{"id":46747,"date":"2023-02-02T00:00:00","date_gmt":"2023-02-02T08:00:00","guid":{"rendered":"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/blog\/wind-turbine-analysis-using-griddb-and-python\/"},"modified":"2025-11-13T12:56:28","modified_gmt":"2025-11-13T20:56:28","slug":"wind-turbine-analysis-using-griddb-and-python","status":"publish","type":"post","link":"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/en\/blog\/wind-turbine-analysis-using-griddb-and-python\/","title":{"rendered":"Wind Turbine Analysis using GridDB and Python"},"content":{"rendered":"
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Wind Turbine Analysis using GridDB and Python\u00b6<\/a><\/h1>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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Overview\u00b6<\/a><\/h1>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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The United States is dotted with several state of the art Wind Turbines designed and installed by leading turbine manufacturers and engineering teams. As per eia.gov <\/a>, wind turbines were the source of about 9.2% of the total power generation in the United States. With an interesting dataset in hand combined with a robust database product, we are going to analyze the wind turbines in the United States.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

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About the Dataset\u00b6<\/a><\/h2>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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The dataset in hand is a collection of land-\nThe United States Wind Turbine Database (USWTDB) provides the locations of land-based and offshore wind turbines in the United States, corresponding wind project information, and turbine technical specifications. The creation of this database was jointly funded by the U.S. Department of Energy (DOE) Wind Energy Technologies Office (WETO) via the Lawrence Berkeley National Laboratory (LBNL) Electricity Markets and Policy Group, the U.S. Geological Survey (USGS) Energy Resources Program, and the American Clean Power Association (ACP).<\/p>\n

72,130 turbines covering 44 states (plus Guam and PR)<\/p>\n

The data can be downloaded from https:\/\/eerscmap.usgs.gov\/uswtdb\/data\/<\/a><\/p>\n

For more information on how the data was collected, refer https:\/\/eta-publications.lbl.gov\/sites\/default\/files\/uswtdb_v5_1_20220729_memo.pdf<\/a><\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

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Dataset Citation:\u00b6<\/a><\/h4>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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Hoen, B.D., Diffendorfer, J.E., Rand, J.T., Kramer, L.A., Garrity, C.P., and Hunt, H.E., 2018, United States Wind Turbine Database v5.1 (July 29, 2022): U.S. Geological Survey, American Clean Power Association, and Lawrence Berkeley National Laboratory data release, https:\/\/doi.org\/10.5066\/F7TX3DN0<\/a>.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

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A short note on the Dataset variables\u00b6<\/a><\/h2>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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\u25d8 case_id - unique uswtdb id\t
\n\u25d8 faa_ors\t- Digital obstacle file id
\n\u25d8 faa_asn\t- Aeronautical Study number
\n\u25d8 usgs_pr_id\t- USGS ID
\n\u25d8 t_state - State where the turbine is located
\n\u25d8 eia_id - Plant ID
\n\u25d8 t_county - County where the turbine is located
\n\u25d8 t_fips - State and county where turbine is located
\n\u25d8 p_name - Name of the turbine\t
\n\u25d8 p_year - Year the project became operational
\n\u25d8 p_tnum - Number of turbines in the project
\n\u25d8 p_cap - Project Capacity (MegaWatt)
\n\u25d8 t_manu - Turbine OEM
\n\u25d8 t_model - Turbine Model
\n\u25d8 t_cap - Turbine Capacity (Kilowatt)
\n\u25d8 t_hh - Turbine Hub Height (Meters)
\n\u25d8 t_rd - Turbine Rotor Diameter (Meters)
\n\u25d8 t_rsa - Turbine Rotor Swept Area (Square Meters)
\n\u25d8 t_ttlh - Turbine Total Height (Meters)
\n\u25d8 retrofit - Whether the turbine has been retrofitted since initial construction or not. (0- No retrofit,1-Yes known retrofit)\t\n\u25d8 retrofit_year - If the turbin was retrofitted, the year of retrofit.
\n\u25d8 t_conf_atr - Level of confidence in the attributes provided. This is an ordinal varible.
\n\u25d8 t_conf_loc - Location Confidence\n\u25d8 t_img_date -\n\u25d8 t_img_srce\t- source of image used to visually verify turbine location
\n\u25d8 xlong\t- Longitude (decimal degrees)\n\u25d8 ylat - Latitude (decimal degrees)<\/p>\n

The code book can be downloaded here <\/a>.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

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Overall Premise\u00b6<\/a><\/h2>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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The overall premise behind this blog post is to use the following interesting features of GridDB -<\/p>\n

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  1. GridDB's TQL capability to retrieve aggregated data.<\/li>\n
  2. GridDB's SQL capability to retrieve data using sub-queries, case statements, group by clauses, having clauses as well retrieving multiple data points using nested sub-queries.<\/li>\n<\/ol>\n

