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Abstract：
Time series forecasting still awaits a transformative breakthrough like that happened in computer vision and natural language processing. The absence of extensive, domain-independent benchmark datasets and standardized performance measurement units poses a significant challenge for it, especially for photovoltaic forecasting applications. Additionally, since it is often time domain-driven, a plethora of highly unique and domain-specific datasets were produced. The lack of uniformity among published models, developed under diverse settings for varying forecasting horizons, and assessed using non-standardized metrics, remains a significant obstacle to the progress of the field as a whole. To address these issues, a systematic review of the state-of-the-art literature on prediction tasks is presented, collected from the Web of Science and Scopus databases, published in 2022 and 2023, and filtered using keywords such as \"photovoltaic,\" \"deep learning,\" \"forecasting,\" and \"time series.\" Finally, 36 case studies were selected. Before comparing, a state-of-the-art demonstration of key elements in the topic was presented, such as model type, hyperparameters, and evaluation metrics. Then, the 36 articles were compared in terms of statistical analysis, including top publishing countries, data sources, variables, input, and output horizon, followed by an overall model comparison demonstrating every proposed model categorized into model type (artificial neural network units, recurrent units, convolutional units, and transformer units). Due to the mostly utilization of specific private datasets measured at the targeted location, having universal error metrics is crucial for clear global benchmarking. Root Mean Squared Error and Mean Absolute Error were the most utilized metrics, although they specifically demonstrate the accuracy relative to their respective sites. However, 33% utilized universal metrics, such as Mean Absolute Percentage Error, Normalized Root Mean Squared Error, and the Coefficient of Determination. Finally, trends, challenges, and future research were highlighted for the relevant topic to spotlight and bypass the current challenges.
摘要：
时间序列预测仍在等待像计算机视觉和自然语言处理那样的变革性突破。缺乏广泛的，与领域无关的基准数据集和标准化的性能度量单位对其构成了重大挑战，特别是光伏预测应用。此外,因为它通常是时域驱动的，产生了大量高度独特和特定领域的数据集。已发布的模型之间缺乏统一性，在不同的环境下开发，用于不同的预测范围，并使用非标准化指标进行评估，仍然是整个领域进展的重大障碍。为了解决这些问题,对预测任务的最新文献进行了系统回顾，从WebofScience和Scopus数据库中收集，于2022年和2023年发布，并使用“光伏”等关键字进行过滤，\"\"深度学习,\“\”预测，\"和\"时间序列。“最后，选择了36个案例研究。比较之前,介绍了该主题中关键要素的最新演示，例如模型类型，超参数,和评估指标。然后，在统计分析方面对这36篇文章进行了比较，包括顶级出版国家，数据源,变量，输入，和输出范围，然后是一个整体模型比较，展示了每一个被分类为模型类型的模型(人工神经网络单元，经常性单位，卷积单位，和变压器单元)。由于主要利用在目标位置测量的特定私人数据集，拥有通用的误差度量对于明确的全球基准测试至关重要。均方根误差和平均绝对误差是最常用的指标，尽管他们特别证明了相对于各自网站的准确性。然而,33%使用通用指标，例如平均绝对百分比误差，归一化均方根误差，和确定系数。最后,趋势,挑战,并强调了未来的研究，以突出相关主题并绕过当前的挑战。
