There is also a number of Jupyter Lab notebooks with examples of how to run the sample code.
This project is not endorsed by, directly affiliated with, maintained, authorized, or sponsored by Fox ESS.
Please refer to the LICENCE for information on copyright, permissions and warranty.
This module builds on the Fox Open API to provide a sample code and utilities that can be used with your inverter and batteries.
- Information on the API can be found here: Open API Documentation
- A list of the error codes used by the API is available here: Wiki/Error-Codes.
To initialise a Jupyter Lab notebook to use the open API, copy the following text and edit the configuration variables needed to add your values:
!pip install foxesscloud --root-user-action=ignore --quiet
import foxesscloud.openapi as f
# add your info here
f.api_key = "my.fox_api_key"
f.device_sn = "my.fox_device_sn"
f.time_zone = "Europe/London"
f.pv_api_key = "my.pv_api_key"
f.pv_system_id = "my.pv_system_id"
f.solcast_api_key = "my.solcast_api_key"
f.plot_file = "plot###.png"
f.pushover_user_key = "my.pushover_user_key"
Advanced users: use the same sequence in bash/python scripts to install modules and initialise variables in a run time enviromment.
You don't have to configure all of the settings. Your Fox ESS Cloud api key is the minimum required to access data about your inverter. Your Fox API key is obtained from foxesscloud.com. Login, go to User Profile, API Management, click Generate API key. Take a copy of the key and save it so you add it to your scripts and notebooks.
For example, replace my.fox_api_key with the API key. Add you inverter serial number if you have more than 1 inverter linked to your account. Be sure to keep the double quotes around the values you enter or you will get a syntax error.
If a value is set for f.plot_file, any charts created will also be saved to an image file:
- f.plot_file: the file name to use. The file extension determines the format - .png, .pdf or .svg. If you provide just a filename, each chart will over-write the file. The default is None and disables saving.
- f.plot_no: if the file name contains ###, this will be replaced by 3 digit plot number that increases for each chart created. The default is 0.
- f.plot_dpi: sets the image resolution. The default is 150. Reducing this value produces smaller, lower resolution images. Increasing this value produces larger, highe resolution images
If you set f.pushover_user_key to your user_key for pushover.net, a summary from set_tariff(), charge_needed(), set_pvoutput() and battery_info() will be sent to your pushover app.
Return information about the current user:
f.get_access_count()
Returns the 'total' number of API accesses allowed per day and the number of API accesses 'remaining' today.
Load information about a site, data logger or inverter (device):
f.get_site()
f.get_logger()
f.get_device()
By default, this will load the first item in the list provided by the cloud. If there is more than 1 item, the call will show the list of items. To select a specific item to work with, call a qualifier:
- Site: full or partial name of the site
- Logger: full or partial serial number
- Inverter: full or partial serial number
When an item is selected, the functions returns a dictionary containing item details and saves these to a global variable (f.site, f.logger, f.device respectively)
Once an inverter is selected, you can make other calls to get information:
f.get_generation()
f.get_battery()
f.get_settings()
f.get_charge()
f.get_min()
f.get_schedule()
Each of these calls will return a dictionary or list containing the relevant information.
get_generation() will return the latest generation information for the device. The results are also stored in f.device as 'generationToday', 'generationMonth' and 'generationTotal'.
get_battery() returns the current battery status, including 'soc', 'volt', 'current', 'power', 'temperature' and 'residual'. The result also updates f.battery.
get_settings() will return the battery settings and is equivalent to get_charge() and get_min(). The results are stored in f.battery_settings. The settings include minSoc, minSocOnGrid, enable charge from grid and the charge times.
get_schedule() returns the current work mode / soc schedule settings. The result is stored in f.schedule.
