Data
Solar-Power

Solar-Power

active ARFF Creative Commons Attribution 4.0 International Visibility: public Uploaded 25-06-2024 by Bruno Belucci Teixeira
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Solar power production in MW recorded per every 4 seconds starting from 01/08/2019 in Australia. From the website: ----- This dataset contains a single very long daily time series representing the solar power production in MW recorded per every 4 seconds starting from 01/08/2019. It was downloaded from the Australian Energy Market Operator (AEMO) online platform. The length of this time series is 7397222. ----- Here is the dataset curated by the Monash Time Series Forecasting Repository. It is not clear which were the preprocessing steps and how did they acquired the data from the original website (https://aemo.com.au/ and http://www.nemweb.com.au/). There are 4 columns: id_series: The id of the time series. date: The date of the time series in the format "%Y-%m-%d". time_step: The time step on the time series. value_0: The values of the time series, which will be used for the forecasting task. Preprocessing: 1 - Renamed columns 'series_name' and 'series_value' to 'id_series' and 'value_0'. 2 - Exploded the 'value' column. 3 - Created 'time_step' column from the exploded data. 4 - Cretead 'date' column from the 'start_timestamp' and 'time_step' column, by offseting the 'start_timestamp' by 4 secods * time_step. 5 - Dropped 'start_timestamp' column. Defined 'id_series' as 'category' and casted 'value_0' to float.

4 features

id_seriesnominal1 unique values
0 missing
value_0numeric1147 unique values
0 missing
time_stepnumeric7397222 unique values
0 missing
datestring7397222 unique values
0 missing

19 properties

7397222
Number of instances (rows) of the dataset.
4
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.
2
Number of numeric attributes.
1
Number of nominal attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
0
Percentage of missing values.
Average class difference between consecutive instances.
50
Percentage of numeric attributes.
0
Number of attributes divided by the number of instances.
25
Percentage of nominal attributes.
Percentage of instances belonging to the most frequent class.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
0
Number of binary attributes.

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