Trees vs Neurons: Comparison between random forest and ANN for high-resolution prediction of building energy consumption
Trees vs Neurons: Comparison between random forest and ANN for high-resolution prediction of building energy consumption
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DOI：
10.1016/j.enbuild.2017.04.038
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发表时间：
2017-07-15
影响因子：
6.7
通讯作者：
Rezgui, Yacine
中科院分区：
文献类型：
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作者：
Ahmad, Muhammad Waseem;Mourshed, Monjur;Rezgui, Yacine
能源预测模型在建筑物中用作先进控制和优化的性能评估引擎，并由设施管理人员和公用事业公司做出明智的决策，以提高能源效率。简化和数据驱动的模型往往是首选的地方详细模拟的相关信息是不可用的，需要快速响应。我们比较了广泛使用的前馈反向传播人工神经网络（ANN）与随机森林（RF）的性能，这是一种基于集成的方法，在预测中越来越受欢迎-用于预测西班牙马德里一家酒店的每小时HVAC能耗。在这两种情况下，增加社交参数（如客人数量）都略微提高了预测的准确性。总体而言，人工神经网络表现略好于RF的均方根误差（RMSE）分别为4.97和6.10。然而，使用分类变量进行调整和建模的容易性为基于集成的算法提供了处理多维复杂数据的优势，这在建筑物中是典型的。RF执行内部交叉验证（即使用袋外样本），并且只有几个调谐参数。这两种模型具有相当的预测能力，几乎同样适用于建筑能源应用。(C)2017作者由爱思唯尔公司出版
Energy prediction models are used in buildings as a performance evaluation engine in advanced control and optimisation, and in making informed decisions by facility managers and utilities for enhanced energy efficiency. Simplified and data-driven models are often the preferred option where pertinent information for detailed simulation are not available and where fast responses are required. We compared the performance of the widely-used feed-forward back-propagation artificial neural network (ANN) with random forest (RF), an ensemble-based method gaining popularity in prediction- for predicting the hourly HVAC energy consumption of a hotel in Madrid, Spain. Incorporating social parameters such as the numbers of guests marginally increased prediction accuracy in both cases. Overall, ANN performed marginally better than RF with root-mean-square error (RMSE) of 4.97 and 6.10 respectively. However, the ease of tuning and modelling with categorical variables offers ensemble-based algorithms an advantage for dealing with multi-dimensional complex data, typical in buildings. RF performs internal cross-validation (i.e. using out-of-bag samples) and only has a few tuning parameters. Both models have comparable predictive power and nearly equally applicable in building energy applications. (C) 2017 The Authors. Published by Elsevier B.V.