San Diego Daily Weather Data

San Diego Daily Weather Data

This post describes how to use San Diego Daily Weather Data to forecast air 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: 2024-14.
  • Data Name: San Diego Daily Weather Data.
  • Data Collection Methods: The weather data, including temperature, pressure, and relative humidity, was collected at a weather station equipped with sensors in San Diego, California, over a 3-year period from September 2011 to September 2014, providing 1-minute interval data across various seasons and weather conditions.
  • Data Source: Link
  • Raw data size and format: 122MB, CSV.
  • Number of tags: 11.
TAG DESCRIPTION
air_pressure air pressure measured at the timestamp (hectopascals)
air_temp air temperature measure at the timestamp (degrees Fahrenheit)
avg_wind_direction wind direction averaged over the minute before the timestamp (degrees, with 0 meaning coming from the North, and increasing clockwise)
avg_wind_speed wind speed averaged over the minute before the timestamp (meters per second)
max_wind_direction highest wind direction in the minute before the timestamp (degrees, with 0 being North and increasing clockwise)
max_wind_speed highest wind speed in the minute before the timestamp (meters per second)
min_wind_direction smallest wind direction in the minute before the timestamp (degrees, with 0 being North and increasing clockwise)
min_wind_speed smallest wind speed in the minute before the timestamp (meters per second)
rain_accumulation amount of accumulated rain measured at the timestamp (millimeters)
rain_duration length of time rain has fallen as measured at the timestamp (seconds)
relative_humidity relative humidity measured at the timestamp (percent)

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 2024-14 DataHub in real-time, select the desired tag names from the data of 11 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 San Diego Daily 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 san_diego_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 san_diego_weather table.
curl http://data.yotahub.com/2024-14/datahub-2024-14-San-Diego-Daily-Weather.csv.gz | machbase-neo shell import --input - --compress gzip --header --method append --timeformat ns san_diego_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/2024-14/datahub-2024-14-San-Diego-Daily-Weather.csv.gz | machbase-neo shell import --input - --compress gzip --header --method append --timeformat ns san_diego_weather --user USERNAME --password PASSWORD

4. Experimental Methodology


  • Model Objective: Air Temperature Forecasting.
  • Tags Used: air_temp tag.
  • Model Configuration: DLinear.
  • Goal: Forecasting the air temperature for the next 5 hours using data from the past 10 hours.
  • Learning Method: supervised Learning.
    • Train: Model Training.
    • Test: Model Performance Evaluation Based on Air 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 Batch Dataset.
  • Data Preprocessing
    • MinMax Scaling.

5. Experiment Code


  • 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 Batch Dataset


  • Method for loading data from the Machbase Neo for a single batch size.
  • The code below is for fetching a time range sequentially for a single batch 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 Batch Dataset Result


  • The R2 score for loading the entire dataset resulted in 0.93, loading the batch dataset resulted in same 0.92.




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

datahub/dataset/2024 at main · machbase/datahub
All Industrial IoT DataHub with data visualization and AI source - machbase/datahub

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