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Forecasting of electricity prices with neural networks

机译：用神经网络预测电价

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During recent years, the electricity energy market deregulation has led to a new free competition situation in Europe and other countries worldwide. Generators, distributors and qualified clients have some uncertainties about the future evolution of electricity markets. In consequence, feasibility studies of new generation plants, design of new systems and energy management optimization are frequently postponed. The ability of forecasting energy prices, for instance the electricity prices, would be highly appreciated in order to improve the profitability of utility investments. The development of new simulation techniques, such as Artificial Intelligence (AI), has provided a good tool to forecast time series. In this paper, it is demonstrated that the Neural Network (NN) approach can be used to forecast short term hourly electricity pool prices (for the next day and two or three days after). The NN architecture and design for prices forecasting are described in this paper. The results are tested with extensive data sets, and good agreement is found between actual data and NN results. This methodology could help to improve power plant generation capacity management and, certainly, more profitable operation in daily energy pools.
机译：近年来，电力市场的放松管制导致欧洲和世界其他国家出现了新的自由竞争局面。发电商，分销商和合格的客户对电力市场的未来发展具有不确定性。因此，新一代工厂的可行性研究，新系统的设计以及能源管理的优化常常被推迟。预测能源价格（例如电价）的能力将得到高度赞赏，以提高公用事业投资的盈利能力。新的仿真技术（例如人工智能（AI））的发展为预测时间序列提供了一个很好的工具。在本文中，证明了神经网络（NN）方法可用于预测短期小时电池价格（第二天以及之后的两三天）。本文介绍了用于价格预测的NN体系结构和设计。使用广泛的数据集测试了结果，并且在实际数据和NN结果之间找到了很好的一致性。这种方法可以帮助改善发电厂的发电容量管理，当然也可以改善日常能源池中的利润。

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