This study discloses a load profile dataset of major household appliances for load flexibility (LF) utilization in residential sectors. To generate the profiles, measured load consumption patterns of each appliance and residents’ time-use survey data are analysed. Representative appliances used during the customers’ daily activities are defined and their power consumption profiles during a single operation cycle are collected according to various scenarios. The entire dataset is available at one-second intervals for daily profiles of individual appliances. The dataset can be used in multiple ways to contribute to the economic and environmental impact estimation of LF application, such as market/rate design, customer targeting, and electricity system planning.

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Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

As part of an effort to monitor electricity usage by plug loads in a new high performance office building, plug load management devices were deployed to enable data collection, analysis, and active control of plug loads. We used a Commercial Off-The-Shelf (COTS) plug load management system to capture relevant data for two different types of multi-function devices (MFDs) in the facility, one of which was tested for use with different power settings. This enabled a quantitative analysis to assess impacts on energy consumption. It was found that a projected 65% reduction in annual energy consumption would result by using a newer, Energy Star compliant model of MFD, and an additional projected 39% reduction in annual energy consumption would result by subsequently changing the time-to-sleep for that MFD. It was also found that it may be beneficial to apply automated analysis with anomaly detection algorithms to detect problems with MFD performance, such as a failure to go to sleep mode or variations in sleep power draw. Furthermore, we observed that energy savings realized by using plug load management devices to de-energize (unplug) MFDs during non-business hours depends on the sleep power draw and time-to-sleep setting. For the MFDs in this study with settings established per the maintenance contract (which were different than factory default values), turning the device off at night and then on in the morning used more energy than leaving it on in sleep mode due to the start-up behavior and excessive time-to-sleep setting of four hours. From this and other assessments, we offer these recommendations to building occupants: reduce MFD time-to-sleep, encourage employees to use the power save button, and apply automated analysis to detect problems with device performance.

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Intro

Electrical disaggregation, also known as non-intrusive load monitoring (NILM) or non-intrusive appliance load monitoring (NIALM), attempts to recognize the energy consumption of single electrical appliances from the aggregated signal. This capability unlocks several applications, such as giving feedback to users regarding their energy consumption patterns or helping distribution system operators (DSOs) to recognize loads which could be shifted to stabilize the electrical grid. The project SmartNIALMeter brought together universities, companies and DSOs and involved the collection of a large data corpus comprising 20 buildings with a total of 100 electrical appliances for a period of up to two years at a sampling interval of five seconds. The variability of the loads, including heat pumps and a charging station for electric vehicles, and the presence of single-phase and three-phase devices make this dataset suitable for several investigations. The total consumption was collected through smart meters and each appliance’s consumption was measured with a dedicated sensor, providing sub-metering for all loads. The dataset can be used to tackle several open research questions, for example to investigate new NILM algorithms able to learn with a limited amount of sub-metered data.

Data Description

For the residential data we chose the Hierarchical Data Format (HDF5), which has been developed for big datasets and fast access. We publish two versions of the SNM dataset - a raw version with minimal curation steps and a version with more extensive preprocessing applied. Both versions of the dataset are organized along the same structure: Each appliance is saved individually as HDF5 and grouped by the building they are measured in. Measurements from individual phases are denoted by the ending L1, L2 or L3 in the file header (e.g. active power L1). This leads to the following file structure:

A detailed description of the dataset, corresponding metadata and the measurement setup can be found in M. Vogel, M. Friedli, M. Camenzind, G. Kniesel, Ch. Klemenjak, G. Gugolz, P. Huber, A. Calatroni, L. Kaufmann, A. Rumsch, A. Paice, "The SmartNIALMeter Electrical Appliance Disaggregation Dataset".
2024-05-02, Data-In-Brief (under review)

Code
The code to generate the preprocessed version of the dataset can be downloaded alongside the dataset. Check on GitHub if an updated versions is available.

