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Application of hybrid computational intelligence models in short-term bus load forecasting

机译：混合计算智能模型在短期公交负荷预测中的应用

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Artificial neural networks (ANNs) are a favorable scheme in load forecasting applications mainly due to their endogenous capacity of robust modeling of data sets with highly non-linear relationship between inputs and outputs. Usually, the inputs correspond to historical load values, exogenous variables like temperature, day type identification codes and others. The outputs refer to the load values under examination. The majority of the load forecasting related literature focuses in aggregated load system level. While contemporary research efforts focus in smart grid technologies, there is need to study the characteristics of small scaled loads. Bus load forecasting refers to prediction of the demand patterns in buses of the transmission and distribution systems. Bus load exhibits low correlation with the aggregated system load, since it is characterized by a high level of stochasticity. Hence, a proper selection and formulation of the forecasting model is essential in order to keep the prediction accuracy within acceptable ranges. The treatment of bus load characteristics is held with computational intelligence techniques such as clustering and ANN. Neural network based systems are a favorable scheme in recent years in price and load predictions over traditional time series models. ANN can fully adapt expert knowledge and modify their parameters accordingly to simulate the problem's attributions through training paradigms. Thus, ANN based systems are an essential choice, justified by the paper's findings, for highly volatile time series. This work focuses on the short-term load forecasting (STLF) of a number of buses within the Greek interconnected system. Firstly, a modified version of the ANN already proposed for the aggregated load of the interconnected system is employed. To enhance the forecasting accuracy of the ANN, the load profiling methodology is used resulting to the formulation of two novel hybrid forecasting models. These models refer to the combination of the ANN with a clustering algorithm, resulting to superior performance. Simulation results indicate that the combination captures and successfully treats the special characteristics of the bus load patterns. The scope of the present paper is to develop efficient forecasting systems for short-term bus load predictions. This is a current research challenge due to the high interest for smart grids and demand side management applications by utilities, regulators, retailer and energy service companies. Bus load forecasting appears to be a more difficult engineering problem compared to forecasting of the total load of a country. No hybrid models for bus load predictions have been presented so far in the literature. Two novel clustering based tools are developed and successfully tested in a number of loads covering different types of electricity consumers and demand levels. (C) 2016 Elsevier Ltd. All rights reserved.
机译：人工神经网络（ANN）是负荷预测应用程序中的一种理想方案，这主要是因为它们具有对输入和输出之间具有高度非线性关系的数据集进行强大建模的内生能力。通常，输入对应于历史负荷值，外生变量（例如温度），日类型识别码等。输出参考所检查的负载值。与负荷预测相关的大多数文献都集中在总负荷系统级别。尽管当代的研究工作集中在智能电网技术上，但仍需要研究小规模负荷的特性。公交车负荷预测是指对输配电系统公交车的需求模式进行预测。总线负载与总系统负载之间的相关性较低，因为它具有较高的随机性。因此，为了将预测精度保持在可接受的范围内，正确选择和制定预测模型至关重要。总线负载特性的处理通过聚类和ANN等计算智能技术进行。与传统的时间序列模型相比，基于神经网络的系统在价格和负荷预测方面是近年来的理想方案。人工神经网络可以充分利用专家知识并相应地修改其参数，以通过训练范例来模拟问题的归因。因此，对于高度易变的时间序列而言，基于人工神经网络的系统是一个必不可少的选择，该论文的发现证明了这一点。这项工作的重点是希腊互连系统中许多公交车的短期负荷预测（STLF）。首先，采用已经提出的用于互连系统的总负荷的人工神经网络的修改版本。为了提高人工神经网络的预测准确性，使用了负荷分析方法，从而形成了两种新颖的混合预测模型。这些模型引用了ANN与聚类算法的组合，从而获得了卓越的性能。仿真结果表明，该组合能够捕获并成功处理总线负载模式的特殊特征。本文的范围是为短期公交车负荷预测开发有效的预测系统。由于公用事业，监管机构，零售商和能源服务公司对智能电网和需求侧管理应用的高度关注，这是当前的研究挑战。与预测一个国家的总负荷相比，预测公共汽车负荷似乎是一个更加困难的工程问题。迄今为止，在文献中还没有提出用于公交车负荷预测的混合模型。开发了两种新颖的基于聚类的工具，并在涵盖不同类型用电者和需求水平的许多负载中成功进行了测试。 （C）2016 Elsevier Ltd.保留所有权利。

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