You can change inverter settings using:
f.set_min(minSocOnGrid, minSoc)
f.set_charge(ch1, st1, en1, ch2, st2, en2)
f.set_group(start, end, mode, min_soc, fdsoc, fdpwr)
f.get_flag()
f.set_schedule(enable, groups, template)
set_min() applies new SoC settings to the inverter. The parameters update battery_settings:
- minSocOnGrid: min Soc on Grid setting e.g. 15 = 15%
- minSoc: min Soc setting e.g. 10 = 10%
set_charge() takes the charge times from the battery_settings and applies these to the inverter. The parameters are optional and will update battery_settings. You should specify all 3 parameter for a time period:
- ch1: enable charge from grid for period 1 (True or False)
- st1: the start time for period 1
- en1: the end time for period 1
- ch2: enable charge from grid for period 2 (True or False)
- st2: the start time for period 2
- en2: the end time for period 2
set_flag() returns the current settings for strategy periods: 'supported' and 'enable'
set_group() returns a time segment structure that can be used to build a list of time segments for set_schedule()
- start, end, mode: required parameters
- min_soc: optional, default is 10
- fdsoc: optional, default is 10. Used when setting a period with ForceDischarge mode
- fdpwr: optional, default is 0. Used when setting a period with ForceDischarge mode
- enable: sets whether this time segment is enable (1) or disabled (0). The default is enabled.
set_schedule() configures a list of scheduled work mode / soc changes with enable=1. If called with enable=0, any existing schedules are disabled. To enable a schedule, you must provide a list of time segments
- enable: 1 to enable schedules, 0 to disable schedules. The default is 1.
- groups: a time segment or list of time segments created using f.set_group().
Real time data reports the latest values for inverter variables, collected every 5 minutes:
f.invert_ct2 = 1
f.get_vars()
f.get_real(v)
f.invert_ct2 determines how the meterPower2 data is handled. When invert_ct2 = 0, meterPower2 produces +ve power values during secondary generation. If meterPower2 produces -ve power values during secondary generation, setting invert_ct2 = 1 will flip the values so they are +ve when generating. The default setting is 1 (invert).
f.get_vars() returns the list of variables that can be queried. This also stores the information:
- f.var_table: a table, indexed by variable that contains information such as the name and unit. ++ f.var_list: a list of all the variables that are available
There are also pre-defined lists:
- power_vars lists the main power variables provided by the inverter
- battery_vars lists the main variables relevant to the battery / BMS
f.get_real returns the latest values for a list of variables.
- v is a variable, or list of variables. The default is to return the latest value for all available variables
History data reports inverter variables, collected every 5 minutes, on a given date / time and period:
f.get_history(time_span, d, v, summary, save, load, plot)
- time_span determines the period covered by the data, for example, 'hour', 'day' or 'week'. The default is 'hour'
- d is a date and time in the format 'YYYY-MM-DD HH:MM:SS'. The default is today's date and time. d may also be a list of dates
- v is a variable, or list of variables (see above)
- summary is optional - see below
- save: set to the root part of a filename to save the results
- load: set to the full filename to load previously saved results
- plot is optional. 1 plots the results with a chart per unit and per day. 2 plots multiple days on the same chart. Default is 0, no plots
The setting for invert_ct2 is applied to history data for meterPower2, so +ve values are returned for secondary generation.
f.sample_time is set to the sample time in minutes for the data processed, rounded to f.sample_rounding samples per minute.
Data generation for the full list of raw_vars can be slow and return a lot of data, so it's best to select the vars you want from the list if you can.
