European Weather Data

European Weather Data

This post describes how to use European Weather Data to prediction temperature through AI learning.

Table of Contents

  1. Data Introduction
  2. Data Visualization with Machbase Neo
  3. Table Creation and Data Upload in Machbase Neo
  4. Experimental Methodology
  5. Experiment Code
  6. Experimental Results

1. Data Introduction


  • DataHub Serial Number: 2025-2.
  • Data Name: European Weather Data.
  • Data Collection Methods: The NASA MERRA-2 reanalysis data includes hourly radiation and temperature data for Europe aggregated by Renewables.ninja, with the averages calculated using population-weighted data for each region.
  • Data Source: Link
  • Raw data size and format: 222MB, CSV.
  • Number of tags: 84.
Tag Description
{code}_radiation_diffuse_horizontal radiation_diffuse_horizontal weather variable for {code} in W/m2
{code}_radiation_direct_horizontal radiation_direct_horizontal weather variable for {code} in W/m2
{code}_temperature temperature weather variable for {code} in degrees C
utc_timestamp Start of time period in Coordinated Universal Time
Code Country
AT Austria
BE Belgium
BG Bulgaria
CH Switzerland
CZ Czech Republic
DE Germany
DK Denmark
EE Estonia
ES Spain
FI Finland
FR France
GB United Kingdom
GR Greece
HR Croatia
HU Hungary
IE Ireland
IT Italy
LT Lithuania
LU Luxembourg
LV Latvia
NL Netherlands
NO Norway
PL Poland
PT Portugal
RO Romania
SE Sweden
SI Slovenia
SK Slovakia

2. Data Visualization with Machbase Neo


  • Data visualization is possible through the Tag Analyzer in Machbase Neo.
  • Select desired tag names and visualize them in various types of graphs.
  • Below, access the 2025-2 DataHub in real-time, select the desired tag names from the data of 84 tags, visualize them, and preview the data patterns.
DataHub Viewer

3. Table Creation and Data Upload in Machbase Neo


  • In the DataHub directory, use setup.wrk located in the European Weather Dataset folder to create tables and load data, as illustrated in the image below.

1) Table Creation

  • The table is created immediately upon pressing the "Run" button in the menu.
  • If the eu_weather table exists, execute the first line and then the second. If it does not exist, start from the second line.

2) Data Upload


  • Loading tables in two different ways.
Method 1) Table loading method using TQL in Machbase Neo (since machbase-neo v8.0.29-rc1

  • Pros

    • Markbase Neo loads as soon as you hit the launch button.
  • Cons

    • Slower table loading speed compared to other method.
Method 2) Loading tables using commands

  • Pros

    • Fast table loading speed.
  • Cons

    • The table loading process is cumbersome.
    • Run cmd window - Change machbase-neo path - Enter command in cmd window.
  • If run the below script from the command shell, the data will be entered at high speed into the eu_weather table.
curl http://data.yotahub.com/2025-2/datahub-2025-2-EU-weather.csv.gz | machbase-neo shell import --input - --compress gzip --header --method append --timeformat ns eu_weather
  • If specify a separate username and password, use the --user and --password options (if not sys/manager) and add the options as shown below.
curl http://data.yotahub.com/2025-2/datahub-2025-2-EU-weather.csv.gz | machbase-neo shell import --input - --compress gzip --header --method append --timeformat ns eu_weather --user USERNAME --password PASSWORD

4. Experimental Methodology


  • Model Objective: Austria Temperature Forecasting.
  • Tags Used: AT_temperature.
  • Model Configuration: PatchMixer.
  • Learning Method: supervised Learning.
    • Train: Model Training.
    • Test: Model Performance Evaluation Based on Austria Temperature Forecasting.
  • Model Optimizer: Adam.
  • Model Loss Function: Mean Squared Error.
  • Model Performance Metric: Mean Squared Error & R2 Score.
  • Data Loading Method
    • Loading the Entire Dataset.
    • Loading the Fetch Dataset.
  • Data Preprocessing
    • MinMax Scaling.

5. Experiment Code


  • Composed of three methods.
  • Data Information: Outputs general information about the data.
  • Visual Information: correlation heatmap, plot, Decomposition about the data.
  • Statistical Test: ADF Test, KPSS Test, PP Test, ljung box Test, Arch Test, VIF Test about the data.

Austria Temperature Forecasting

  • Below is the code for each of the two ways to get data from the database.
  • If all the data can be loaded and trained at once without causing memory errors, then method 1 is the fastest and simplest.
  • If the data is too large, causing memory errors, then the batch loading method proposed in method 2 is the most efficient.

Method 1) Loading the Entire Dataset


  • The code below is implemented in a way that loads all the data needed for training from the database all at once.
  • It is exactly the same as loading all CSV files (The only difference is that the data is loaded from Machbase Neo).
  • Pros
    • Can use the same code that was previously utilizing CSVs (Only the loading process is different).
  • Cons
    • Unable to train if trainable data size exceeds memory size.

Method 2) Loading the Fetch Dataset


  • Method for loading data from the Machbase Neo for a buffer size.
  • Pros
    • It is possible to train the model regardless of the data size, no matter how large it is.
  • Cons
    • It takes longer to train compared to method 1.

6. Experimental Results


Method 1) Loading the Entire Dataset Result


Method 2) Loading the Fetch Dataset Result


  • The R2 score shows high performance above 0.9 in both methods.
  • The actual scale-based MSE value is also around 0.1, demonstrating decent performance.

※ Various datasets and tutorial codes can be found in the GitHub repository below.

datahub/dataset at main · machbase/datahub
All Industrial IoT DataHub with data visualization and AI source - machbase/datahub

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