{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Alaska Energy Data Site\n", "\n", "## URL: ak-energy-data.analysisnorth.com" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This site provides a number of different data sets related to Alaskan energy use. They are primarily useful for creating energy models that require weather data and fuel/utility costs. Much of the data comes from the [AkWarm Energy Modeling software](http://analysisnorth.com/AkWarm/AkWarm2download.html). There are\n", "two types of files on the site: \n", "\n", "1. the raw, source data files that contain the original data. These files are found in the `raw`\n", "subdirectory; and \n", "\n", "2. files that contain data processed from the original files. These files are found in the `proc`\n", "subdirectory. The processing often selects out a\n", "subset of fields and sometimes combines datasets together. The processed data is typically in two formats:\n", "a pickled and compressed (bz2) Python Pandas DataFrame, and a standard CSV file. Info on using the Pandas\n", "format is below.\n", "\n", "You can access any of the data files by simply downloading them in your browser \n", "or another tool that can make HTTP requests. The file names and directory paths are indicated in the various\n", "sections below. You can also access the files via programming code, and examples are given below using the\n", "Python language. You can download this [Jupyter Notebook](http://jupyter.org/) and execute the sample code \n", "provided below to experiment with accessing the data.\n", "[Here is the link](http://ak-energy-data.analysisnorth.com/index.ipynb) to download this notebook.\n", "You will need Python 3.6+ installed to execute the code.\n", "\n", "To see the code that was used to process the raw data into processed files, see\n", "[this Notebook on the associated GitHub Site](https://github.com/alanmitchell/ak-energy-admin/blob/master/ak-energy-data-admin.ipynb)." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# Execute this cell prior to any of the cells below\n", "import urllib\n", "import io\n", "import pandas as pd\n", "import requests" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Functions that Retrieve Data as a Pandas DataFrame or a CSV File\n", "\n", "These Python functions can be used to retrieve the processed data on the site, either as\n", "a Pandas DataFrame or as CSV text." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "base_url = 'http://ak-energy-data.analysisnorth.com/'\n", "\n", "def get_df(file_path):\n", " \"\"\"Returns a Pandas DataFrame that is found at the 'file_path'\n", " below the Base URL for accessing data. The 'file_path' should end\n", " with '.pkl' and points to a pickled, compressed (bz2), Pandas DataFrame.\n", " \"\"\"\n", " b = requests.get(urllib.parse.urljoin(base_url, file_path)).content\n", " df = pd.read_pickle(io.BytesIO(b), compression='bz2')\n", " return df\n", "\n", "def get_csv(file_path):\n", " \"\"\"Returns a string in CSV format that is found at the 'file_path'\n", " below the Base URL for accessing data. The 'file_path' should end\n", " with '.csv' and points to a CSV data file.\n", " \"\"\"\n", " txt = requests.get(urllib.parse.urljoin(base_url, file_path)).text\n", " return txt\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Accessing City and Utility Data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here are the data files available having City and Utility Data. The files in the `city-util/proc`\n", "directory contain processed data derived from data in the files found in the `city-util/raw`\n", "directory.\n", "\n", "```\n", "├── city-util\n", "│   ├── proc\n", "│   │   ├── city.csv\n", "│   │   ├── city.pkl\n", "│   │   ├── misc_info.csv\n", "│   │   ├── misc_info.pkl\n", "│   │   ├── utility.csv\n", "│   │   └── utility.pkl\n", "│   └── raw\n", "│   ├── City Utility Links.xlsx\n", "│   ├── City.xlsx\n", "│   ├── Misc Info.xlsx\n", "│   └── Utility.xlsx\n", "\n", "```\n", "\n", "For example, browser access to the `utility.csv` file would be:\n", "`http://ak-energy-data.analysisnorth.com/city-util/proc/utility.csv`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Access City Data" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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NameLatitudeLongitudeERHRegionIDWAPRegionIDImpCostFuelReferFuelCityIDOil1PriceOil2Price...TMYnameElecUtilitiesGasPricearis_citycensus_citycensus_areaancsa_regionrailbelthubavg_elec_usage
ID
1Anchorage61.151958-149.864105221FalseNaN3.08NaN...ANCHORAGE INTL AP, AK[(Anchorage ML&P - Residential, 2), (Anchorage...0.93955AnchorageAnchorage municipalityAnchorage municipalityCook Inlet Regional (CIRI)RailbeltTrue[674.3176110825001, 616.54369648225, 568.80201...