For example, this Jupyter Lab cell will load an inverter and return power data at 5 minute intervals for the 17th June 2023:
d = '2023-06-17 00:00:00'
result=f.get_history('day', d=d, v=f.power_vars)
Setting the optional parameter 'summary' when calling get_raw() provides a summary of the raw data
- summary = 0: basic history data, no summary
- summary = 1: summary is calculated
- summary = 2: summary is calculated and raw data is removed to save time / space
- summary = 3: as (2) but for energy only, an hourly cumulative state is also generated, similar to the state used in Home Assistant long term statistics
The summary includes the following attributes:
- count: the number of data points
- average: the average value of the data points
- max: the maximum value of the data points
- max_time: the time when the maximum value occured (HH:MM)
- min: the minimum value of the data points
- min_time: the time when the minimum value occured (HH:MM)
For power values (unit = kW), the summary performs a Riemann sum of the data, integrating kW over the day to estimate energy in kWh. In this case, the following attributes are also added:
- kwh: the total energy generated or consumed
- kwh_off: the total energy consumed or generated during the off-peak time of use
- kwh_peak: the total energy consumed or generated during the peak time of use
- kwh_neg: the total energy from -ve power flow (all other totals are based on +ve power flow)
This example shows power graphs for today and yesterday:
Report data provides information on the energy produced by the inverter, battery charge and discharge energy, grid consumption and feed-in energy and home energy consumption:
f.get_report(report_type, d, v, summary, save, load, plot, station)
- report_type sets the period covered by the report and is one of 'day', 'week', 'month', 'year':
- when 'day' is selected, energy is reported each hour through the day
- when 'week' is selected, energy is reported for the 7 days up to and including the date
- when 'month' is selected, energy is reported each day through the month
- when 'year' is selected, energy is reported each month through the year
- d is a date and time in the format 'YYYY-MM-DD HH:MM:SS'. The default is yesterday. d may also be a list of dates
- v is a variable, or list of variables. The default is to use report_vars
- summary is optional - see below
- save: set to the root part of a filename to save the results
- load: set to the full filename to load previously saved results
- plot is optional. 1 to plot results
- station is optional. 1 gets data for a site (using f.station_id), 0 gets data for a device (using f.device_id). The default is 0.
The list of variables that can be reported on is stored in f.report_vars.
Note that reporting by 'day' produces inaccurate hourly data, where the sum does not reconcile with the daily total given in the monthly report. To correct this, reporting by day also gets the monthly data and uses the daily total to correctly report the total.
Setting the optional parameter 'summary' when calling get_report() provides a summary of the report data:
- summary = 0: basic report data, no summary. report_type cannot be 'week'
- summary = 1: summary is calculated
- summary = 2: corrected total only is reported to save time / space. report_type must be 'day'
The result data for each variable includes the following attributes when summary=2
- 'variable': name of the data set
- 'total': corrected total of the data items
When summary=1, the following items are also added:
- 'data': 'index' and 'value' of each data point
- 'date': that was used to produce the report
- 'count': the number of data items
- 'sum': the sum of the data items
- 'max': the biggest value in 'data'
- 'max_index': the index of the biggest value in 'data'
- 'min': the smallest value in 'data'
- 'min_index': the index of the smallest value in 'data'
- 'average': corrected average of the data items
For example, this Jupyter Lab cell will report energy data by day for the month of June 2023:
d = '2023-06-17'
result=f.get_report('month', d=d)
This example plots weekly data:
The previous section provides functions that can be used to access and control your inverter. This section covers utilities and operations that build upon these functions.
Uses forecast PV yield for tomorrow to work out if charging from grid is needed tonight to deliver the expected consumption for tomorrow. If charging is needed, the charge times are configured. If charging is not needed, the charge times are cleared. The results are sent to the inverter.
f.charge_needed(forecast, force_charge, forecast_selection, forecast_times, update_setings, show_data, show_plot)
All the parameters are optional:
- forecast: the kWh expected tomorrow (optional, see below)
- force_charge: 1 any remaining time in a charge period has force charge set, 2 charging uses the entire charge period, 0 None (default)
- forecast_selection: if set to 1, settings are only updated if there is a forecast. Default is 0, generation is used when forecasts are not available
- forecast_times: a list of hours when forecasts can be obtained. By default, the forecast times for the selected tariff are used (see below)
- update_settings: 0 no changes, 1 update charge time, 2 update work mode, 3 update charge time and work mode. The default is 0
- show_data: 1 show battery SoC data, 2 show battery Residual data, 3 show timed data, 4 show timed data before and after charging. The default is 1.
- show_plot: 1 plot battery SoC data. 2 plot battery Residual, Generation and Consumption. 3 plot 2 + Charge and Discharge The default is 3
charge_needed() uses a number of models to better estimate the state of the battery.
