This Dataset, as a Matlab script, shows input voltages vectors and output voltage reference vector for improved control of input power factor in Multiphase Conventional Matrix Converters (MCMC) using the transfer function of the load angle. Compared to the direct space--vector modulation techniques, the proposed solution can rely on the load parameters and obtain the greater value of an input displacement angle in a certain range of voltage transfer ratio and the load power factor.
The heating and electricity consumption data are the results of an energy audit program aggregated for multiple load profiles of a residential customer. These profiles include HVAC systems loads, convenience power, elevator, etc. The datasets are gathered between December 2010 and November 2018 with a one-hour timestep resolution, thereby containing 140,160 measurements, half of which is for heat or electricity. In addition to the historical energy consumption values, a concatenation of weather variables is also available.
This is a publicly available dataset of heating and electricity consumption profiles, aggregated from multiple load profiles of a residential customer. The dataset is gathered between December 2010 and November 2018 with a one-hour time step resolution, thereby containing 70,080 measurements. In addition to the historical energy consumption values, a concatenation of meteorological variables is also included. The weather variables are air pressure, temperature, and humidity plus wind speed and solar irradiation at the predetermined location.
An efficient artificial scenerio generator for EV load simulation modeling has been developed acquiring probabilistic method for characterizing the stochastic nature of EVs and generate the schedule of EVs charging to ultimately achieve the EV load profile for impact study of EVs on distribution network. Model has been tested under different settings and by generating different scenarios to make it viable, realistic and adaptable to any defined characteristics.
This repository includes the energy consumption data set of a Data Server that is running in the facilities of the Information Technology Center (CTI) of the Escuela Superior Politécnica del Litoral (ESPOL). In addition, it includes Matlab scripts to perform the prediction of energy consumption. The data acquisition equipment was implemented in the Electronic Prototype Development Matter of the Faculty of Electrical Engineering and Computing (FIEC), based on the ESP32 hardware.
Data were collected from an HP Z440 workstation for 245 days (35 weeks) with a sampling rate of one value per second. The columns represent the following variables:
- Voltage (V)
- Currennt (A)
- Power (PA) - Watts (W)
- Frecuency - Hertz (Hz)
- Active Energy - kilowatts per hour (KWh)
- Power factor - Adimentional
- ESP32 temperature - Centigrade Degrees (°C)
- CPU consumption - Percentage (%)
- CPU power consumption - Percentage (%)
- CPU temperature - Centigrade Degrees (°C)
- GPU consumption - Percentage (%)
- GPU power consumption - Percentage (%)
- GPU temperature - Centigrade Degrees (°C)
- RAM memory consumption - Percentage (%)
- RAM memory power consumption - Percentage (%)
This dataset contains solar radiation data from Coto Laurel Puerto From May 20,2019 to May 19, 2020. Additional power ramp rate data is provided for seven different methods: Ramp saturation, first order low-pass filter, second order low-pass filter, moving average, exponential moving average, enhanced linear exponential smoothing, and predictive dynamics smoothing.
The data is in .mat format. Please use MATLAB to access to it. Ramp rate data results are in the out.mat file. Daily data is available under the name of each PRRC method.
These datasets collect sensorial information about collaborative robot functioning. We recorded information from two different kinds of robots UR3e and UR10e. This dataset is used for data-driving modeling of the power consumption of cobots. The datasets have the following information: recording time, trajectory ID, joints' positions, joints' velocities, motor currents, motor torques, motor voltages, end effector position, force and momentum exerted to the end effector, current and voltage of the robot.