diff --git a/quartz_solar_forecast/dataset/README.md b/quartz_solar_forecast/dataset/README.md new file mode 100644 index 00000000..59a8d2c1 --- /dev/null +++ b/quartz_solar_forecast/dataset/README.md @@ -0,0 +1,10 @@ +## Test set analysis +The test set contains the data used for testing the solar forecast models developed. It contains 2500 data points, with `pv_id` and `timestamps` of when the data was collected. Upon analysing the dataset, the following observations were made: +1. The data is pretty evenly distrbuted throughout the year, with the highest number of data points from the month of May (256 data points) +2. Looking at distribution by hour of the day, the highest number of data points is from 19:00 hrs (132 data points), and the least from 00:00 hrs (87 data points) + +By analysing the metadata, available at [Hugging Face](https://huggingface.co/datasets/openclimatefix/uk_pv), along with the test set, it can be observed that: +1. Most of the data in the test set has a tilt angle of 30-34 degrees +2. The maximum kwp is 4.0 & the minmum kwp is 2.25 in the test set. + +A detailed anaysis of the test set can be found at quartz_solar_forecast/dataset/dataset_analysis/test_set_analysis.ipynb \ No newline at end of file diff --git a/quartz_solar_forecast/dataset/dataset_analysis/test_set_analysis.ipynb b/quartz_solar_forecast/dataset/dataset_analysis/test_set_analysis.ipynb new file mode 100644 index 00000000..ea0053c6 --- /dev/null +++ b/quartz_solar_forecast/dataset/dataset_analysis/test_set_analysis.ipynb @@ -0,0 +1,4880 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Analysing the test set " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# To view the plots and interact with it, head to https://nbviewer.org and paste the link of this notebook" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/bikrambaruah/opt/anaconda3/envs/assignment/lib/python3.7/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "import plotly.express as px\n", + "from huggingface_hub import hf_hub_download #used to download metadata.csv from OCF's Hugging Face repo" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Plotting the hourly distribution of data points\n", + "fig = px.bar(hourly_counts, x='hour', y='count',\n", + " labels={'hour': 'Hour of the Day', 'count': 'Number of Data Points in the Test Set'},\n", + " title='Hourly Distribution of Data Points')\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From the plot above, we can see that the highest number of data points is from 19:00 hrs, and the least number of data points is from 00:00 hrs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analysing the metadata" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The metadata.csv can be downloaded from https://huggingface.co/datasets/openclimatefix/uk_pv \n", + "
It contains a column ss_id, which is the same as pv_id in the testset.csv file\n", + "
It also contains geographical coordinates, orientation, tilt & kwp of the data points" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'../metadata.csv'" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hf_hub_download(repo_id=\"openclimatefix/uk_pv\", filename=\"metadata.csv\", repo_type=\"dataset\", local_dir='../')" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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