Manual Consumption: You can provide your 'annual_consumption' in kWh e.g. 5500. This figure is factored down to a daily consumption by dividing by 365 and applying f.seasonality. This normally decreases consumption in the summer and increases it in winter. Seasonality is a list of weightings by month for Jan, Feb, Mar, Apr etc. Preset lists are 'f.high_seasonality' (recommend where electric heating is ued), 'f.medium_seasonality' (default) amd 'f.no_seasonality' (all months the same). The daily consumption is profiled by hour using f.daily_consumption. This maps your consumption for a day to the hours when more or less energy is consumed. It is a list of 24 values for the times 00, 01, 02, 03 .. 23. Preset lists are 'f.high_profile' (larger peaks at 8am and 6pm), 'f.medium_profile' (default, more balanced) and 'f.no_profile' (flat).
Historic Consumption: If annual_consumption is not provided, your consumption history is used. By default, this looks at your average consumption for the last 3 days using the load power reported by your inverter. For systems with multiple inverters where CT2 is not connected, the load power may not be correct. For this and other cases where you want to set your consumption, provide your annual_consumption.
Manual Forecast: You can provide a specific 'forecast' in kWh e.g. 20. This is profiled using f.seasonal_sun to map solar generation to the time of day. The mapping is broken down into 4 seasons: winter, spring, summer and autumn (winter is Dec, Jan, Feb, spring is Mar, Apr, May etc). There are 4 preset lists: 'f.winter_sun', 'f.spring_sun', 'f.summer_sun' and 'f.autumn_sun'. Seasonal_sun is used for manual and historic forecasts
Solcast: If you provide an API key for Solcast, your forecast will be downloaded after 9pm each day and used as the basis for your next days generation (see below).
Solar: if you configure one or more f.solar_array, forecast.solar will be called to provide a forrecast for your next days generation (see below).
Historic Generation: If 'forecast' is not provided and Solcast and Solar forecasts are not available, your generation history is used. By default, this looks at your average solar generation for the last 3 days and applies the f.seasonal_sun profile.
Note: if using Solcast or forecast.solar, calls to the API are very limited so repeated calls to charge_needed can exhaust the calls available, resulting in failure to get a forecast. The tariff forecast_times set the hours when forecast data is fetched (see tariffs).
Given the data available, the modelling works as follows:
- gets current information on your battery
- estimates your consumption (including contigency)
- gets forecast data from Solcast or forecast.solar (if configured)
- gets your generation history
- uses the charge available now and the expected charging or discharging of the battery to forecast the battery state
- works out if there is a deficit (i.e. when the battery would be discharged below your min_soc)
- reports the charge needed (deficit) or the minimum expected battery level
- updates your battery charge settings (if update_settings is not 0)
- gets the current work mode and updates this if timed work mode changes are configured
The following parameters and default values are used to configure charge_needed and may be updated if required using name=value:
contingency: 20 # % of consumption to allow as contingency
capacity: None # Battery capacity in kWh (over-rides generated value if set)
charge_current: None # max battery charge current setting in A. None uses a value derrived from the inverter model
discharge_current: None # max battery discharge current setting in A. None uses a value derrived from the inverter model
export_limit: None # maximum export power. None uses the inverter power rating
discharge_loss: 0.97 # loss converting battery discharge power to grid power
pv_loss: 0.95 # loss converting PV power to battery charge power
grid_loss: 0.97 # loss converting grid power to battery charge power
charge_loss: None # loss converting charge power to residual
inverter_power: None # inverter power consumption W (dynamically set)
bms_power: 25 # BMS power consumption W
bat_resistance: 0.070 # internal resistance of a battery in ohms
volt_curve: lifepo4_curve # battery OCV from 0% to 100% SoC
nominal_soc: 55 # SoC for nominal open circuit voltage
generation_days: 3 # number of days to use for average generation (1-7)
consumption_days: 3 # number of days to use for average consumption (1-7)
consumption_span: 'week' # 'week' = last 7 days or 'weekday' = last 7 weekdays e.g. Saturdays
use_today: 21.0 # hour when today's generation and consumption data will be used
min_hours: 0.25 # minimum charge time to set (in decimal hours)
min_kwh: 0.5 # minimum charge to add in kwh
solcast_adjust: 100 # % adjustment to make to Solcast forecast
solar_adjust: 100 # % adjustment to make to Solar forecast
forecast_selection: 1 # 1 = only update charge times if forecast is available, 0 = use best available data. Default is 1.