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The experiment tested the Transient Heel Loading Device.. Dataset provided by the ESDC. Please refer to the datasets landing page at http://esdcdoi.esac.esa.int/doi/html/data/hre/hreda/30c46c2766864837b5771a88046eeb97.html

This dataset contains EEG recordings that measure cognitive load in individuals performing arithmetic and Stroop tasks. The dataset aims to facilitate the study of mental stress and cognitive load through EEG analysis.

Demographics: - Number of Subjects: 15 (8 males and 7 females) - Average Age: 21 years

Device and Data Collection: - Device: OpenBCI EEG Electrode Cap Kit with Cyton board (8 channels) - Channels: Fp1, Fp2, F7, F3, FZ, F4, F8, C2 (10-20 system) - Sampling Rate: 250 Hz - Duration: 1-2 minutes per session

Tasks and Levels: 1. Stroop Test Dataset: - Natural Level: Baseline brain activity - Low-Level Stress: Simple Stroop questions within 10 seconds - Mid-Level Stress: Standard Stroop questions within 10 seconds with visual interference - High-Level Stress: Complex Stroop questions with a 20-second limit

1. Arithmetic Test Dataset:
   • Natural Level: Baseline brain activity
   • Low-Level Stress: Simple arithmetic within 10 seconds
   • Mid-Level Stress: Slightly more complex arithmetic within 20 seconds
   • High-Level Stress: Complex arithmetic within 20 seconds

File Format: - Data files are in plain text format (.txt).

Using the Dataset: - Each .txt file contains EEG data corresponding to a single session of either the arithmetic or Stroop task at one of the four cognitive load levels. - The first line of each file may contain metadata specific to the recording (e.g., subject ID, condition). - Subsequent lines contain EEG signal values recorded at the specified sampling rate.

Directory Structure: /raw_data/ /Arithmetic_Data/ - natural-1.txt to natural-15.txt - lowlevel-1.txt to lowlevel-15.txt - midlevel-1.txt to midlevel-15.txt - highlevel-1.txt to highlevel-15.txt /Stroop_Data/ - natural-1.txt to natural-15.txt - lowlevel-1.txt to lowlevel-15.txt - midlevel-1.txt to midlevel-15.txt - highlevel-1.txt to highlevel-15.txt

The dataset used in this work is composed by four participants, two men and two women. Each of them carried the wearable device Empatica E4 for a total number of 15 days. They carried the wearable during the day, and during the nights we asked participants to charge and load the data into an external memory unit. During these days, participants were asked to answer EMA questionnaires which are used to label our data. However, some participants could not complete the full experiment or some days were discarded due to data corruption. Specific demographic information, total sampling days and total number of EMA answers can be found in table I.

Table I. Summary of participants' collected data.

This dataset provides three different type of labels. Activeness and happiness are two of these labels. These are the answers to EMA questionnaires that participants reported during their daily activities. These labels are numbers between 0 and 4.
These labels are used to interpolate the mental well-being state according to [1] We report in our dataset a total number of eight emotional states: (1) pleasure, (2) excitement, (3) arousal, (4) distress, (5) misery, (6) depression, (7) sleepiness, and (8) contentment.

The data we provide in this repository consist of two type of files:

• CSV files: These files contain physiological signals recorded during the data collection process. The first line of each CSV file defines the timestamp by which data started being sampled. The second line defines the sampling frequency used for gathering the signal. From the third line until the end of the file, one can find sampled datapoints.
  
• Excel files: These files contain the labels obtained from EMA answers. It is indicated the timestamp at which the answer was registered. Labels for pleasure, activeness and mood can be found in this file.

NOTE: Files are numbered according to each specific sampling day. For example, ACC1.csv corresponds to the signal ACC for sampling day 1. The same applied to excel files.

Code and a tutorial of how to labelled and extract features can be found in this repository: https://github.com/edugm94/temporal-feat-emotion-prediction

References:

[1] . A. Russell, “A circumplex model of affect,” Journal of personality and social psychology, vol. 39, no. 6, p. 1161, 1980

pet-shop32-per-device-4_9062874564 Dataset

  Dataset Description

pet-shop32-per-device-4_9062874564 is a generated dataset designed to support the development of domain specific embedding models for retrieval tasks.