2Adak51.848896-176.635422235FalseNaN4.424.69...ADAK NAS, AK[(Adak -TDX Residential, 677), (Adak - TDX Com...NaNAdakAdak cityAleutians West Census AreaAleutAffordable Energy Strategy AreaFalse[471.936998147, 423.45531781, 460.81549341, 42...
3Akutan54.134945-165.772446235FalseNaN2.912.91...DUTCH HARBOR, AK[(Akutan, City of - Residential, 9), (Akutan, ...NaNAkutanAkutan cityAleutians East BoroughAleutAffordable Energy Strategy AreaFalse[500.99156233, 439.98431584599996, 456.5106555...
4Allakaket66.561386-152.649445445FalseNaN5.50NaN...BETTLES FIELD, AK[(Allakaket - APT - Residential, 249), (Allaka...NaNAllakaketAllakaket cityYukon-Koyukuk Census AreaDoyonAffordable Energy Strategy AreaFalse[338.509000744, 291.889556239, 276.804469551, ...
5Angoon57.496647-134.578903113FalseNaN4.104.08...SITKA JAPONSKI AP, AK[(Inside Passage Electric Coop - Residential, ...NaNAngoonAngoon cityHoonah-Angoon Census AreaSealaska CorporationAffordable Energy Strategy AreaFalse[277.08055898099997, 257.757954545, 237.155612...
\n", "

5 rows × 29 columns

\n", "
" ], "text/plain": [ " Name Latitude Longitude ERHRegionID WAPRegionID ImpCost \\\n", "ID \n", "1 Anchorage 61.151958 -149.864105 2 2 1 \n", "2 Adak 51.848896 -176.635422 2 3 5 \n", "3 Akutan 54.134945 -165.772446 2 3 5 \n", "4 Allakaket 66.561386 -152.649445 4 4 5 \n", "5 Angoon 57.496647 -134.578903 1 1 3 \n", "\n", " FuelRefer FuelCityID Oil1Price Oil2Price \\\n", "ID \n", "1 False NaN 3.08 NaN \n", "2 False NaN 4.42 4.69 \n", "3 False NaN 2.91 2.91 \n", "4 False NaN 5.50 NaN \n", "5 False NaN 4.10 4.08 \n", "\n", " ... TMYname \\\n", "ID ... \n", "1 ... ANCHORAGE INTL AP, AK \n", "2 ... ADAK NAS, AK \n", "3 ... DUTCH HARBOR, AK \n", "4 ... BETTLES FIELD, AK \n", "5 ... SITKA JAPONSKI AP, AK \n", "\n", " ElecUtilities GasPrice aris_city \\\n", "ID \n", "1 [(Anchorage ML&P - Residential, 2), (Anchorage... 0.93955 Anchorage \n", "2 [(Adak -TDX Residential, 677), (Adak - TDX Com... NaN Adak \n", "3 [(Akutan, City of - Residential, 9), (Akutan, ... NaN Akutan \n", "4 [(Allakaket - APT - Residential, 249), (Allaka... NaN Allakaket \n", "5 [(Inside Passage Electric Coop - Residential, ... NaN Angoon \n", "\n", " census_city census_area \\\n", "ID \n", "1 Anchorage municipality Anchorage municipality \n", "2 Adak city Aleutians West Census Area \n", "3 Akutan city Aleutians East Borough \n", "4 Allakaket city Yukon-Koyukuk Census Area \n", "5 Angoon city Hoonah-Angoon Census Area \n", "\n", " ancsa_region railbelt hub \\\n", "ID \n", "1 Cook Inlet Regional (CIRI) Railbelt True \n", "2 Aleut Affordable Energy Strategy Area False \n", "3 Aleut Affordable Energy Strategy Area False \n", "4 Doyon Affordable Energy Strategy Area False \n", "5 Sealaska Corporation Affordable Energy Strategy Area False \n", "\n", " avg_elec_usage \n", "ID \n", "1 [674.3176110825001, 616.54369648225, 568.80201... \n", "2 [471.936998147, 423.45531781, 460.81549341, 42... \n", "3 [500.99156233, 439.98431584599996, 456.5106555... \n", "4 [338.509000744, 291.889556239, 276.804469551, ... \n", "5 [277.08055898099997, 257.757954545, 237.155612... \n", "\n", "[5 rows x 29 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Access as a Pandas DataFrame\n", "df = get_df('city-util/proc/city.pkl')\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Name Anchorage\n", "Latitude 61.152\n", "Longitude -149.864\n", "ERHRegionID 2\n", "WAPRegionID 2\n", "ImpCost 1\n", "FuelRefer False\n", "FuelCityID NaN\n", "Oil1Price 3.08\n", "Oil2Price NaN\n", "PropanePrice 4.5\n", "BirchPrice 325\n", "SprucePrice 345\n", "CoalPrice 175\n", "SteamPrice NaN\n", "HotWaterPrice NaN\n", "MunicipalSalesTax NaN\n", "BoroughSalesTax NaN\n", "TMYid 702730\n", "TMYname ANCHORAGE INTL AP, AK\n", "ElecUtilities [(Anchorage ML&P - Residential, 2), (Anchorage...