    GridDB offers two types of containers namely 'Collections' and 'Time Series' containers. To learn more about different types of containers offered by GridDB, refer to this interesting article on data modeling with GridDB<\/a>.<\/p>\n

    GridDB also offers multiple mechanisms to retrieve data from containers. It supports TQL as well as SQL to retrieve data. We will demonstrate different scenarios and how to retrieve data for the same from the GridDB container.<\/p>\n

    Don't forget to get a GridDB cloud subscription <\/a> in order to get started.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

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    Loading the basic libraries required\u00b6<\/a><\/h3>\n<\/div>\n<\/div>\n<\/div>\n<\/div>
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    In\u00a0[1]:<\/div>\n
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    <\/span>import<\/span> <\/span>os<\/span>\nimport<\/span> <\/span>numpy<\/span> <\/span>as<\/span> <\/span>np<\/span>\nimport<\/span> <\/span>pandas<\/span> <\/span>as<\/span> <\/span>pd<\/span>\nimport<\/span> <\/span>nums_from_string<\/span> <\/span>as<\/span> <\/span>ns<\/span>\nfrom<\/span> <\/span>IPython.core.display<\/span> <\/span>import<\/span> Image<\/span>,<\/span> display<\/span>\nimport<\/span> <\/span>seaborn<\/span> <\/span>as<\/span> <\/span>sns<\/span>\nfrom<\/span> <\/span>matplotlib<\/span> <\/span>import<\/span> pyplot<\/span> as<\/span> plt<\/span>\nfrom<\/span> <\/span>mapboxgl.utils<\/span> <\/span>import<\/span> *<\/span>\nfrom<\/span> <\/span>mapboxgl.viz<\/span> <\/span>import<\/span> *<\/span>\nfrom<\/span> <\/span>matplotlib.dates<\/span> <\/span>import<\/span> DateFormatter<\/span>\nimport<\/span> <\/span>plotly.graph_objects<\/span> <\/span>as<\/span> <\/span>go<\/span> # For the map<\/span>\nsns<\/span>.<\/span>set<\/span>()<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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    C:\\Users\\mg_su\\anaconda3\\lib\\site-packages\\scipy\\__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.1\n  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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    Points to note\u00b6<\/a><\/h4>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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    1. It is important to remember that the order of the columns in the container should be the same as the order of the columns in the dataframe. This is similar to an Insert operation in any relational database.<\/li>\n<\/ol>\n<\/div>\n<\/div>\n<\/div>\n<\/div>
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      In\u00a0[2]:<\/div>\n
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      <\/span>Turbine_Data<\/span> =<\/span> pd<\/span>.<\/span>read_excel<\/span>(<\/span>\"Turbine_Data.xlsx\"<\/span>,<\/span> header<\/span>=<\/span>0<\/span>,<\/span>keep_default_na<\/span>=<\/span>True<\/span>,<\/span> index_col<\/span>=<\/span>None<\/span>)<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>
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      In\u00a0[3]:<\/div>\n
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      <\/span># Dropping rows that have the operational year (p_year) that are empty<\/span>\nprint<\/span>(<\/span>len<\/span>(<\/span>Turbine_Data<\/span>))<\/span>\nTurbine_Data<\/span>.<\/span>dropna<\/span>(<\/span>subset<\/span>=<\/span>[<\/span>'p_year'<\/span>],<\/span>inplace<\/span>=<\/span>True<\/span>)<\/span>\nprint<\/span>(<\/span>len<\/span>(<\/span>Turbine_Data<\/span>))<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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      72130\n71398\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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      Missing Value Imputations and Data type conversions\u00b6<\/a><\/h3>\n<\/div>\n<\/div>\n<\/div>\n<\/div>
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      In\u00a0[4]:<\/div>\n
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      <\/span>#Check for missing values and fill it<\/span>\nTurbine_Data<\/span>.<\/span>fillna<\/span>(<\/span>Turbine_Data<\/span>.<\/span>median<\/span>(),<\/span>inplace<\/span>=<\/span>True<\/span>)<\/span>\nTurbine_Data<\/span>.<\/span>head<\/span>()<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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      C:\\Users\\mg_su\\AppData\\Local\\Temp\/ipykernel_13828\/1904398437.py:2: FutureWarning: DataFrame.mean and DataFrame.median with numeric_only=None will include datetime64 and datetime64tz columns in a future version.\n  Turbine_Data.fillna(Turbine_Data.median(),inplace=True)\nC:\\Users\\mg_su\\AppData\\Local\\Temp\/ipykernel_13828\/1904398437.py:2: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n  Turbine_Data.fillna(Turbine_Data.median(),inplace=True)\n<\/pre>\n<\/div>\n<\/div>\n
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