  Associated Model

This dataset was used to train the pet-shop32-per-device-4_9062874564 model.

  How to Use

To use this dataset for model training or evaluation, you can load it using the Hugging Face datasets library as follows: from datasets… See the full description on the dataset page: https://huggingface.co/datasets/florianhoenicke/pet-shop32-per-device-4_9062874564.

This dataset contains hyperspectral images obtained using SPECIM IQ for the Munsell soil color chart (MSC).

The hyperspectral images are stored in ENVI format. For those who are only interested in the endmember spectra for the MSC, we also provided the spectral library .sli and .hdr inside the endmembers folder.

The acquisition details for each image can be found in the .hdr file and metadata folder inside the whole folder. For the whole image, the acquisition details are:

Table 1. Acquisition details

The dataset is organized into several folders, each containing different types of datasets.

• whole folder contains the entire scene hyperspectral image. This folder contains capture, metadata, and results subfolder.
  • .png inside the folder is natural color plotting (RGB from default bands in Table. 1) from captured hyperspectral image.
  • capture folder contains dark reference, white reference and radiance data
  • metadata folder contains the metadata of the acquisition and device settings.
  • results contains the reflectance calculated by the device (in .dat, .hdr and rendered natural plotting in .png ) from the hyperspectral camera and .png images of the scene, background, and viewfinder from the device's RGB camera.

• chips folder contains only the cropped 20*20 voxels for each color chip reflectances. Each page has its own folder and each folder contains .hdr and .img for each color chip.

• endmembers folder contains the spectral library (.sli and .hdr). Each page in MSC have their own .sli and .hdr.

Some of the code snippets that might help to read the dataset

using python spectral library to load the dataset

from spectral import *
import matplotlib.pyplot as plt

# load the hyperspectral image .hdr and store it to a variable
hsi = open_image(PATH)

# get the natural RGB plotting of the hyperspectral image using the SPECIM main band
hsi_rgb = hsi[:,:,[70,53,19]]

# read the spectral library .sli and store it to a variable
sli = open_image(PATH)
# plot the first endmember 
plt.plot(sli.spectra[0])
# get the endmembers name
sli.names

if you have any question kindly reach me on riestiyf@stud.ntnu.no

The 2017 version of Plug-Load Appliance Identification Dataset (PLAID), containing voltage and current measurements from different electrical household appliances sampled at 30 kHz, collected at 65 different locations in Pittsburgh, Pennsylvania (US).

We provide MATLAB binary files (.mat) and comma separated values files of data collected from a pilot study of a plug load management system that allows for the metering and control of individual electrical plug loads. The study included 15 power strips, each containing 4 channels (receptacles), which wirelessly transmitted power consumption data approximately once per second to 3 bridges. The bridges were connected to a building local area network which relayed data to a cloud-based service. Data were archived once per minute with the minimum, mean, and maximum power draw over each one minute interval recorded. The uncontrolled portion of the testing spanned approximately five weeks and established a baseline energy consumption. The controlled portion of the testing employed schedule-based rules for turning off selected loads during non-business hours; it also modified the energy saver policies for certain devices. Three folders are provided: “matFilesAllChOneDate” provides a MAT-file for each date, each file has all channels; “matFilesOneChAllDates” provides a MAT-file for each channel, each file has all dates; “csvFiles” provides comma separated values files for each date (note that because of data export size limitations, there are 10 csv files for each date). Each folder has the same data; there is no practical difference in content, only the way in which it is organized.

Detailed household load and solar generation in minutely to hourly resolution. This data package contains measured time series data for several small businesses and residential households relevant for household- or low-voltage-level power system modeling. The data includes solar power generation as well as electricity consumption (load) in a resolution up to single device consumption. The starting point for the time series, as well as data quality, varies between households, with gaps spanning from a few minutes to entire days. All measurement devices provided cumulative energy consumption/generation over time. Hence overall energy consumption/generation is retained, in case of data gaps due to communication problems. Measurements were conducted 1-minute intervals, with all data made available in an interpolated, uniform and regular time interval. All data gaps are either interpolated linearly, or filled with data of prior days. Additionally, data in 15 and 60-minute resolution is provided for compatibility with other time series data. Data processing is conducted in Jupyter Notebooks/Python/pandas.

