Learning-based Energy Consumption Prediction

As more people send information to the cloud-fog infrastructure, this brings many problems to the management of computer energy consumption. Therefore, energy consumption management of servers, fog devices and cloud computing platform should be investigated to comply with the Green IT requirement. In this paper, we propose an energy consumption prediction model consisting of several components such as hardware design, data pre-processing, characteristics extraction and selection. Our main goal is to develop a non-invasive meter based on a network of sensors that includes a microcontroller, the MQTT communication protocol and the energy measurement module. This meter measures voltage, current, power, frequency, energy and power factor while a dashboard is used to present the energy measurements in real-time. In particular, we perform measurements using a workstation that has similar characteristics to the servers of a Datacenter locate at the Information Technology Center in ESPOL, which currently provide this type of services in Ecuador. For convenience, we evaluated different linear regression models to select the best one and to predict future energy consumption based on the several measurements from the workstation during several hours which enables the consumer to optimize and to reduce the maintenance costs of the IT equipment. The supervised machine learning algorithms presented in this work allow us to predict the energy consumption by hours and by days.