annual_consumption: None # optional annual consumption in kWh. If set, this replaces consumption history
timed_mode: 0 # 1 = apply timed work mode, 0 = None
special_contingency: 30 # contingency for special days when consumption might be higher
special_days: ['12-25', '12-26', '01-01']
full_charge: None # day of month (1-28) to do full charge or 'daily' or day of week: 'Mon', 'Tue' etc
derate_temp: 21 # battery temperature in C when derating charge current is applied
derate_step: 5 # step size for derating e.g. 21, 16, 11
derating: [24, 15, 10, 2] # derated charge current for each temperature step e.g. 21C, 16C, 11C, 6C
force: 1 # 1 = disable strategy periods when setting charge. 0 = fail if strategy period has been set.
data_wrap: 6 # data items to show per line
target_soc: None # target soc for charging
These values are stored / available in f.charge_config.
The default battery open circuit voltage curve versus SoC from 0% to 100% is:
lifepo4_curve = [51.30, 52.00, 52.30, 52.40, 52.50, 52.60, 52.70, 52.80, 52.9, 53.1, 53.50]
This example shows the results reported by charge needed:
Provides detailed information on the current state of the batteries:
f.battery_info(count, plot, log)
f.battery_monitor(interval, run, log, save, count)
battery_info() prints information on the battery and cells:
- count: optional over-ride. The default is based on factorising the number of cells reported by 16 or 18 to work out the number of batteries.
- plot: 1 plot the cell voltages for each battery, 2 plot the cell temperatueres, 0 don't plot. The default is 1
- log: see below. Default is 0
battery_monitor() runs battery_info() in log mode on a schedule to provide information on the battery status over a period of time:
- interval: the time in minutes between log entries. The default is 30 minutes
- run: the number of log entries to create. The default is 48 i.e. every 30 minues for 24 hours in total
- log: 0 = display, 1 = log battery info, 2 = add cell volts, 3 = add cell temps. The default is 1
- save: name of a CSV file to write log data to
- count: optional over-ride for the number of batteries
This is an example of the output from battery_info():
f.date_list(s, e, limit, span, today)
Returns a list of dates in the format 'YYYY-MM-DD'. This function will not return dates in the future. The last date will be yesterday or today (if today is True). All parameters are optional:
- s: start date
- e: end date
- limit: maximum number of days. The default is 200
- span: the range of dates. One of 'day', 'week', 'month' or 'year', '2days' or 'weekday'
- today: 1 allows today to be included, 2 allows future dates to be included. Default is 0, date list will stop at yesterday
You can use 'span' as follows:
- 'day' provides a single day
- 'week' will provide the dates of 7 consequetive days
- 'month' will provide the dates of the days up to the same date in the preceeding (or follwing) month
- '2days' will provide the dates of yesterday and today
- 'weekday' will provide the dates of the same day of the week, going backwards (or forwards) up to 7 weeks
f.british_summer_time(d) # 1 if d is in Britsh Summer Time, 0 if not
Times and time period settings are held as decimal hours. Functions for working with time strings with the format 'HH:MM:SS' and decimal hours include:
f.time_hours(t, d=None) # convert time to decimal hours. t is a time string ('HH:MM' or 'HH:MM:SS'), d is optional and is the default time if s is None
f.hours_time(h, mm=True, ss=False, day=False) # convert decimal hours to time (HH:MM:SS). mm = include minutes, ss = include seconds, day = include /n for day when hours > 24
f.hours_in(h, {'start': a, 'end': b}) # True if decimal hour h is in the time period a -> b
Tariffs configure when your battery can be charged and provide time of use (TOU) periods to split your grid import and export into peak, off-peak and shoulder times when data is uploaded to PV Ouptut.