\n", "GasPrice 0.93955\n", "aris_city Anchorage\n", "census_city Anchorage municipality\n", "census_area Anchorage municipality\n", "ancsa_region Cook Inlet Regional (CIRI)\n", "railbelt Railbelt\n", "hub True\n", "avg_elec_usage [674.3176110825001, 616.54369648225, 568.80201...\n", "Name: 1, dtype: object" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# The first row in the table\n", "df.iloc[0]" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ID,Name,Latitude,Longitude,ERHRegionID,WAPRegionID,ImpCost,FuelRefer,FuelCityID,Oil1Price,Oil2Price,PropanePrice,BirchPrice,SprucePrice,CoalPrice,SteamPrice,HotWaterPrice,MunicipalSalesTax,BoroughSalesTax,TMYid,TMYname,ElecUtilities,GasPrice,aris_city,census_city,census_area,ancsa_region,railbelt,hub,avg_elec_usage\n", "1,Anchorage,61.1519584655762,-149.864105224609,2,2,1,False,,3.07999992370605,,4.5,325.0,345.0,175.0,,,,,702730,\"ANCHORAGE INTL AP, AK\",\"[('Anchorage ML&P - Residential', 2), ('Anchorage ML&P - Commercial - Sm', 293), ('Anchorage ML&P - Commercial - Lg', 480), ('Chugach Electric- Residential', 1), ('Chugach Electric - Commercial - Sm', 361), ('Chugach Electric - Commercial - Lg', 548)]\",0.939549997448921,Anchorage,Anchorage municipality,Anchorage municipality,Cook Inlet Regional (CIRI),Railbelt,True,\"[674.3176110825001, 616.54369648225, 568.8020181537499, 559.5290597065, 502.68865773625, 474.855271649, 467.94495386925, 483.58090842574995, 498.77690241749997, 532.8355384270001, 575.696935941, 610.274161992]\"\n", "2,Adak,51.8488960266113,-176.63542175293,2,3,5,False,,4.42000007629395,4.69000005722046,,,,,,,0.0399999991059303,,704540,\"ADAK NAS, AK\",\"[('Adak -TDX Residential', 677), ('Adak - TDX Commercial', 678)]\",,Adak,Adak city,Aleutians West Census Area,Aleut,Affordable Energy Strategy Area,False,\"[471.936998147, 423.45531781, 460.81549341, 424.29430280099996, 414.198261989, 408.02137305699995, 428.550250417, 419.71331168800003, 409.07778510199995, 425.893819855, 443.339960239, 463.328994468]\"\n", "3,Akutan,54.1349449157715,-165.772445678711,2,3,5,False,,2.91000008583069,2.91000008583069,,,,,,,,,704890,\"DUTCH HARBOR, AK\",\"[('Akutan, City of - Residential', 9), ('Akutan, City of - Commercial - Sm', 288), ('Akutan, City of - Commercial - Lg', 475)]\",,Akutan,Akutan city,Aleutians East Borough,Aleut,Affordable Energy Strategy Area,False,\"[500.99156233, 439.98431584599996, 456.510655517, 421.946346724, 426.966352624, 380.76338928900003, 392.608543794, 400.059089699, 410.11991491599997, 434.168674699, 445.715396996, 472.65755627]\"\n", "4,Allakaket,66.5613861083984,-152.649444580078,4,4,5,False,,5.5,,11.4399995803833,,,,,,,,701740,\"BETTLES FIELD, AK\",\"[('Allakaket - APT - Residential', 249), ('Allakaket - APT - Commercial - Sm', 291), ('Allakaket - APT- Commercial - Lg', 478)]\",,Allakaket,Allakaket city,Yukon-Koyukuk Census Area,Doyon,Affordable Energy Strategy Area,False,\"[338.509000744, 291.889556239, 276.804469551, 259.698748275, 241.22759162, 241.87788097700002, 237.01448643400002, 240.39129591, 250.339561607, 260.703968331, 289.122744033, 324.815682083]\"\n" ] } ], "source": [ "# Example that uses Python to access the data as CSV text.