请将下面的英文翻译成中文 感激不尽!谢绝机器直翻!

请将下面的英文翻译成中文 感激不尽!谢绝机器直翻!
There are various methods adopted for the forecasting of future market price. One approach to predict the market behaviors is regression. The basic idea is to use the historical prices, quantity and other information such as load forecast, and temperatures to predict the MCPs. That is, use history and other estimated factors in the future to ‘‘fit’’ and ‘‘extrapolate’’ the prices and quantity.
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.
该论文是基于神经网络的电价预测 谢谢各位啦！