There are a number of different pre-configured tariffs:
- Octopus Flux: off-peak from 02:00 to 05:00, peak from 16:00 to 19:00, forecasts from 22:00 to 23:59. Timed work mode change to Self Use at 7am and Feed In First at 4pm.
- Intelligent Octopus: off-peak from 23:30 to 05:30, forecasts from 22:00 to 23:59
- Octopus Cosy: off-peak from 04:00 to 07:00 and 13:00 to 16:00, peak from 16:00 to 19:00, forecasts from 02:00 to 03:59 and 12:00 to 12:59
- Octopus Go: off peak from 00:30 to 04:30, forecasts from 22:00 to 23:59
- Agile Octopus: off-peak from 02:30 to 05:00, peak from 16:00 to 19:00, forecasts from 22:00 to 23:59
- British Gas Electric Driver: off-peak from 00:00 to 05:00, forecasts from 22:00 to 23:59
- Eco 7: Economy 7: off-peak from 00:30 to 07:30 GMT (01:30 to 08:30 during BST)
Custom periods can be configured for specific times if required:
- Custom: charging from 02:00 to 05:00, no off-peak or peak times, forecasts from 22:00 to 23:59
The active tariff is configured by calling 'f.set_tariff() with the name of the tariff to use:
f.set_tariff('flux')
When Agile Octopus is selected, a price based charging period is configured using the 30 minute price forecast. For example:
f.set_tariff('agile', product, region, start_at, end_by, duration, times, forecast_times, work_times, update, weighting, time_shift)
This gets the latest 30 minute pricing and uses this to work out the best off peak charging period.
- product: optional Agile Octopus product code (see below). The default is "AGILE-FLEX-22-11-25"
- region: optional region to use for prices (se below). The default is 'H' (Southern England)
- start_at: optional earliest start time for charge period in hours, the default is 23:00
- end_by: optional latest end time for charge period in hours, the default is 08:00
- duration: optional charge time period in hours, the default is 3 hours. Valid range is 1-6 hours
- times: a list of charge periods that can be used instead of start_at, end_by and duration (see below)
- forecast_times: a list of times when a forecast can be obtained from Solcast / forecast.solar
- work_times: a list of work modes and times when these are set. The format is [(mode, start, end),...]
- update: optional, 1 (the default) sets the current tariff to Agile Octopus. Setting to 0 does not change the current tariff
- weighting: optional, default is None (see below)
- time_shift: optional system time shift in hours. The default is for system time to be UTC and to apply the current day light saving time (e.g. GMT/BST)
Product codes include:
- 'AGILE-18-02-21' = The original version capped at 35p per unit
- 'AGILE-22-07-22' = The cap rose to 55p
- 'AGILE-22-08-31' = The cap was increased to 78p
- 'AGILE-VAR-22-10-19' = This version raised the cap to £1 per unit and also introduced a new formula.
- 'AGILE-FLEX-22-11-25' = Cap stays at £1 per unit but new formula only deducts 17.9p from higher unit prices (default)
Region codes include:
- 'A' = Eastern England
- 'B' = East Midlands
- 'C' = London
- 'D' = Merseyside and Northern Wales
- 'E' = West Midlands
- 'F' = North Eastern England
- 'G' = North Western England
- 'H' = Southern England (default)
- 'J' = South Eastern England
- 'K' = Southern Wales
- 'L' = South Western England
- 'M' = Yorkshire
- 'N' = Southern Scotland
- 'P' = Northern Scotland
Pricing for tomorrow is updated around 4pm each day. If run before this time, prices from yesterday are used. By default, prices for tomorrow are fetched after 5pm. The setting for this is:
- f.agile_update_time = 17
The best charging period is determined based on the weighted average of the 30 minute prices over the duration. The default is flat (all prices are weighted equally). You can change the weighting by providing 'weighting'. The following preset weightings are provided:
- f.front_loaded: [1.0, 0.9, 0.8, 0.7, 0.6, 0.5]
- f.first_hour: [1.0, 1.0]
Specifying start_at, end_by and duration allows either the AM or PM charging slot for any tariif to be updated, depending on the time. Agile periods will be calculated; othe rtariffs will use the start and end times directly. By default, set_tariff() updates the AM charge period if start_at is after 9pm and end_by is before 8am; it updates the PM charge period if start_at is after 8am and the end_by is before 9pm. Only the relevant AM or PM charge time is updated e.g. if you configure a PM charging period, the AM charginig period is not changed.
