tesp_support.weather package

TESP is the Transactive Energy Simulation Platform tesp_support for Weather

Submodules

tesp_support.weather.PSM_download module

Simple script to download PSM weather files and convert them to DAT files.

In the name of expediency, this script contains two functions that were copy and pasted from dsot_solar.py. I wish that those functions had been better abstracted when I wrote them but, it’s not worth the time and effort to do so now. This script will likely ever only be run a few times as it is just creating input files and is not a part of the co-sim runtime.

The script does make use of PSMvstoDAT.py as a function, though. I had to make minor edits to that function as it was slightly non-abstracted and not completely stand-alone.

tesp_support.weather.PSM_download.download_nsrdb_data(dso_meta, output_path)

This function queries the NSRDB database over the web and pulls down and does a conversion.

Function pulls down the solar data down calls PSMv3toDAT to convert them to appropriate format.

Parameters:

  • dso_meta (list) – List of dicts with metadata associated with each DSO, specifically the site information

  • output_path (str) –

tesp_support.weather.PSM_download.parse_DSO_location(dso_metadata_path, worksheet_name)

This function parses the DSO metadata which is contained in an Excel spreadsheet

Parameters:

  • dso_metadata_path (str) – Path to the Excel file containing the metadata to be parsed.

  • worksheet_name (str) – Name of the worksheet in the Excel file containing the metadata

Returns:

One dictionary per DSO with appropriate metadata captured.

Return type:

list<dict>

tesp_support.weather.PSMv3toDAT module

This code reads in PSM v3 csv files obtained from https://maps.nrel.gov/nsrdb-viewer/.

The csv file includes 6 columns for the following, in addition to date and time information: temperature, humidity, DNI, DHI, pressure and wind_speed

tesp_support.weather.PSMv3toDAT.weatherdat(psmv3csvfile, bus_str, location_str)

Takes a weather csv file name obtained from NREL PSM v3 and does a conversion.

The function reads the file, converts the data to the desired units, and outputs dat file with the desired format

Parameters:

  • psmv3csvfile (str) – name of the file to be converted, without ext ‘.csv’

  • bus_str (str) – id of the bus

  • location_str (str) –

tesp_support.weather.TMY3toCSV module

Convert typical meteorological year version 3 (TMY3) data to comma separated values (CSV)

For common use by TESP agents.

Public Functions:
readtmy3:

Read a TMY3 file in to a pandas dataframe, using python tkinter.

weathercsv:

Converts TMY3 weather data to CSV in the requested time range.

weathercsv_cloudy_day:

Converts weather data to cloudy days CSV in the requested time range.

tesp_support.weather.TMY3toCSV.readtmy3(filename=None, coerce_year=None, recolumn=True)

Read a TMY3 file in to a pandas dataframe.

Parameters:

  • filename (object) –

  • coerce_year –

  • recolumn –

Return type:

dict, dict

tesp_support.weather.TMY3toCSV.weathercsv(tmyfile, output_file, start_time, end_time, year)

Converts TMY3 weather data to CSV in the requested time range

Parameters:

  • tmyfile (str) – the input TMY3 data file

  • output_file (str) – the output CSV data file

  • start_time (str) – the starting date and time of interest, like 2013-07-01 00:00:00

  • end_time (str) – the ending date and time of interest, like 2013-07-08 00:00:00

  • year (int) – the year of interest

tesp_support.weather.TMY3toCSV.weathercsv_cloudy_day(start_time, end_time, output_file)

Converts weather data to cloudy days CSV in the requested time range

Parameters:

  • start_time (str) – the starting date and time of interest, like 2013-07-01 00:00:00

  • end_time (str) – the ending date and time of interest, like 2013-07-08 00:00:00

  • output_file (str) – the output CSV data file

tesp_support.weather.TMYtoEPW module

Functions to convert Typical Meteorological Year data for EnergyPlus

The input must be in TMY2 format. If only TMY3 files are available, see the TMY3toTMY2_ansi.c program distributed with TESP under the [weather_path]/TMY2EPW/source_code directory.

Public Functions:
convert_tmy2_to_epw:

Converts TMY2 file to EPW.

convert_tmy3_to_epw:

Converts TMY3 file to EPW.

tesp_support.weather.TMYtoEPW.convert_tmy2_to_epw(in_file_root, out_file_root=None)

Converts TMY2 to EPW

Reads in_file_root.tmy2 and writes in_file_root.epw

Parameters:

  • in_file_root (str) – The input path and base file name, without extension

  • out_file_root (str) – The output path and base file name, without extension

tesp_support.weather.TMYtoEPW.convert_tmy3_to_epw(in_file_root, out_file_root=None)

Converts TMY3 to EPW

Reads in_file_root.tmy3 and writes out_file_root.epw

Parameters:

  • in_file_root (str) – The input path and base file name, without extension

  • out_file_root (str) – The output path and base file name, without extension

tesp_support.weather.TMYtoEPW.removeZero(x)

Helper function to strip leading ‘0’ from a string

tesp_support.weather.weather_agent module

Weather Agent

This weather agent needs an WEATHER_CONFIG environment variable to be set, which is a json file.

tesp_support.weather.weather_agent.convertTimeToSeconds(time)

Convert time string with unit to integer in seconds

Parses the unit in day, hour, minute and second. It will not recognize week, month, year, millisecond, microsecond or nanosecond, they can be added if needed.