The Medicare Durable Medical Equipment, Devices & Supplies by Referring Provider and Service dataset contains information on usage, payments and submitted charges organized by National Provider Identifier (NPI), Healthcare Common Procedure Coding System (HCPCS) code, and supplier rental indicator. Note: This full dataset contains more records than most spreadsheet programs can handle, which will result in an incomplete load of data. Use of a database or statistical software is required.

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The FDA Device Dataset by Dataplex provides comprehensive access to over 24 million rows of detailed information, covering 9 key data types essential for anyone involved in the medical device industry. Sourced directly from the U.S. Food and Drug Administration (FDA), this dataset is a critical resource for regulatory compliance, market analysis, and product safety assessment regarding.

Dataset Overview:

This dataset includes data on medical device registrations, approvals, recalls, and adverse events, among other crucial aspects. The dataset is meticulously cleaned and structured to ensure that it meets the needs of researchers, regulatory professionals, and market analysts.

24 Million Rows of Data:

With over 24 million rows, this dataset offers an extensive view of the regulatory landscape for medical devices. It includes data types such as classification, event, enforcement, 510k, registration listings, recall, PMA, UDI, and covid19 serology. This wide range of data types allows users to perform granular analysis on a broad spectrum of device-related topics.

Sourced from the FDA:

All data in this dataset is sourced directly from the FDA, ensuring that it is accurate, up-to-date, and reliable. Regular updates ensure that the dataset remains current, reflecting the latest in device approvals, clearances, and safety reports.

Key Features:

• Comprehensive Coverage: Includes 9 key device data types, such as 510(k) clearances, premarket approvals, device classifications, and adverse event reports.

• Regulatory Compliance: Provides detailed information necessary for tracking compliance with FDA regulations, including device recalls and enforcement actions.

• Market Analysis: Analysts can utilize the dataset to assess market trends, monitor competitor activities, and track the introduction of new devices.

• Product Safety Analysis: Researchers can analyze adverse event reports and device recalls to evaluate the safety and performance of medical devices.

Use Cases: - Regulatory Compliance: Ensure your devices meet FDA standards, monitor compliance trends, and stay informed about regulatory changes.

• Market Research: Identify trends in the medical device market, track new device approvals, and analyze competitive landscapes with up-to-date and historical data.

• Product Safety: Assess the safety and performance of medical devices by examining detailed adverse event reports and recall data.

Data Quality and Reliability:

The FDA Device Dataset prioritizes data quality and reliability. Each record is meticulously sourced from the FDA's official databases, ensuring that the information is both accurate and up-to-date. This makes the dataset a trusted resource for critical applications, where data accuracy is vital.

Integration and Usability:

The dataset is provided in CSV format, making it compatible with most data analysis tools and platforms. Users can easily import, analyze, and utilize the data for various applications, from regulatory reporting to market analysis.

User-Friendly Structure and Metadata:

The data is organized for easy navigation, with clear metadata files included to help users identify relevant records. The dataset is structured by device type, approval and clearance processes, and adverse event reports, allowing for efficient data retrieval and analysis.

Ideal For:

• Regulatory Professionals: Monitor FDA compliance, track regulatory changes, and prepare for audits with comprehensive and up-to-date product data.

• Market Analysts: Conduct detailed research on market trends, assess new device entries, and analyze competitive dynamics with extensive FDA data.

• Healthcare Researchers: Evaluate the safety and efficacy of medical devices product data, identify potential risks, and contribute to improved patient outcomes through detailed analysis.

This dataset is an indispensable resource for anyone involved in the medical device industry, providing the data and insights necessary to drive informed decisions and ensure compliance with FDA regulations.

Provide the location, power supply range, main transformer capacity, and maximum load of "ultra-high voltage and primary substations" and "secondary substations" for the reference of medium and high voltage and ultra-high voltage large users in their investment and establishment planning in the early stage.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.