论文标题
在薄电力市场中的预测价格预测型号
Day Ahead Price Forecasting Models in Thin Electricity Market
论文作者
论文摘要
印度的电力市场（大坝）的前一天很薄，但增长。一致的价格预测对于它们在投资组合优化模型中的利用很重要。具有标准外源变量（例如特殊日期效应等）的单变量或多元模型并不总是有用的。需求和供应的驱动因素包括大型地理区域的天气变化，电力系统的中断以及合同的突然变化，这些合同导致玩家访问权力交流。这些需要在预测模型中考虑这些。观察到这样的模型可以大大减少预测误差，从而在条件下胜过其他模型，而这些模型既不频繁，也不是在定义的间隔内重复出现的。本文为印度开发模型，并使用模型置信集（MCS）方法来测试这些模型的实用性，该模型可以选择最佳模型。在现场商业环境中，已开发了两年的印度电力公司的方法。
Day Ahead Electricity Markets (DAMs) in India are thin but growing. Consistent price forecasts are important for their utilization in portfolio optimization models. Univariate or multivariate models with standard exogenous variables such as special day effects etc. are not always useful. Drivers of demand and supply include weather variations over large geographic areas, outages of power system elements and sudden changes in contracts which lead the players to access power exchanges. These needs to be considered in forecasting models. Such models are observed to considerably reduce forecasting errors by outperforming other models under conditions, which are neither infrequent nor recur at defined intervals. This paper develops models for India and tests the utility of these models using Model Confidence Set (MCS) approach which picks up the best models. The approach has been developed for a power utility in India over a period of two years in live business environment.
