Weather
Data from measurements in Åre during the winter 2018-2019.
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This dataset includes gathering 18-month raw PV data at time intervals of about 200 µs (5 kHz sampling). A post-processing 365-day day-by-day downsampled version, converted to 10 ms intervals (100 Hz sampling), is also included. The end results are two databases: 1. The original, raw, data, including both fast (short circuit, 200 µs) and slow (sweep, 2.5-3.9 s) information for 18 months. These show intervals of missing points, but are provided to allow potential users to reproduce any new work. 2.
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Accurate short-term load forecasting (STLF) plays an increasingly important role in reliable and economical power system operations. This dataset contains The University of Texas at Dallas (UTD) campus load data with 13 buildings, together with 20 weather and calendar features. The dataset spans from 01/01/2014 to 12/31/2015 with an hourly resolution. The dataset is beneficial to various research such as STLF.
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It is possible to construct "aerosol cytometers" based on different types of Zhulanov's laser aerosol counters | diffusion aerosol spectrometers (DAS) [1-8] and "hydrosol cytometers" based on hydrosol particle counters (adopted for ocean marine, ocean and hydrothermal conditions [9,10]).
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The dataset is supporting material for Colli et al 2018 paper. The paper describes the results obtained by the application of an innovative environmental monitoring technique able to estimate rainfall intensity in real-time by processing the attenuation of microwave satellite link signal measured by low cost sensors. The satellite used during our work, Turksat 42° E, belong to the plethora of satellites operating for television and radio channel broadcasting.
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With the advent of huge data availability and cheap processing power to derive insights from data ,applications of Big Data and Machine Learning are gaining popularity in every industry. Now, Predicting about future is no more an alternate but a necessity to increase efficiency with accurate output. Forecasting for a shorter duration (Nowcasting) fits perfectly in this space to estimate the final production for better planning and control.
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Early data was collected by technicians using mercury thermometers, where any variation in the visit time impacted measurements. In the 1940s, the construction of airports caused many weather stations to be moved. In the 1980s, there was a move to electronic thermometers that are said to have a cooling bias.
Given this complexity, there are a range of organizations that collate climate trends data. The three most cited land and ocean temperature data sets are NOAA’s MLOST, NASA’s GISTEMP and the UK’s HadCrut.
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