Day-ahead photovoltaic power production forecasting methodology based on machine learning and statistical post-processing
Day-ahead photovoltaic power production forecasting methodology based on machine learning and statistical post-processing
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DOI：
10.1016/j.apenergy.2020.115023
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发表时间：
2020-06
期刊：
影响因子：
11.2
通讯作者：
S. Theocharides;G. Makrides;Andreas Livera;M. Theristis;Paris Kaimakis;G. Georghiou
中科院分区：
文献类型：
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作者：
S. Theocharides;G. Makrides;Andreas Livera;M. Theristis;Paris Kaimakis;G. Georghiou
确保光伏系统大规模无缝集成的一个主要挑战是提高能源产量预测的准确性，特别是在光伏份额高的电网地区。本文的范围是通过提出一种统一的方法来解决这个问题，该方法基于数据驱动的机器学习技术和统计后处理，用于提高准确性的小时平均前一天光伏发电预测。更具体地说，所提出的预测方法框架包括数据质量阶段、数据驱动的功率输出机器学习模型开发（人工神经网络），天气聚类评估（K-means clustering）后处理输出优化（线性回归校正法）结果表明，线性回归系数在预测产量中的应用，开发的日前光伏发电神经网络通过进一步校正太阳辐照度预测偏差提高了性能精度。当应用于历史系统数据集时，所得到的优化模型提供了4.7%的平均绝对百分比误差。最后，该模型进行了验证，在炎热和寒冷的半干旱气候的位置，所得到的结果表明，通过产生的平均绝对百分比误差为4.7%和6.3%，分别预测精度接近协议。验证分析表明，该模型具有较高的预测精度和稳定性。
A main challenge towards ensuring large-scale and seamless integration of photovoltaic systems is to improve the accuracy of energy yield forecasts, especially in grid areas of high photovoltaic shares. The scope of this paper is to address this issue by presenting a unified methodology for hourly-averaged day-ahead photovoltaic power forecasts with improved accuracy, based on data-driven machine learning techniques and statistical post-processing. More specifically, the proposed forecasting methodology framework comprised of a data quality stage, data-driven power output machine learning model development (artificial neural networks), weather clustering assessment (K-means clustering), post-processing output optimisation (linear regressive correction method) and the final performance accuracy evaluation.The results showed that the application of linear regression coefficients to the forecasted outputs of the developed day-ahead photovoltaic power production neural network improved the performance accuracy by further correcting solar irradiance forecasting biases. The resulting optimised model provided a mean absolute percentage error of 4.7% when applied to historical system datasets. Finally, the model was validated both, at a hot as well as a cold semi-arid climatic location, and the obtained results demonstrated close agreement by yielding forecasting accuracies of mean absolute percentage error of 4.7% and 6.3%, respectively. The validation analysis provides evidence that the proposed model exhibits high performance in both forecasting accuracy and stability.