Data processing

Submodules

Data processing

class fleetrl.utils.data_processing.data_processing.DataLoader(path_name, schedule_name, spot_name, tariff_name, building_name, pv_name, time_conf, ev_conf, target_soc, building_flag, pv_flag, real_time)

Bases: object

The DataLoader class handles the csv import and pre-processing of the timeseries information. Optimized pandas functions such as merge_asof are used to significantly speed up processing compared to loops. Cython could further improve this initial processing step. It only happens once when instantiating env objects.

compute_from_schedule(ev_conf, time_conf, target_soc)

This function pre-processes the input data and adds additional rows to the file. There flag, time left at charger, soc on return, consumption, etc.

Use merge_asof and vectorized operations for performance gains

Returns:

None

load_building_load(path_name, file_name, date_range)

Load building load from csv :type path_name: :param path_name: Parent directory string :type file_name: :param file_name: file name with .csv ending :type date_range: :param date_range: pd.date_range which was defined from the “date” column in the EV schedules. Note that the EV schedule dates therefore dictate the model’s timeframe. :return: load dataframe

load_feed_in(path_name, tariff_name, date_range)

Load feed-in from csv :type path_name: :param path_name: Parent directory string :type tariff_name: :param tariff_name: file name with .csv ending :type date_range: :param date_range: pd.date_range which was defined from the “date” column in the EV schedules. Note that the EV schedule dates therefore dictate the model’s timeframe. :return: tariff dataframe

load_prices(path_name, spot_name, date_range)

Load prices from csv :type path_name: :param path_name: Parent directory string :type spot_name: :param spot_name: file name with .csv ending :type date_range: :param date_range: pd.date_range which was defined from the “date” column in the EV schedules. Note that the EV schedule dates therefore dictate the model’s timeframe. :return: spot price dataframe

load_prices_original(path_name, spot_name, date_range)

Load prices from csv :type path_name: :param path_name: Parent directory string :type spot_name: :param spot_name: file name with .csv ending :type date_range: :param date_range: pd.date_range which was defined from the “date” column in the EV schedules. Note that the EV schedule dates therefore dictate the model’s timeframe. :return: spot price dataframe

load_pv(path_name, pv_name, date_range)

Load pv from csv :type path_name: :param path_name: Parent directory string :type pv_name: :param pv_name: file name with .csv ending :type date_range: :param date_range: pd.date_range which was defined from the “date” column in the EV schedules. Note that the EV schedule dates therefore dictate the model’s timeframe. :return: pv dataframe

static shape_price_reward(db, ev_conf)

• de-trend prices, so they can be used as a reward function

• agent should not be penalised more if the general price level is higher

• instead, the agent should just focus on price fluctuations and exploit them

• computing average for whole year, split data into monthly chunks

• offset monthly chunks, such that the monthly average = annual average

• this corrects for absolute price increases, but leaves fluctuations intact

Parameters:

• db (DataFrame) – database with schedules, pv, prices, load, dataframe

• ev_conf (EvConfig) – ev config object

Returns:

db with updated, de-trended prices