论文标题
IEEE-CIS第三技术挑战中预测和优化的方法论
Methodology for forecasting and optimization in IEEE-CIS 3rd Technical Challenge
论文作者
论文摘要
该报告提供了我在IEEE-CIS第三技术挑战中使用的方法的描述。 为了进行预测，我使用了澳大利亚电表学局（BOM）提供的太阳变量以及欧洲中等范围天气预报中心（ECMWF）提供的许多天气变量。 由于观察到随着时间的推移，建筑物和所有太阳能实例都被一起训练。使用的其他变量包括基于一年中的日子和一天中的傅立叶值，以及一周中天数的二进制变量。 时间序列的开始日期是根据阶段1仔细调整的，并使用清洁和阈值来降低每个时间序列的观察到的错误率。 为了优化，使用开发的预测使用了四步方法。首先，为重复和重复的加和一次性活动求解了混合企业计划（MIP），然后使用混合构成二次程序（MIQP）扩展其中的每一个。 从两个（“数组”和“元组”方法中的“数组”之一）中选择了一般策略，而从五个（“无强制放电”）中选择了特定的步骤改进策略。
This report provides a description of the methodology I used in the IEEE-CIS 3rd Technical Challenge. For the forecast, I used a quantile regression forest approach using the solar variables provided by the Bureau of Meterology of Australia (BOM) and many of the weather variables from the European Centre for Medium-Range Weather Forecasting (ECMWF). Groups of buildings and all of the solar instances were trained together as they were observed to be closely correlated over time. Other variables used included Fourier values based on hour of day and day of year, and binary variables for combinations of days of the week. The start dates for the time series were carefully tuned based on phase 1 and cleaning and thresholding was used to reduce the observed error rate for each time series. For the optimization, a four-step approach was used using the forecast developed. First, a mixed-integer program (MIP) was solved for the recurring and recurring plus once-off activities, then each of these was extended using a mixed-integer quadratic program (MIQP). The general strategy was chosen from one of two ("array" from the "array" and "tuples" approaches) while the specific step improvement strategy was chosen from one of five ("no forced discharge").
