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Day-ahead price forecasting of electricity markets by a new feature selection algorithm and cascaded neural network technique

机译：利用新的特征选择算法和级联神经网络技术进行电力市场日间价格预测

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摘要

With the introduction of restructuring into the electric power industry, the price of electricity has become the focus of all activities in the power market. Electricity price forecast is key information for electricity market managers and participants. However, electricity price is a complex signal due to its non-linear, non-stationary, and time variant behavior. In spite of performed research in this area, more accurate and robust price forecast methods are still required. In this paper, a new forecast strategy is proposed for day-ahead price forecasting of electricity markets. Our forecast strategy is composed of a new two stage feature selection technique and cascaded neural networks. The proposed feature selection technique comprises modified Relief algorithm for the first stage and correlation analysis for the second stage. The modified Relief algorithm selects candidate inputs with maximum relevancy with the target variable. Then among the selected candidates, the correlation analysis eliminates redundant inputs. Selected features by the two stage feature selection technique are used for the forecast engine, which is composed of 24 consecutive forecasters. Each of these 24 forecasters is a neural network allocated to predict the price of 1 h of the next day. The whole proposed forecast strategy is examined on the Spanish and Australia's National Electricity Markets Management Company (NEMMCO) and compared with some of the most recent price forecast methods.
机译：随着重组进入电力行业，电价已成为电力市场上所有活动的焦点。电价预测是电力市场经理和参与者的关键信息。但是，由于电价具有非线性，不稳定和时变特性，因此它是一个复杂的信号。尽管在该领域进行了研究，但仍需要更准确，更可靠的价格预测方法。本文针对电力市场的日间价格预测提出了一种新的预测策略。我们的预测策略由新的两阶段特征选择技术和级联神经网络组成。提出的特征选择技术包括针对第一阶段的改进的Relief算法和针对第二阶段的相关性分析。改进的救济算法选择与目标变量具有最大相关性的候选输入。然后，在选定的候选项中，相关分析将消除多余的输入。由两阶段特征选择技术选择的特征用于预测引擎，该引擎由24个连续的预测器组成。这24个预报器中的每个预报器都是一个神经网络，分配用于预测第二天1小时的价格。整个拟议的预测策略已在西班牙和澳大利亚的国家电力市场管理公司（NEMMCO）上进行了审查，并与一些最新的价格预测方法进行了比较。

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