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