\n", "# As mentioned above, the CSV files can be downloaded directly \n", "# with a browser by entering the appropriate URL, such as:\n", "#\n", "# http://ak-energy-data.analysisnorth.com/city-util/proc/city.csv\n", "\n", "txt = get_csv('city-util/proc/city.csv')\n", "lines = txt.splitlines()\n", "for i in range(5):\n", " print(lines[i])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Access Utility Data" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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IDNameActiveTypeIsCommercialChargesRCCPCECO2CustomerChgDemandChargeNameShortBlocks
ID
11Chugach Electric- ResidentialTrue1FalseTrue0.00001.108.00NaNChugac[(nan, 0.18715799739584343), (nan, nan), (nan,...
22Anchorage ML&P - ResidentialTrue1FalseTrueNaN1.1013.62NaNAnchor[(nan, 0.17457800125703232), (nan, nan), (nan,...
33Golden Valley Electric - ResidentialTrue1FalseTrueNaN2.2817.50NaNGolden[(nan, 0.22799800289794792), (nan, nan), (nan,...
55Enstar Natural Gas - G1 (Res)True2FalseTrue0.0000NaN16.00NaNEnstar[(nan, 0.9405279974453149), (nan, nan), (nan, ...
66Akhiok, City of ResidentialTrue1FalseFalse0.32652.20NaNNaNAkhiok[(nan, 0.800000011920929), (nan, nan), (nan, n...
\n", "
" ], "text/plain": [ " ID Name Active Type IsCommercial \\\n", "ID \n", "1 1 Chugach Electric- Residential True 1 False \n", "2 2 Anchorage ML&P - Residential True 1 False \n", "3 3 Golden Valley Electric - Residential True 1 False \n", "5 5 Enstar Natural Gas - G1 (Res) True 2 False \n", "6 6 Akhiok, City of Residential True 1 False \n", "\n", " ChargesRCC PCE CO2 CustomerChg DemandCharge NameShort \\\n", "ID \n", "1 True 0.0000 1.10 8.00 NaN Chugac \n", "2 True NaN 1.10 13.62 NaN Anchor \n", "3 True NaN 2.28 17.50 NaN Golden \n", "5 True 0.0000 NaN 16.00 NaN Enstar \n", "6 False 0.3265 2.20 NaN NaN Akhiok \n", "\n", " Blocks \n", "ID \n", "1 [(nan, 0.18715799739584343), (nan, nan), (nan,... \n", "2 [(nan, 0.17457800125703232), (nan, nan), (nan,... \n", "3 [(nan, 0.22799800289794792), (nan, nan), (nan,... \n", "5 [(nan, 0.9405279974453149), (nan, nan), (nan, ... \n", "6 [(nan, 0.800000011920929), (nan, nan), (nan, n... " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Access as a Pandas DataFrame\n", "df = get_df('city-util/proc/utility.pkl')\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "ID 1\n", "Name Chugach Electric- Residential\n", "Active True\n", "Type 1\n", "IsCommercial False\n", "ChargesRCC True\n", "PCE 0\n", "CO2 1.1\n", "CustomerChg 8\n", "DemandCharge NaN\n", "NameShort Chugac\n", "Blocks [(nan, 0.18715799739584343), (nan, nan), (nan,...\n", "Name: 1, dtype: object" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# The first row in the table\n", "df.iloc[0]" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[(nan, 0.18715799739584343), (nan, nan), (nan, nan), (nan, nan), (nan, nan)]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# The \"blocks\" field is a Python list of tuples (rate, upper usage cut-off\n", "# for the block.)\n", "df.iloc[0].Blocks" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Miscellaneous AkWarm City / Utility Info" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "ID 1\n", "LibVersion 2018-09-27 00:00:00\n", "DiscountRate 0.03\n", "RegSurcharge 0.004\n", "RegSurchargeElectric 0.000978\n", "PCEkWhLimit 500\n", "PCEFundingPct 1\n", "MiscNotes Inflation factors and discount rate from 2011 ...