Data
Solar-Radiation-Prediction

Solar-Radiation-Prediction

active ARFF Database: Open Database, Contents: Database Contents Visibility: public Uploaded 24-03-2022 by Dustin Carrion
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Context Space Apps Moscow was held on April 29th 30th. Thank you to the 175 people who joined the International Space Apps Challenge at this location! Content The dataset contains such columns as: "wind direction", "wind speed", "humidity" and temperature. The response parameter that is to be predicted is: "Solar_radiation". It contains measurements for the past 4 months and you have to predict the level of solar radiation. Just imagine that you've got solar energy batteries and you want to know will it be reasonable to use them in future? Acknowledgements Thanks NASA for the dataset. Inspiration Predict the level of solar radiation. Here are some intersecting dependences that i have figured out: Humidity Solarradiation. 2.Temeperature Solarradiation. The best result of accuracy I could get using cross-validation was only 55.

11 features

UNIXTimenumeric32686 unique values
0 missing
Datastring118 unique values
0 missing
Timestring8299 unique values
0 missing
Radiationnumeric14434 unique values
0 missing
Temperaturenumeric38 unique values
0 missing
Pressurenumeric38 unique values
0 missing
Humiditynumeric94 unique values
0 missing
WindDirection(Degrees)numeric17951 unique values
0 missing
Speednumeric37 unique values
0 missing
TimeSunRisestring51 unique values
0 missing
TimeSunSetstring56 unique values
0 missing

19 properties

32686
Number of instances (rows) of the dataset.
11
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
7
Number of numeric attributes.
0
Number of nominal attributes.
Number of instances belonging to the least frequent class.
0
Number of binary attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
Average class difference between consecutive instances.
0
Percentage of missing values.
0
Number of attributes divided by the number of instances.
63.64
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
0
Percentage of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.

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