The daily analysis of loads is one of the most important activities for power utilities in order to be able to meet the energy demand. This analysis not only includes short-term forecasting but it also encompasses the completion of missing load data, known as imputation. In this work we show that adding information of attached or bordering primary substation helps to improve the prediction accuracy in a single substation, since its neighbours may share common weather-related (e.g. temperature, humidity, wind direction, etc.) and human-related (e.g. work-calendar, holidays, cultural consumption patterns, etc.) data. In order to validate these approaches, we test the forecasting and imputation neighbouring methodology on a wide variety of datasets. Results confirm that, given a primary substation, the addition of information from surrounding substations does improve the forecasting and imputation errors.

负荷的日常分析是电力公司为了满足能源需求而最重要的活动之一。该分析不仅包括短期预测，而且还包括完成缺失载荷数据的工作，即估算。在这项工作中，我们表明，添加连接的或毗邻的主要变电站的信息有助于提高单个变电站的预测准确性，因为其邻居可能共享与天气有关的常见信息（例如温度，湿度，风向等）和与人类有关的信息。 （例如，工作日历，假期，文化消费模式等）数据。为了验证这些方法，我们在各种数据集上测试了预测和估算邻近方法。结果证实，给定一级变电站，