PDF(Pubmed)
Abstract：
Prediction of the energy, active production from the PV solar plant is a challenge in cloudy weather or with clouds over the solar plant; therefore, it has impact in the planning of the power system, especially in the season analysis and prediction accuracy adjustments, for example in holidays. In 2022, some authors published some analysis associated to horizontal pyranometers and the limits in the evaluation of the data, the Mean Bias Error of Daily Solar Irradiation average (MIEave) ranges from 0.17% to 2.86% associated a sudden change in the weather, it increases the \"risk of misestimating the potential electricity generation\" with short-term error of more than 50% and the Global Horizontal Irradiance (GHI) has a mean bias error (MBE) of at least ±8% [1]. In this research article, a novel proposal for short-term forecasting combines the satellite with meteorological station data and statistical model associated to the new seasonality analysis by using two approaches: i) NeuralProphet, Ridge regression, ii) Long Short-Term Memory with convolutional neural networks. Besides, it requires three KPI as feedback, it is the mean absolute error (MAE), relative Root mean square error (RMSE), and mean absolute percentage error (MAPE). The results demonstrate a MAPE of 5.93% and a computational time 852.10 s and the comparison with new predictions methods from 2019 to 2021. This research article illustrates the new approach with the forecasting method in a case of the PV solar plant in Peru and proves the robustness and seasonality results, and new short-terms improvements associated to external influence as cloudy conditions and resource availability. Our findings are an improvement of the model MAPE 12.14%-5.93%; even compared with the literature and currently models as ARIMA-LSTM with 10.57%, LSTM with NN and G, SARIMA and SVM considering Gaussian White Noise with 8.14% and Prophet with SVM with 8.81%.
摘要：
对能量的预测，光伏太阳能发电厂的主动生产在多云天气或太阳能发电厂上空有云层的情况下是一个挑战；因此，它对电力系统的规划有影响，特别是在季节分析和预测精度调整方面，例如在假期。2022年，一些作者发表了一些与水平比重计相关的分析和数据评估中的限制，日太阳平均辐射的平均偏差误差（MIEave）范围从0.17％到2.86％与天气的突然变化有关，它增加了“错误估计潜在发电量的风险”，短期误差超过50%，全球水平辐照度(GHI)的平均偏差误差(MBE)至少为±8%[1]。在这篇研究文章中，通过使用两种方法，将卫星与气象站数据和与新的季节性分析相关的统计模型相结合，用于短期预报：i）NeuralProphet，岭回归，ii)卷积神经网络的长短期记忆。此外,它需要三个KPI作为反馈，它是平均绝对误差(MAE)，相对均方根误差(RMSE)，和平均绝对百分比误差(MAPE)。结果表明，MAPE为5.93％，计算时间为852.10s，并与2019年至2021年的新预测方法进行了比较。本研究文章说明了在秘鲁光伏太阳能发电厂的情况下预测方法的新方法，并证明了鲁棒性和季节性结果，以及与外部影响相关的新的短期改进，如多云条件和资源可用性。我们的发现是MAPE模型的改进12.14％-5.93％;即使与文献和目前的ARIMA-LSTM模型相比，也有10.57％，带NN和G的LSTM，SARIMA和SVM考虑了8.14%的高斯白噪声和8.81%的Prophet支持向量机。
