Short term electrical load forecasting is a topic of major interest for the planning of energy production and distribution. The use of artificial neural networks has been demonstrated as a valid alternative to classical statistical methods in terms of accuracy of results. However, a common architecture able to forecast the load in different geographical regions, showing different load shape and climate characteristics, is still missing. In this paper we discuss a heterogeneous neural network architecture composed of an unsupervised part, namely a neural gas, which is used to analyze the process in sub models finding local features in the data and suggesting regression variables, and a supervised one, a multilayer perceptron, which performs the approximation of the underlying function. The resulting outputs are then summed by a weighted fuzzy average, allowing a smooth transition between sub models. The effectiveness of the proposed architecture Is demonstrated by two days ahead load forecasting of L'Energie de L'Ouest Suisse (EOS) power system sub areas, corresponding to five different geographical regions, and of its total electrical load.
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机译:短期电力负荷预测是能源生产和分配计划的主要关注主题。就结果的准确性而言,已证明使用人工神经网络可以替代传统的统计方法。然而,仍然缺少能够预测不同地理区域的负荷,显示出不同负荷形状和气候特征的通用架构。在本文中,我们讨论了一种由无监督部分(即神经气体)组成的异构神经网络体系结构,该结构用于分析子模型中的过程,以发现数据中的局部特征并建议回归变量;以及一种受监督的多层感知器,它执行基本功能的近似值。然后,将输出结果与加权模糊平均值求和,以允许子模型之间的平滑过渡。提前两天对与五个不同地理区域相对应的L'Energie de L'Ouest Suisse(EOS)电力系统子区域的负荷预测及其总电力负荷,可以证明所提出体系结构的有效性。
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