Parameters:

time (str) – time with unit

Returns:

represent the input time in second

Return type:

int

tesp_support.weather.weather_agent.deltaTimeToResmapleFreq(time)

Convert time unit to a resampling frequency that can be recognized by pandas.DataFrame.resample()

Parses unit in day, hour, minute and second. It won’t recognize week, month, year, millisecond, microsecond or nanosecond, they can be added if needed.

Parameters:

time (str) – time with unit

Returns:

time with resample frequency

Return type:

str

tesp_support.weather.weather_agent.findDeltaTimeMultiplier(time)

Find the multiplier to convert delta_time to seconds

Parses unit in day, hour, minute and second. It won’t recognize week, month, year, millisecond, microsecond or nanosecond, they can be added if needed.

Parameters:

time (str) – time with unit

Returns:

the multiplier to convert delta_time to seconds

Return type:

int

tesp_support.weather.weather_agent.startWeatherAgent(file)

The weather agent publishes weather data as configured by the json file

Parameters:

file (str) – the weather data file

tesp_support.weather.weather_agent.usage()
class tesp_support.weather.weather_agent.weather_forecast(variable, period, W_dict)

Bases: object

This object includes the error to a weather variable

Parameters:

  • variable (str) – Type of weather variable being forecasted

  • period (int) – period of the sinusoidal bias

  • W_dict (dict) – dictionary for specifying the generation of the error envelope

weather_variable

Type of weather variable being forecasted

Type:

str

# Type of error insertion
distribution

type of distribution –> 0 uniform;1 triangular;2 truncated normal the standard deviation is computed for 95% of values to be within bounds in a conventional normal distribution

Type:

int

P_e_bias

pu maximum bias at first hour –> [0 to 1]

Type:

float

P_e_envelope

pu maximum error from mean values –> [0 to 1]

Type:

float

Lower_e_bound

pu of the maximum error at the first hour –> [0 to 1]

Type:

float

# Bias variable
biasM

sinusoidal bias for altering the error envelope

Type:

float) (1 X period

Period_bias

period of the sinusoidal bias

Type:

int

get_truncated_normal(EL, EH)

Truncated normal distribution

make_forecast(weather, t=0)

Include error to a known weather variable

Parameters:

  • weather (float) (1 x desired number of hours ahead) – known weather variable

  • t (int) – time in hours

Returns:

weather variable with included error ENV_U (float) (1 x desired number of hours ahead): envelope with bias upper bound ENV_l (float) (1 x desired number of hours ahead): envelope with bias lower bound

Return type:

weather_f (float) (1 x desired number of hours ahead)

tesp_support.weather.weather_agent_f module

Weather Agent

This weather agent needs an WEATHER_CONFIG environment variable to be set, which is a json file.

tesp_support.weather.weather_agent_f.convertTimeToSeconds(time)

Convert time string with unit to integer in seconds

Parses the unit in day, hour, minute and second. It will not recognize week, month, year, millisecond, microsecond or nanosecond, they can be added if needed.

Parameters:

time (str) – time with unit

Returns:

represent the input time in second

Return type:

int

tesp_support.weather.weather_agent_f.deltaTimeToResmapleFreq(time)

Convert time unit to a resampling frequency that can be recognized by pandas.DataFrame.resample()

Parses unit in day, hour, minute and second. It won’t recognize week, month, year, millisecond, microsecond or nanosecond, they can be added if needed.

Parameters:

time (str) – time with unit

Returns:

time with resample frequency

Return type:

str

tesp_support.weather.weather_agent_f.findDeltaTimeMultiplier(time)

Find the multiplier to convert delta_time to seconds

Parses unit in day, hour, minute and second. It won’t recognize week, month, year, millisecond, microsecond or nanosecond, they can be added if needed.

Parameters:

time (str) – time with unit

Returns:

the multiplier to convert delta_time to seconds

Return type:

int

tesp_support.weather.weather_agent_f.startWeatherAgent(file)

The weather agent publishes weather data as configured by the json file

Parameters:

file (str) – the weather data file

tesp_support.weather.weather_agent_f.usage()
class tesp_support.weather.weather_agent_f.weather_forecast(variable, period, W_dict)

Bases: object

This object includes the error to a weather variable

Parameters:

  • variable (str) – Type of weather variable being forecasted

  • period (int) – period of the sinusoidal bias

  • W_dict (dict) – dictionary for specifying the generation of the error envelope

weather_variable

Type of weather variable being forecasted

Type:

str

# Type of error insertion
distribution

type of distribution –> 0 uniform;1 triangular;2 truncated normal the standard deviation is computed for 95% of values to be within bounds in a conventional normal distribution

Type:

int

P_e_bias

pu maximum bias at first hour –> [0 to 1]

Type:

float

P_e_envelope

pu maximum error from mean values –> [0 to 1]

Type:

float

Lower_e_bound

pu of the maximum error at the first hour –> [0 to 1]

Type:

float

# Bias variable
biasM

sinusoidal bias for altering the error envelope

Type:

float) (1 X period

Period_bias

period of the sinusoidal bias

Type:

int

get_truncated_normal(EL, EH)

Truncated normal distribution

make_forecast(weather, t=0)

Include error to a known weather variable

Parameters:

  • weather (float) (1 x desired number of hours ahead) – known weather variable

  • t (int) – time in hours

Returns:

weather variable with included error ENV_U (float) (1 x desired number of hours ahead): envelope with bias upper bound ENV_l (float) (1 x desired number of hours ahead): envelope with bias lower bound

Return type:

weather_f (float) (1 x desired number of hours ahead)