Accurately detecting power line defects under diverse weather conditions is crucial for ensuring power grid reliability and safety. Existing power line inspection datasets, while valuable, often lack the diversity needed for training robust machine learning models, particularly for adverse weather scenarios like fog, rain, and nighttime conditions.
Measurement campaign was performed across the entire growing season of 2023. Seven Arduino+LoRaWAN sensors were measuring soil moisture, temperature and pH, as well as solar irradiance in the radius of about 35 km around Gdansk, Poland. Raw data were being collected with a nomadic gateway aboard an UAV and transferred to the cloud for analysis (package part A). Based on the physics informed analysis of the underlying measurement processes anomalies were classified and parametrized.
This repository contains datasets and code with a novel numerical approach aimed at finding a distribution network expansion plan (DNEP) that prevents future congestion and voltage issues. This approach is tested using the modified IEEE 33-bus network. Electricity demand and PV production data for a leap year with a 1-minute resolution was generated using the CREST model from the Loughborough University and is provided as a dataset of future high-load and high-production scenario.
The dataset includes active power measurements for a residential student building located in Bucharest, Romania, collected at 1 frame/ 2 second reporting rate over 12 consecutive months.
Always-on appliances include the refrigerator and the wireless router. Several other appliances are installed in the residential unit: washing machine, lighting fixtures, electrical iron, vacuum cleaner, various ICT charging devices, and air conditioning (seldom used).
The dataset includes active power measurements for a residential student building located in Bucharest, Romania, collected at 1 frame/second reporting rate over 10 consecutive months.
Always-on appliances include the refrigerator and the wireless router. Several other appliances are installed in the residential unit: washing machine, lighting fixtures, electrical iron, vacuum cleaner, various ICT charging devices, and air conditioning (seldom used).
Three exemplary knee bone models derived from ultrasound imaging and their respective magnetic resonance imaging reference.
Apart from the ground truth, a partial scan as accessable by ultrasound imaging as well as full bone model computed by a statistical shape model is provided.
This dataset accounts for the cross-dependency between renewable generation and spot prices of 200 coupled scenarios for the three-dimensional time series comprising the spot prices, wind generation, and solar generation for every hour. To do that, we used the commercial hydrothermal dispatch model SDDP (from PSR Consulting) to simulate Brazilian spot prices based on the scenarios for the main renewable spots of the Brazilian system. We selected the year 2025 as the target year for the contracting horizon.
