Accurate determination of the geothermal gradient is critical for assessing
the geothermal energy potential of a given region. Of particular interest is
the case of Colombia, a country with abundant geothermal resources. A history
of active oil and gas exploration and production has left drilled boreholes in
different geological settings, providing direct measurements of the geothermal
gradient. Unfortunately, large regions of the country where geothermal
resources might exist lack such measurements. Indirect geophysical measurements
are costly and difficult to perform at regional scales. Computational thermal
models could be constructed, but they require very detailed knowledge of the
underlying geology and uniform sampling of subsurface temperatures to be
well-constrained. We present an alternative approach that leverages recent
advances in supervised machine learning and available direct measurements to
predict the geothermal gradient in regions where only global-scale geophysical
datasets and course geological knowledge are available. We find that a Gradient
Boosted Regression Tree algorithm yields optimal predictions and extensively
validate the trained model. We show that predictions of our model are within
12\% accuracy and that independent measurements performed by other authors
agree well with our model. Finnally, we present a geothermal gradient map for
Colombia that highlights regions where futher exploration and data collection
should be performed.

使用监督机器学习和可用的直接测量结果预测地热梯度，并得到 12％精度内的准确预测，验证了模型的可靠性，并呈现了哥伦比亚的地热梯度地图。

哥伦比亚地热梯度预测：机器学习方法

Predicting the Geothermal Gradient in Colombia: a Machine Learning  Approach

Since no solutions have been proposed in Colombia that seek to reduce the
consumption of electricity at the residential level, this paper describes the
design and implementation of a simple prototype of a low-cost home energy
management system (HEMS). The objective of this plat-form is to monitor the
energy consumption of typical household devices so that users can access the
consumption of each device separately and then establish the strategy that
allows them to reduce energy consumption at home. In order to demonstrate that
our system is viable, the system has been evaluated by measuring weekly energy
consumption with the on-line and off-line HEMS using a test bench with typical
household devices in a Sincelejo typical household. The evaluation has shown
that with the installation of this HEMS, consumption is reduced by 27%. This
shows that it is possible to achieve a good reduction percentage with a
low-cost system.

在哥伦比亚，本文描述了一种低成本家庭能源管理系统（HEMS）的简单原型的设计和实施，旨在监测典型家用设备的能源消耗，用户可以单独访问每个设备的能源消耗并制定降低家庭能源消耗的策略，评估结果显示，通过安装该 HEMS 系统，能源消耗减少了 27%。