\n", "Name: 0, dtype: object" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Access as a Pandas Series\n", "series = get_df('city-util/proc/misc_info.pkl')\n", "# This is really just one record so it is a Pandas Series object\n", "series" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Access TMY3 Climate Data\n", "\n", "All of the Alaska TMY3 (Typical Meterological Year) files are available, although\n", "only one site is included for Anchorage (International Airport). The files are named\n", "through use of the TMY3 Site ID. Original source files ar ein the `wx/tmy3/raw`\n", "directory. An Excel file containing ASHRAE 2017 Design Heating Temperatures\n", "for each TMY3 site is available (`design_temps.xlsx`). Further info on the \n", "processed files is below.\n", "\n", "```\n", "└── wx\n", " └── tmy3\n", " ├── proc\n", " │   ├── 700197.csv\n", " │   ├── 700197.pkl\n", " │   ├── 700260.csv\n", " │   ├── 700260.pkl\n", " │   ├── 700637.csv\n", " │   ├── 700637.pkl\n", " ... the rest of the Alaska TMY3 files\n", " │   ├── tmy3_meta.csv\n", " │   └── tmy3_meta.pkl\n", " └── raw\n", " ├── 700197.csv\n", " ├── 700260.csv\n", " ├── 700637.csv\n", " ├── 701043.csv\n", " ... the rest of the Alaska TMY3 files\n", " ├── design_temps.xlsx\n", "```" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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db_temprhwind_spdmonth
timestamp
2018-01-01 00:30:0017.608416.1059681
2018-01-01 01:30:0019.588316.1059681
2018-01-01 02:30:0019.228716.3296621
2018-01-01 03:30:0016.348416.3296621
2018-01-01 04:30:0017.608116.5533561
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" ], "text/plain": [ " db_temp rh wind_spd month\n", "timestamp \n", "2018-01-01 00:30:00 17.60 84 16.105968 1\n", "2018-01-01 01:30:00 19.58 83 16.105968 1\n", "2018-01-01 02:30:00 19.22 87 16.329662 1\n", "2018-01-01 03:30:00 16.34 84 16.329662 1\n", "2018-01-01 04:30:00 17.60 81 16.553356 1" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = get_df('wx/tmy3/proc/700197.pkl')\n", "df.head()\n", "# Units are IP (English), temperature in degrees F, rh in %, wind speed in miles per hour.\n", "# Timestamps are placed in the middle of the hour with the year arbitrarily\n", "# set to 2018." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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citydb_temp_avgelevationheating_design_templatitudelongituderh_avgstateurlutc_offsetwind_spd_avg
tmy_id
703870WRANGELL43.07291842.6509215.856.483-132.36775.674315AKhttp://ak-energy-data.analysisnorth.com/wx/tmy...-9.05.976894
700260BARROW W POST-W ROGERS ARPT [NSA - ARM]10.72971232.80840-34.371.320-156.62084.193950AKhttp://ak-energy-data.analysisnorth.com/wx/tmy...-9.012.178734
702750VALDEZ WSO39.33643822.965887.161.133-146.35074.864498AKhttp://ak-energy-data.analysisnorth.com/wx/tmy...-9.06.165962
702185MEKORYUK33.63232949.21260-10.660.367-166.26780.209817AKhttp://ak-energy-data.analysisnorth.com/wx/tmy...-9.014.999576
702510TALKEETNA STATE ARPT35.236486344.48820-17.662.317-150.10074.301826AKhttp://ak-energy-data.analysisnorth.com/wx/tmy...-9.05.276497
