英语翻译Important methodological issues and techniques for electricity load and price forecasting are presented in.Computationally intensive methods like variable segmentation,multiple modeling,combinations and neural networks for forecasting dem
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英语翻译Important methodological issues and techniques for electricity load and price forecasting are presented in.Computationally intensive methods like variable segmentation,multiple modeling,combinations and neural networks for forecasting dem
英语翻译
Important methodological issues and techniques for electricity load and price forecasting are presented in.Computationally intensive methods like variable segmentation,multiple modeling,combinations and neural networks for forecasting demand side and strategic simulation using artificial agents for the supply side are used.Conceptual framework for designing price forecasting approaches are presented .Modeling competitive market behavior in capturing uncertainty in inputs/outputs with adaptability and transparency is presented.Model of Market-clearing price (MCP) and Database for forecasting electricity prices is described.Forecasting Energy prices using neural networks and fuzzy logic and their combination is discussed in .Historical behaviors of spot prices was evaluated for these methods.Emphasis is placed on the identification of important parameters which influence the forecasted quantity.Basic framework of artificial neural network for load forecasting based on historical load data and temperature is presented .A multi layer perception using three hidden layers is implemented for accurate load forecasting.
英语翻译Important methodological issues and techniques for electricity load and price forecasting are presented in.Computationally intensive methods like variable segmentation,multiple modeling,combinations and neural networks for forecasting dem
重要的方法问题和电力负荷预测技术和价格提出英寸如可变分割,多重造型,组合及预测需求方和战略模拟使用供应方使用人工神经网络计算密集型剂的方法.概念设计的价格预测方法的框架介绍.捕捉建模与适应性和透明度的输入/输出的不确定性,提出了竞争性的市场行为.模型的市场出清价格(MCP)和数据库预测电价描述.能源价格预测采用神经网络与模糊逻辑及其组合进行了讨论研究.现货价格的历史行为进行了评价这些方法.重点放在其影响力的预测数量重要参数辨识.人工神经网络的基本框架,负荷预测根据历史负荷数据和温度主办.一个多层感知使用三个隐藏层是实行负荷预测准确.