energy consumption
This dataset supports a review and an in-depth analysis on the environmental impacts of integrated circuits (ICs). The paper is currently under review.
We gathered data from foundry reports, industry roadmaps, scientific literature, and commercial state-of-the-art LCA databases. All assumptions are detailed.
More information can be found on the GitHub repository : https://github.com/ThibaultPirson/environmental-footprint-IC.
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385 Views
Two electric vehicles were used in this study, namely the Renault Zoe Q210 2016 and the Renault Kangoo ZE 2018. The EVs were equipped with data loggers connected to the CAN bus recording data on the HV battery current, voltage, SoC, and instantaneous speeds. We also used a GPS logger mobile application to record GPS tracks and altitudes. Data were collected from six drivers (four men and two women) with varying levels of driving experience (from less than two months to more than 10 years) on a variety of roads and driving conditions for nearly 200 kilometers
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2378 Views
The fast development of urban advancement in the past decade requires reasonable and realistic solutions for transport, building infrastructure, natural conditions, and personal satisfaction in smart cities. This paper presents and explores predictive energy consumption models based on data-mining techniques for a smart small-scale steel industry in South Korea. Energy consumption data is collected using IoT based systems and used for prediction.
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1187 Views
This file contains one-year measurements of demand (average 11 kWh/day), Electric vehicle charging (3 kW rating), and PV generation (3.3 kWp) for a household in London, UK.
This dataset is associated with the following paper:
A. A. R. Mohamed, R. J. Best, X. A. Liu and D. J. Morrow, "A Comprehensive Robust Techno-Economic Analysis and Sizing Tool for the Small-Scale PV and BESS," in IEEE Transactions on Energy Conversion, doi: 10.1109/TEC.2021.3107103.
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2451 Views
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.
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4720 Views
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.
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15388 Views
Performance of Wireless Sensor Networks (WSN) based on IEEE 802.15.4 and Time Slotted Channel Hopping (TSCH) has been shown to be mostly predictable in typical real-world operating conditions. This is especially true for performance indicators like reliability, power consumption, and latency. This article provides and describes a database (i.e., a set of data acquired with real devices deployed in a real environment) about measurements on OpenMote B devices, implementing the 6TiSCH protocol, made in different experimental configurations.
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2502 Views
This dataset contains daily maximum load data with the average demand, customer count and PV capacity at two substations Arkana and Muchea, Western Australia used in the accepted IEEE Transactions on Power Systemspaper titled “The Use of Extreme Value Theory for Forecasting Long-Term Substation Maximum Electricity Demand” by Li and Jones (2019). The dataset spans from 01/01/2008 to 30/06/2022, part history (01/01/2008 to 16/09/2018) and part forecast (17/09/2018 to 30/06/2022). The dataset is beneficial to various research such as long-term load forecast.
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697 Views
This dataset contains daily maximum load data with the average demand, customer count and PV capacity at two substations Arkana and Muchea, Western Australia used in the accepted IEEE Transactions on Power Systemspaper titled “The Use of Extreme Value Theory for Forecasting Long-Term Substation Maximum Electricity Demand” by Li and Jones (2019). The dataset spans from 01/01/2008 to 30/06/2022, part history (01/01/2008 to 16/09/2018) and part forecast (17/09/2018 to 30/06/2022). The dataset is beneficial to various research such as long-term load forecast.
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195 Views