To disable a charging period, set duration=0.
set_tariff() can configure multiple charging periods for any tariff using the times parameter instead of start_at, end_by and duration. Times is a list of tuples containing values for start_at, end_by and duration. For example, this parameter configures an AM charging period between 11pm and 8am with a 3 hour period and a PM charging period between 12 noon and 4pm with a 1 hour period:
- times=[("23:00", "8:00", 3), ("12:00", "16:00", 1)]
These functions produce CSV data for upload to pvoutput.org including PV generation, Export, Load and Grid consumption by day in Wh. The functions use the energy estimates created from the raw power data (see above). The estimates include PV energy generation that are not otherwise available from the Fox Cloud. Typically, the energy results are within 3% of the values reported by the meters built into the inverter.
Returns CSV upload data using the API format:
f.get_pvoutput(d, tou)
- d is the date or a list of dates, to get data for. The default is yesterday
- tou: optional, setting tou=1 uploads data with time of use. The default, tou=0 does not split data and is more accurate.
You can copy and paste the output data to the pvoutput data CSV Loader, using the following settings:
For example, this Jupyer Lab cell will provide a CSV data upload for June 2023:
f.get_pvoutput(f.date_list('2023-06-01', '2023-06-30'))
Loads CSV data directly using the PV Ouput API:
f.set_pvoutput(d, system_id, tou, push)
- d is optional and is the date, or a list of dates, to upload
- system_id is optional and allow you to select where data is uploaded to (where you have more than 1 registered system)
- tou: optional, setting tou=1 uploads data with time of use. The default, tou=0 does not split data and is more accurate
- push: optional. 0 = do not sent to pushover, 1 = send summary to pushover, 2 = send first day summary only
Get and display solar data from your solcast.com account using your API key:
f.solcast_api_key = "my.solcast_api_key"
fcast = f.Solcast()
print(fcast)
Returns a 7 day forecast. Optional parameters are:
- days: number of days to get. The default is 7
- estimated: whether to get history / estimated data. 1 = yes, 0 = no. Default is 0.
- reload: cached data handling. 0 = use saved data, 1 = fetch new data, 2 = use saved data for today (default)
- quiet: True to stop Solcast producing progress messages
Forecast data is saved to f.solcast_save. The default is 'solcast.txt'.
fcast.plot_daily()
fcast.plot_hourly(day)
Plots the estimate / forecast data. plot_daily() plots the daily yield. plot_hourly() plots each day separately.
- day: optional. 'today', 'tomorrow', 'all' or a specific list of dates. The default is to plot today and tomorrow
Get and display solar data from forecast.solar for today and tomorrow:
f.solar_array('South', lat=51.1789, lon=-1.8262, kwp=6.4)
You need to configure your solar arrays by calling f.solar_array(). This takes the following parameters:
- name: the name of each of your arrays
- lat: the latitude where the array is located. The default is Stonehenge.
- lon: the longitude where the array is located. The default is Stonehenge.
- dec: the declination of the array - 0 is lying flat and 90 is vertical. Default is 30
- az: azimuth of the array. 0 is pointing due South, -90 is pointing East, 90 is pointing West. The default is 0
- kwp: the size of the array in kWp. The default is 5kWp
- dam: damping factor. Default is None
- inv: inverter power limit (when the array will clip). The default is None
- hor: a list of values describing obstructions on the horizon
Add one array for each string attached to your inverter. If your solar production is limited by clipping, set the inverter power so the forecast better matches your generation.
