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Application of extreme learning machine for short term output power forecasting of three grid-connected PV systems

机译：极限学习机在三台光伏并网系统短期输出功率预测中的应用

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The power output (PO) of a photovoltaic (PV) system is highly variable because of its dependence on solar irradiance and other meteorological factors. Hence, accurate PO forecasting of a grid-connected PV system is essential for grid stability, optimal unit commitment, economic dispatch, market participation and regulations. In this paper, a day ahead and 1 h ahead mean PV output power forecasting model has been developed based on extreme learning machine (ELM) approach. For this purpose, the proposed forecasting model is trained and tested using PO of PV system and other meteorological parameters recorded in three grid-connected PV system installed on a roof-top of PEARL laboratory in University of Malaya, Malaysia. The results obtained from the proposed model are compared with other popular models such as support vector regression (SVR) and artificial neural network (ANN). The performance in terms of accuracy and precision of the prediction models is conducted with standard statistical error indicators including: relative root mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute bias error (MABE) and coefficient of determination (R-2). The comparison of results obtained from the proposed ELM model to other models showed that ELM model enjoys higher accuracy and less computational time in forecasting the daily and hourly PV output power. (C) 2017 Elsevier Ltd. All rights reserved.
机译：光伏（PV）系统的功率输出（PO）高度可变，因为它依赖于太阳辐照度和其他气象因素。因此，对并网光伏系统的准确PO预测对于电网稳定性，最佳机组承诺，经济调度，市场参与和法规至关重要。在本文中，基于极限学习机（ELM）方法开发了提前一天和提前1小时的平均PV输出功率预测模型。为此，建议的预测模型是使用光伏系统的PO和其他气象参数进行训练和测试的，该光伏系统的气象参数是在马来西亚马来亚大学PEARL实验室的屋顶上安装的三个并网光伏系统中记录的。从提议的模型获得的结果与其他流行的模型进行了比较，例如支持向量回归（SVR）和人工神经网络（ANN）。预测模型在准确性和精确度方面的性能是通过标准的统计误差指标进行的，这些指标包括：相对均方根误差（RMSE），平均绝对百分比误差（MAPE），平均绝对偏差误差（MABE）和确定系数（ R-2）。从所提出的ELM模型与其他模型获得的结果的比较表明，ELM模型在预测每日和每小时PV输出功率方面具有较高的准确性，并减少了计算时间。 （C）2017 Elsevier Ltd.保留所有权利。

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