Most machine learning (ML) proposals in the Internet of Things (IoT) space are designed and evaluated on pre-processed datasets, where the data acquisition and cleaning steps are often considered a black box. Therefore, the data acquisition stage requires additional data cleaning/anomaly techniques, which translate to additional resources, energy, and storage.
We introduce a dataset concerning electric-power consumption-related features registed in seven main municipalities of Nariño, Colombia, from 2010 to 2016. The dataset consists of 4423 socio-demographic characteristics, and 6 power-consumption-referred measured values. Data were fully collected by the company Centrales Eléctricas de Nariño (CEDNEAR) according to the client consumption records.
The electronic system has been design to know the position human body. Of this way the system use a three axis accelerometer to detect five common positions (i) ventral decubitus, (ii) right lateral decubitus, (iii) left lateral decubitus, (iv) supine decubitus and (v) seated. The sensor data was acquire with ten diferrents persons, their each positions was how they felt confortable. The accelerometer acquire data from 3 axis possible (X,Y,Z)
Pressure position sensors and actuators are represented by red large and blue circles on the seating, respectively. Black circles display ultrasound sensors in the backrest, yellow arrows show output peripheral, and red arrows indicate peripheral input into the analog-digital converter.
S1= Preasure sensor, value 0-1023 decimal
S2= Preasure sensor, value 0-1023 decimal
S3= Preasure sensor, value 0-1023 decimal
S4= Ultrasonic sensor, value 0-15 cm