get new intensity file
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Hanzhang Ma
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72d4ce811e
commit
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372
read_data/convert_data.ipynb
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372
read_data/convert_data.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 85,
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"metadata": {},
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"outputs": [],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"import os\n",
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"import csv"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 86,
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"metadata": {},
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"outputs": [],
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"source": [
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"def read_csv(filename):\n",
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" skip_rows = list(range(1, 17))\n",
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" data = pd.read_csv(filename, sep=';', skiprows=skip_rows)\n",
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" return data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 87,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/tmp/ipykernel_3075037/3659192646.py:3: DtypeWarning: Columns (32,33,35) have mixed types. Specify dtype option on import or set low_memory=False.\n",
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" data = pd.read_csv(filename, sep=';', skiprows=skip_rows)\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"Index(['Time', 'Irradiance onto horizontal plane ',\n",
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" 'Diffuse Irradiation onto Horizontal Plane ', 'Outside Temperature ',\n",
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" 'Module Area 1: Height of Sun ',\n",
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" 'Module Area 1: Irradiance onto tilted surface ',\n",
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" 'Module Area 1: Module Temperature ', 'Grid Export ',\n",
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" 'Energy from Grid ', 'Global radiation - horizontal ',\n",
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" 'Deviation from standard spectrum ', 'Ground Reflection (Albedo) ',\n",
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" 'Orientation and inclination of the module surface ', 'Shading ',\n",
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" 'Reflection on the Module Surface ',\n",
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" 'Irradiance on the rear side of the module ',\n",
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" 'Global Radiation at the Module ',\n",
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" 'Module Area 1: Reflection on the Module Surface ',\n",
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" 'Module Area 1: Global Radiation at the Module ',\n",
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" 'Global PV Radiation ', 'Bifaciality ', 'Soiling ',\n",
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" 'STC Conversion (Rated Efficiency of Module) ', 'Rated PV Energy ',\n",
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" 'Low-light performance ', 'Module-specific Partial Shading ',\n",
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" 'Deviation from the nominal module temperature ', 'Diodes ',\n",
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" 'Mismatch (Manufacturer Information) ',\n",
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" 'Mismatch (Configuration/Shading) ',\n",
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" 'Power optimizer (DC conversion/clipping) ',\n",
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" 'PV Energy (DC) without inverter clipping ',\n",
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" 'Failing to reach the DC start output ',\n",
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" 'Clipping on account of the MPP Voltage Range ',\n",
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" 'Clipping on account of the max. DC Current ',\n",
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" 'Clipping on account of the max. DC Power ',\n",
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" 'Clipping on account of the max. AC Power/cos phi ', 'MPP Matching ',\n",
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" 'PV energy (DC) ',\n",
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" 'Inverter 1 - MPP 1 - to Module Area 1: PV energy (DC) ',\n",
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" 'Inverter 1 - MPP 2 - to Module Area 1: PV energy (DC) ',\n",
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" 'Inverter 1 - MPP 3 - to Module Area 1: PV energy (DC) ',\n",
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" 'Inverter 1 - MPP 4 - to Module Area 1: PV energy (DC) ',\n",
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" 'Inverter 1 - MPP 5 - to Module Area 1: PV energy (DC) ',\n",
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" 'Inverter 1 - MPP 6 - to Module Area 1: PV energy (DC) ',\n",
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" 'Inverter 2 - MPP 1 - to Module Area 1: PV energy (DC) ',\n",
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" 'Inverter 2 - MPP 2 - to Module Area 1: PV energy (DC) ',\n",
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" 'Energy at the Inverter Input ',\n",
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" 'Input voltage deviates from rated voltage ', 'DC/AC Conversion ',\n",
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" 'Own Consumption (Standby or Night) ', 'Total Cable Losses ',\n",
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" 'PV energy (AC) minus standby use ', 'Feed-in energy ',\n",
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" 'Inverter 1 to Module Area 1: Own Consumption (Standby or Night) ',\n",
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" 'Inverter 1 to Module Area 1: PV energy (AC) minus standby use ',\n",
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" 'Inverter 2 to Module Area 1: Own Consumption (Standby or Night) ',\n",
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" 'Inverter 2 to Module Area 1: PV energy (AC) minus standby use ',\n",
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" 'Unnamed: 58'],\n",
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" dtype='object')"
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]
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},
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"execution_count": 87,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"\n",
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"file_name = 'Riyahd_raw.csv'\n",
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"df = read_csv(file_name)\n",
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"df.columns"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 88,
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"metadata": {},
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"outputs": [],
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"source": [
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"remain_column = ['Time','PV energy (AC) minus standby use ']\n",
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"energy_row_name = remain_column[1]\n",
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"\n",
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"df = df[remain_column]\n",
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"df[energy_row_name] = df[energy_row_name].str.replace(',','.').astype(float)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 89,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"770594.226863267"
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]
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},
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"execution_count": 89,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"sum_energy = df[energy_row_name].sum()\n",
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"sum_energy"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 90,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"1975.882632982736"
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]
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},
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"execution_count": 90,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"sum_energy / 390"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 91,
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"metadata": {},
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"outputs": [],
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"source": [
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"group_size = 15\n",
