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Deterministic and probabilistic forecasting of photovoltaic power based on deep convolutional neural network

机译：基于深度卷积神经网络的光伏发电确定性和概率性预测

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摘要

The penetration of photovoltaic (PV) energy into modern electric power and energy systems has been gradually increased in recent years due to its benefits of being abundant, inexhaustible and clean. In order to reduce the negative impacts of PV energy on electric power and energy systems, advanced forecasting approach with high accuracy is a pressing need. Aimed at this, a novel hybrid method for deterministic PV power forecasting based on wavelet transform (WT) and deep convolutional neural network (DCNN) is firstly proposed in this paper. WT is used to decompose the original signal into several frequency series. Each frequency has better outlines and behaviors. DCNN is employed to extract the nonlinear features and invariant structures exhibited in each frequency. Then, a probabilistic PV power forecasting model that combines the proposed deterministic method and spine quantile regression (QR) is originally developed to statistically evaluate the probabilistic information in PV power data. The proposed deterministic and probabilistic forecasting methods are applied to real PV data series collected from PV farms in Belgium. Numerical results presented in the case studies demonstrate that the proposed methods exhibit the ability of improving forecasting accuracies in terms of seasons and various prediction horizons, when compared to conventional forecasting models.
机译：近年来，由于光伏（PV）能源丰富，取之不尽，用之不竭，其在现代电力和能源系统中的渗透已逐渐增加。为了减少光伏能源对电力和能源系统的负面影响，迫切需要具有高精度的高级预测方法。为此，本文首次提出了一种基于小波变换（WT）和深度卷积神经网络（DCNN）的确定性光伏发电功率预测的混合方法。 WT用于将原始信号分解为几个频率序列。每个频率都有更好的轮廓和行为。 DCNN用于提取每个频率中表现出的非线性特征和不变结构。然后，最初开发了一种将所提出的确定性方法与脊柱分位数回归（QR）相结合的概率PV功率预测模型，以统计方式评估PV功率数据中的概率信息。拟议的确定性和概率性预测方法适用于从比利时的光伏电站收集的真实光伏数据系列。案例研究中提供的数值结果表明，与传统的预测模型相比，所提出的方法在季节和各种预测范围方面具有提高预测精度的能力。

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