...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Energy and power engineering >Short-Term Electricity Price Forecasting Using a Combination of Neural Networks and Fuzzy Inference
【24h】

Short-Term Electricity Price Forecasting Using a Combination of Neural Networks and Fuzzy Inference

机译：神经网络和模糊推理相结合的短期电价预测

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

This paper presents an artificial neural network, ANN, based approach for estimating short-term wholesale electricity prices using past price and demand data. The objective is to utilize the piecewise continuous na-ture of electricity prices on the time domain by clustering the input data into time ranges where the variation trends are maintained. Due to the imprecise nature of cluster boundaries a fuzzy inference technique is em-ployed to handle data that lies at the intersections. As a necessary step in forecasting prices the anticipated electricity demand at the target time is estimated first using a separate ANN. The Australian New-South Wales electricity market data was used to test the system. The developed system shows considerable im-provement in performance compared with approaches that regard price data as a single continuous time se-ries, achieving MAPE of less than 2% for hours with steady prices and 8% for the clusters covering time pe-riods with price spikes.
机译：本文提出了一种基于人工神经网络（ANN）的方法，该方法可以使用过去的价格和需求数据估算短期批发电价。目的是通过将输入数据聚类到保持变化趋势的时间范围内，在时域上利用电价的分段连续性质。由于簇边界的不精确性，采用了模糊推理技术来处理交点处的数据。作为预测价格的必要步骤，首先使用单独的ANN估算目标时间的预期电力需求。澳大利亚新南威尔士州的电力市场数据用于测试该系统。与将价格数据视为单个连续时间序列的方法相比，已开发的系统显示出显着的性能改进，对于稳定价格的小时而言，MAPE小于2％，对于涵盖时间周期的集群而言，MAPE小于8％。价格暴涨。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号