能第二天的总发电量。作者通过运用函数数据来分析，预测精度比传统的神经网络等方法提高了近30%。
该文摘要为： The intra-day time-varying pattern of solar data is more informative than the aggregated mean daily data. However, most of the traditional forecasting models often construct the 1-day ahead daily power forecast based on its historical daily averages but ignore the information from its intra-day dynamic pattern. Intuitively, the use of aggregated data could cause certain loss of information in forecasting, which in turn adversely affects forecasting accuracy. In order to make use of the valuable trajectory information of the power output within a day, this paper suggests a partial functional linear regression model (PFLRM) for forecasting the daily power output of PV systems. The PFLRM is a generalization of the traditional multiple linear regression model but enables to model nonlinearity structure. Compared to the neural network models that are often criticized by the requirements of past experience and reliable knowledge in the design of network architecture, the suggested method only involves a few parameter estimates. A regularized algorithm was used to estimate the PFLRM parameters. It is shown that the regularized PFLRM improves the forecast accuracy of power output over the traditional multiple linear regression and neural network models. The results were validated based on a 2.1 kWgrid connected PV system.
