Short-term electricity load forecasting (Panama case study)

This is a useful dataset to train and test Machine Learning forecasting algorithms and compare results with the official forecast from weekly pre-dispatch reports. The following considerations should be kept to compare forecasting results with the weekly pre-dispatch forecast: 1. Saturday is the first day of each weekly forecast; for instance, Friday is the last day. 2. A 72 hours gap of unseen records should be considered before the first day to forecast. In other words, next week forecast should be done with records until each Tuesday last hour. Data sources provide hourly records. The data composition is the following: 1. Historical electricity load, available on daily post-dispatch reports, from the grid operator (CND). 2. Historical weekly forecasts available on weekly pre-dispatch reports, both from CND. 3. Calendar information related to school periods, from Panama's Ministery of Education. 4. Calendar information related to holidays, from "When on Earth?" website. 5. Weather variables, such as temperature, relative humidity, precipitation, and wind speed, for three main cities in Panama, from Earthdata. The original data sources provide the post-dispatch electricity load in individual Excel files on a daily basis and weekly pre-dispatch electricity load forecast data in individual Excel files on a weekly basis, both with hourly granularity. Holidays and school periods data is sparse, along with websites and PDF files. Weather data is available on daily NetCDF files. For simplicity, the published datasets are already pre-processed by merging all data sources on the date-time index: 1. A CSV file containing all records in a single continuous dataset with all variables. 2. A CSV file containing the load forecast from weekly pre-dispatch reports. 3. Two Excel files containing suggested regressors and 14 training/testing datasets pairs as described in the PDF file.本数据集可用于训练、测试机器学习预测算法，并可将算法预测结果与每周预调度报告中的官方预测结果进行对比。若需将预测结果与每周预调度预测结果进行对比，需遵循以下规则： 1. 每个周度预测的起始日为周六，举例而言，周五为该周度预测周期的最后一日； 2. 在预测首日之前，需考虑存在72小时的未观测数据间隔。换言之，下一周的预测应使用截至对应周二最后一小时的历史数据完成。 数据源提供逐小时记录数据，数据集的构成如下： 1. 历史电力负荷数据：源自电网运营商CND，可通过每日调度后报告获取； 2. 历史周度预测数据：同样源自CND，可通过每周预调度报告获取； 3. 学期相关日历信息：源自巴拿马教育部； 4. 节假日相关日历信息：源自"When on Earth?"网站； 5. 气象变量数据：涵盖巴拿马三座主要城市的气温、相对湿度、降水量及风速等变量，源自Earthdata平台。 原始数据源以每日为周期，将调度后电力负荷数据存储为独立Excel文件；周度预调度电力负荷预测数据则以每周为周期存储为独立Excel文件，二者均为逐小时粒度。节假日与学期数据较为零散，存储于网页及PDF文件中。气象数据以每日为周期存储为NetCDF格式文件。 为简化使用，已发布的数据集已完成预处理，通过日期时间索引将所有数据源进行融合，具体包含： 1. 单个CSV文件：包含所有变量的完整连续数据集的全部记录； 2. 单个CSV文件：包含每周预调度报告中的电力负荷预测数据； 3. 两个Excel文件：包含PDF文档中提及的推荐回归变量，以及14组训练/测试数据集对。