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Electricity price forecast using Combinatorial Neural Network trained by a new stochastic search method

机译：运用新型随机搜索方法训练的组合神经网络预测电价

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摘要

Electricity price forecast is key information for successful operation of electricity market participants. However, the time series of electricity price has nonlinear, non-stationary and volatile behaviour and so its forecast method should have high learning capability to extract the complex input/output mapping function of electricity price. In this paper, a Combinatorial Neural Network (CNN) based forecasting engine is proposed to predict the future values of price data. The CNN-based forecasting engine is equipped with a new training mechanism for optimizing the weights of the CNN. This training mechanism is based on an efficient stochastic search method, which is a modified version of chemical reaction optimization algorithm, giving high learning ability to the CNN. The proposed price forecast strategy is tested on the real-world electricity markets of Pennsylvania-New Jersey-Maryland (PJM) and mainland Spain and its obtained results are extensively compared with the results obtained from several other forecast methods. These comparisons illustrate effectiveness of the proposed strategy. (C) 2015 Elsevier Ltd. All rights reserved.
机译：电价预测是电力市场参与者成功运作的关键信息。然而，电价的时间序列具有非线性，不稳定和波动的特性，因此其预测方法应具有较高的学习能力，以提取复杂的电价输入/输出映射函数。本文提出了一种基于组合神经网络（CNN）的预测引擎来预测价格数据的未来价值。基于CNN的预测引擎配备了用于优化CNN权重的新训练机制。这种训练机制基于高效的随机搜索方法，该方法是化学反应优化算法的改进版本，为CNN提供了很高的学习能力。建议的价格预测策略已在宾夕法尼亚州，新泽西州，马里兰州（PJM）和西班牙大陆的真实电力市场上进行了测试，其获得的结果与从其他几种预测方法获得的结果进行了广泛比较。这些比较说明了所提出策略的有效性。 （C）2015 Elsevier Ltd.保留所有权利。

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