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"df['group_id'] = df.index // group_size\n",
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"\n",
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"sums = df.groupby('group_id')[energy_row_name].sum()\n",
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"sums_df = sums.reset_index(drop=True).to_frame(name = 'Energy')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 92,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<bound method NDFrame.head of Energy\n",
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"0 0.0\n",
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"1 0.0\n",
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"2 0.0\n",
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"3 0.0\n",
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"4 0.0\n",
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"... ...\n",
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"35035 0.0\n",
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"35036 0.0\n",
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"35037 0.0\n",
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"35038 0.0\n",
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"35039 0.0\n",
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"\n",
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"[35040 rows x 1 columns]>"
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]
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},
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"execution_count": 92,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"sums_df.head"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 93,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" Time\n",
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"0 2023-01-01 00:00:00\n",
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"1 2023-01-01 00:15:00\n",
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"2 2023-01-01 00:30:00\n",
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"3 2023-01-01 00:45:00\n",
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"4 2023-01-01 01:00:00\n",
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" Time\n",
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"35035 2023-12-31 22:45:00\n",
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"35036 2023-12-31 23:00:00\n",
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"35037 2023-12-31 23:15:00\n",
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"35038 2023-12-31 23:30:00\n",
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"35039 2023-12-31 23:45:00\n"
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]
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}
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],
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"source": [
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"\n",
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"start_date = '2023-01-01'\n",
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"end_date = '2023-12-31'\n",
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"\n",
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"# 生成每天的15分钟间隔时间\n",
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"all_dates = pd.date_range(start=start_date, end=end_date, freq='D')\n",
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"all_times = pd.timedelta_range(start='0 min', end='1435 min', freq='15 min')\n",
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"\n",
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"# 生成完整的时间标签\n",
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"date_times = [pd.Timestamp(date) + time for date in all_dates for time in all_times]\n",
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"\n",
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"# 创建DataFrame\n",
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"time_frame = pd.DataFrame({\n",
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" 'Time': date_times\n",
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"})\n",
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"\n",
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"# 查看生成的DataFrame\n",
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"print(time_frame.head())\n",
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"print(time_frame.tail())\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 94,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"(35040, 1)\n",
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"(35040, 1)\n"
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]
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}
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],
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"source": [
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"print(sums_df.shape)\n",
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"print(time_frame.shape)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 95,
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"metadata": {},
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"outputs": [],
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"source": [
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"# sums_df['Time'] = time_frame['Time']\n",
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"sums_df = pd.concat([time_frame, sums_df], axis=1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 96,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" Energy\n",
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"Time \n",
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"2023-01-01 00:00:00 0.0\n",
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"2023-01-01 00:15:00 0.0\n",
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"2023-01-01 00:30:00 0.0\n",
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"2023-01-01 00:45:00 0.0\n",
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"2023-01-01 01:00:00 0.0\n"
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]
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}
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],
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"source": [
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"sums_df.set_index('Time', inplace=True)\n",
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"print(sums_df.head())"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 97,
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"metadata": {},
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"outputs": [],
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"source": [
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"max_value = sums_df['Energy'].max()\n",
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"sums_df['Energy'] = sums_df['Energy'] / max_value\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 98,
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"metadata": {},
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"outputs": [],
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"source": [
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"def save_csv(df, filename, columns):\n",
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" tmp_df = df.copy()\n",
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" tmp_df[columns[1]] = tmp_df[columns[1]].round(4)\n",
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" with open(filename, 'w', newline='') as file:\n",
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" writer = csv.writer(file)\n",
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" writer.writerow(columns)\n",
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" for index, row in tmp_df.iterrows():\n",
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" time_formatted = index.strftime('%H:%M')\n",
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" writer.writerow([time_formatted, row[columns[1]]])\n",
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" \n",
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" print(f'The file is written to {filename}')\n",
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" \n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 99,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"The file is written to Riyahd.csv\n"
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]
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}
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],
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"source": [
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"save_csv(sums_df, 'Riyahd.csv', ['Time', 'Energy'])"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "pv",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.9"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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