论文标题
日常电价预测的神经网络：单一与多个输出
Neural networks in day-ahead electricity price forecasting: Single vs. multiple outputs
论文作者
论文摘要
人工智能和机器学习方法领域的最新进展导致其在文献中的普及大幅提高，包括电价预测。所述方法涵盖了从决策树到随机森林到各种人工神经网络模型和混合方法的非常广泛的范围。在电价预测中，神经网络是最受欢迎的机器学习方法，因为它们为经过良好测试的线性回归模型提供了非线性对应物。但是，他们的应用并不直接，需要考虑多个实施因素。这些因素之一是网络的结构。本文在使用深层神经网络时进行了全面的比较，该结构分别介绍了一天中的每个小时，一种反映了每日拍卖结构和价格向量。结果表明，使用后者具有显着的准确性优势，并在五个不同的功率交换的数据中得到了证实。
Recent advancements in the fields of artificial intelligence and machine learning methods resulted in a significant increase of their popularity in the literature, including electricity price forecasting. Said methods cover a very broad spectrum, from decision trees, through random forests to various artificial neural network models and hybrid approaches. In electricity price forecasting, neural networks are the most popular machine learning method as they provide a non-linear counterpart for well-tested linear regression models. Their application, however, is not straightforward, with multiple implementation factors to consider. One of such factors is the network's structure. This paper provides a comprehensive comparison of two most common structures when using the deep neural networks -- one that focuses on each hour of the day separately, and one that reflects the daily auction structure and models vectors of the prices. The results show a significant accuracy advantage of using the latter, confirmed on data from five distinct power exchanges.
