Mini-grids are the lowest-cost solutions for electrifying many homes and businesses in rural communities with low energy access. Estimates of the electricity demand of unelectrified customers are a crucial input to selecting mini-grid sites, projecting revenue, and sizing system components to provide adequate capacity while minimizing capital costs. Typical customer survey-based demand estimates for these communities — where there are no historical data — are not reliable, typically overpredicting demand. Here, we test a data-driven approach to demand prediction using survey and smart meter data from 1378 Tanzanian mini-grid customers. We found that models incorporating customer survey data into their predictions consistently out-performed a baseline model that did not. Our best-performing model, the LASSO, predicted daily electricity demand with a median absolute error of 66% and 37% for individual connections and mini-grid sites, respectively. Quantitative measures of variable importance show that most survey data are not useful for estimating demand. These results suggest that surveys should prioritize thorough inventories of prospective customers' currently-owned appliances instead of detailed demographic information or self-reported habits and plans. Pairing shortened questionnaires with smart meter data from preexisting mini-grids can improve estimates of initial customer electricity demand significantly compared to standard field practices.

小型电网是使能源匮乏的农村社区的许多家庭和企业通电的成本最低的解决方案。对非电气化客户的电力需求进行估算，对于选择小型电网站点，预测收入以及确定系统组件的大小以提供足够的容量同时最小化资本成本至关重要。这些社区的典型基于客户调查的需求估计（没有历史数据）是不可靠的，通常会高估需求。在这里，我们使用来自1378坦桑尼亚微型电网客户的调查和智能电表数据，测试了一种数据驱动的方法来预测需求。我们发现，将客户调查数据纳入其预测的模型始终优于没有的基线模型。我们表现​​最好的模型LASSO 预测每日用电量，单个连接和微型电网站点的平均绝对误差中位数分别为66％和37％。重要性的定量度量表明，大多数调查数据对估算需求没有用。这些结果表明，调查应该优先考虑潜在客户当前拥有的设备的完整清单，而不是详细的人口统计信息或自我报告的习惯和计划。与标准的现场实践相比，将缩短的调查表与预先存在的小型电网的智能电表数据配对可以显着改善对初始客户电力需求的估计。重要性的定量度量表明，大多数调查数据对估算需求没有用。这些结果表明，调查应该优先考虑潜在客户当前拥有的设备的完整清单，而不是详细的人口统计信息或自我报告的习惯和计划。与标准的现场实践相比，将缩短的问卷与预先存在的小型电网的智能电表数据配对可以显着改善对初始客户电力需求的估计。重要性的定量度量表明，大多数调查数据对估算需求没有用。这些结果表明，调查应该优先考虑潜在客户当前拥有的设备的完整清单，而不是详细的人口统计信息或自我报告的习惯和计划。与标准的现场实践相比，将缩短的问卷与预先存在的小型电网的智能电表数据配对可以显着改善对初始客户电力需求的估计。