energy consumption
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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3482 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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807 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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230 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.
- Categories:
300 Views