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" ], "text/plain": [ " city db_temp_avg elevation \\\n", "tmy_id \n", "703870 WRANGELL 43.072918 42.65092 \n", "700260 BARROW W POST-W ROGERS ARPT [NSA - ARM] 10.729712 32.80840 \n", "702750 VALDEZ WSO 39.336438 22.96588 \n", "702185 MEKORYUK 33.632329 49.21260 \n", "702510 TALKEETNA STATE ARPT 35.236486 344.48820 \n", "\n", " heating_design_temp latitude longitude rh_avg state \\\n", "tmy_id \n", "703870 15.8 56.483 -132.367 75.674315 AK \n", "700260 -34.3 71.320 -156.620 84.193950 AK \n", "702750 7.1 61.133 -146.350 74.864498 AK \n", "702185 -10.6 60.367 -166.267 80.209817 AK \n", "702510 -17.6 62.317 -150.100 74.301826 AK \n", "\n", " url utc_offset \\\n", "tmy_id \n", "703870 http://ak-energy-data.analysisnorth.com/wx/tmy... -9.0 \n", "700260 http://ak-energy-data.analysisnorth.com/wx/tmy... -9.0 \n", "702750 http://ak-energy-data.analysisnorth.com/wx/tmy... -9.0 \n", "702185 http://ak-energy-data.analysisnorth.com/wx/tmy... -9.0 \n", "702510 http://ak-energy-data.analysisnorth.com/wx/tmy... -9.0 \n", "\n", " wind_spd_avg \n", "tmy_id \n", "703870 5.976894 \n", "700260 12.178734 \n", "702750 6.165962 \n", "702185 14.999576 \n", "702510 5.276497 " ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# There is a file with summary info about each site \n", "# available.\n", "df = get_df('wx/tmy3/proc/tmy3_meta.pkl')\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## NEEP Cold-Climate Air Source Heat Pump Specifications\n", "\n", "See (http://www.neep.org/initiatives/high-efficiency-products/emerging-technologies/ashp/cold-climate-air-source-heat-pump)\n", "for more info about the data. The original NEEP spreadsheet is found at `heat-pump/raw/neep_ashp_data.xlsx`\n", "\n", "```\n", "├── heat-pump\n", "│   ├── proc\n", "│   │   ├── hp_specs.csv\n", "│   │   └── hp_specs.pkl\n", "│   └── raw\n", "│   └── neep_ashp_data.xlsx\n", "```" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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brandahri_numzonesoutdoor_modelindoor_modelhspfin_pwr_5F_maxcapacity_5F_maxin_pwr_47F_mincop_5F_maxcop_17F_maxcop_47F_max
0Acd202110483SingleUHD09KCH38S-OUHD09KCH38S-I15.01.8212977.00.202.0896612.4056522.657178
1Acd201863350SingleOCD09KCH23S-OOCD09KCH23S-I10.51.339171.00.162.0208682.1830962.605076
2Acd202110500MultiMPD30KCH21S-ONon-Ducted Indoor Units11.53.0119356.01.051.8846133.1260913.584757
3Acd202110494MultiMPD18KCH22S-ONon-Ducted Indoor Units11.02.3013450.00.751.7138292.4152592.365502
4Acd202132789SingleUHD24KCH22S-OUHD24KCH22S-I12.03.2022553.00.382.0655102.2771972.534669
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" ], "text/plain": [ " brand ahri_num zones outdoor_model indoor_model hspf \\\n", "0 Acd 202110483 Single UHD09KCH38S-O UHD09KCH38S-I 15.0 \n", "1 Acd 201863350 Single OCD09KCH23S-O OCD09KCH23S-I 10.5 \n", "2 Acd 202110500 Multi MPD30KCH21S-O Non-Ducted Indoor Units 11.5 \n", "3 Acd 202110494 Multi MPD18KCH22S-O Non-Ducted Indoor Units 11.0 \n", "4 Acd 202132789 Single UHD24KCH22S-O UHD24KCH22S-I 12.0 \n", "\n", " in_pwr_5F_max capacity_5F_max in_pwr_47F_min cop_5F_max cop_17F_max \\\n", "0 1.82 12977.0 0.20 2.089661 2.405652 \n", "1 1.33 9171.0 0.16 2.020868 2.183096 \n", "2 3.01 19356.0 1.05 1.884613 3.126091 \n", "3 2.30 13450.0 0.75 1.713829 2.415259 \n", "4 3.20 22553.0 0.38 2.065510 2.277197 \n", "\n", " cop_47F_max \n", "0 2.657178 \n", "1 2.605076 \n", "2 3.584757 \n", "3 2.365502 \n", "4 2.534669 " ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# The processed data only includes the Ductless, mini-split models.\n", "df = get_df('heat-pump/proc/hp_specs.pkl')\n", "df.head()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 2 }