See the API documentation for more information on parameter values.
fcast = f.Solar()
print(fcast)
Returns a forecast for today and tomorrow. Optional parameters are:
- reload: cached data handling. 0 = use saved data, 1 = fetch new data, 2 = use saved data for today (default)
- quiet: set to True to stop Solar producing progress messages
Forecast data is saved to f.solar_save. The default is 'solar.txt'.
fcast.plot_daily()
fcast.plot_hourly(day)
Plots the estimate / forecast data. plot_daily() plots the daily yield. plot_hourly() plots each day separately.
- day: optional. 'today', 'tomorrow', 'all' or a specific list of dates. The default is to plot today and tomorrow
Send messages to a pushover user account:
f.output_spool(app_key, h)
f.output(s)
f.output_close(plot, file)
f.output_message(app_key, message, plot)
Calling f.output_spool() with an app key will start the system spooling output to send to pushover. h is an optional header to add as the first line of the message. H may include <time>, <date> or <datetime> and these will be set to current system time and date respectively.
When spooling is active, any calls to f.output() add lines to the spooled message. If appending to the message would exceed 1024 characters, the existing spooled message is sent and a new message spool is started.
Calling f.output_close() will send the spooled message and optionally attach a binary image file. You can set plot=1 to attach the last plot file created (when f.plot_file is set) or specify a file.
f.output_message() is a shorcut to send a message without spooling output.
If needed, you can add the following setting to increase the level of information reported by the foxesscloud module:
f.debug_setting = 2
This setting can be:
- 0: silent mode (minimal output)
- 1: information reporting (default)
- 2: more debug information, updating of inverter settings is disabled
- 3: internal variables and values are displayed (verbose)
2.2.5
Refactor debug messaging.
Simplify charge_needed() output.
Added 'target_soc' to charge_needed() settings
Fix bat_info() giving incorrect temperatures when API returns 0 instead of -50 where there is no battery
Fix key error when accessing cell volts and temps using an agent / installer account.
Ensure output is generated if get_battery() fails using battery_info().
Update f.avg() to include calculation of averages in lists containng None values.
Added 'data_wrap' to charge_config.
2.1.9
Update get_history() to use GMT or BST when plotting instead of mixed time zones.
Added 'economy_7' tariff that charges using GMT when clocks change.
Updated charge / discharge profiles for charge_needed() to show power flow in relation to work mode.
Better reporting of reason for http error code.
Template code for get_named_settings() added - not functional in this version due to Open API limitations.
Update set_pvoutput() to allow push=2.
Fix problem in set_pvoutput() sending summary to pushover when tou=1.
Improve accuracy of time of use data in get_pvoutput().
Improve handling of date / time for queries.
Comments added to pvoutput data when exported exceeds generated.
Fixed charge times being set incorrectly in set_tariff() when tariff is not Agile or Flux.
Show inverter model info and flag error in charge_needed() if residual is less than 0.1 kWh.
Updated output text to work better with pushover limit of 1024 bytes.
Fixed incorrect pushover app key for battery info.
Added pushover summary to set_pvoutout().
Added support for pushover notifications in set_tariff(), charge_needed() and battery_info().
Added saving plots to an image file.
Fix problem setting charge times if values are not read before writing.
Debug information added for HTTP timeout.
Fixed a problem where erratic sample times resulted in incorrect energy calculation.
2.0.9
Added HTTP request reponse_time monitoring.
Added 60 second time-out and retry for http requests.
Fix to handle history and report values returned by Fox that are null.
Added invert_ct2 setting so the values for secondary generation can be configured so they are always +ve for secondary generation.
Updated condition where charge needed exceeds battery capacity.
Revised battery LiFePO4 calibration.
Updated battery_info() to show more derrived battery data.
Fix battery_monitor() logging to file.
Improvements to determine number of batteries.
Updated battery_info() and battery_monitor() to include mre log information and save to file.
Updated library that uses the Fox Open API. Information on the API can be found here: Open API Documentation