Multilayer perceptron for short-term load forecasting: from global to local approach
Multilayer perceptron for short-term load forecasting: from global to local approach
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DOI：
10.1007/s00521-019-04130-y
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发表时间：
2020-04-01
影响因子：
6
通讯作者：
Dudek, Grzegorz
中科院分区：
文献类型：
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作者：
Dudek, Grzegorz
许多预测模型都建立在神经网络的基础上。这些模型中的关键问题是数据表示和预测问题的分解，这些问题强烈地转化为预测的准确性。在这项工作中，我们以短期电力负荷需求预测为例来考虑这两个问题。负荷时间序列既表达了趋势，也表达了多个季节周期。为了处理多季节性问题，我们考虑了四种问题分解方法。根据问题的分解程度，将问题分解为局部子问题，并利用神经网络对问题进行建模。我们从能胜任所有预测任务的全局模型，到能胜任子问题的局部模型，再到为每个预测任务单独建立的模型。此外，我们还考虑了不同的输入数据编码方式，并分析了数据表示方式对结果的影响。利用四个欧洲国家的实际电力系统数据对预测模型进行了检验。结果表明，与全局方法相比，局部方法能显著提高负荷预测的精度。分解程度越高，预测误差减少的幅度越大。
Many forecasting models are built on neural networks. The key issues in these models, which strongly translate into the accuracy of forecasts, are data representation and the decomposition of the forecasting problem. In this work, we consider both of these problems using short-term electricity load demand forecasting as an example. A load time series expresses both the trend and multiple seasonal cycles. To deal with multi-seasonality, we consider four methods of the problem decomposition. Depending on the decomposition degree, the problem is split into local subproblems which are modeled using neural networks. We move from the global model, which is competent for all forecasting tasks, through the local models competent for the subproblems, to the models built individually for each forecasting task. Additionally, we consider different ways of the input data encoding and analyze the impact of the data representation on the results. The forecasting models are examined on the real power system data from four European countries. Results indicate that the local approaches can significantly improve the accuracy of load forecasting, compared to the global approach. A greater degree of decomposition leads to the greater reduction in forecast errors.