electricity_demand.py

"""generating electrical demand profile from elexon profiles
"""
from importlib.resources import files as ifiles
import pandas as pd
from pathlib import Path
import matplotlib.pyplot as plt
import numpy as np

from ..constants import INDIR

plt.style.use('ggplot')
plt.rcParams.update({'font.size': 18})



def import_elexon_profile():

    # import the unrestricted residential
    # elexon electricity profile from excel sheet
    path = ifiles('pylesa').joinpath('data', 'electricity_profiles.xlsx')
    df = pd.read_excel(path)
    df = df.drop(columns=['Profile Class 1'])
    columns = df.iloc[1].values
    data = df.iloc[2:50].values
    p = pd.DataFrame(data, columns=columns)
    p_av = ((p + p.shift(-1)) / 2)[::2]
    p_av = p_av.reset_index(drop=True)
    return p_av


def time_of_year(hour):

    # TIMINGS PROBABLY NOT ACCURATE - ESTIMATIONS MADE

    # function returning time of year for the hour chosen
    # end of winter is last day of march. simplify to end of march
    # 31 march... 90 days between zero hour and end of march 31st
    # 90 * 24 = 2160
    if hour >= 0 and hour < 2160:
        toy = 'Wtr'
    # spring is starting on 31st march... 15th June approx
    # 24 * 77 + 2160 = 4008
    elif hour >= 2160 and hour < 4008:
        toy = 'Spr'
    # summer is between 25th august and ten weeks after
    # 10 * 7 * 24 + 4008 = 5688
    elif hour >= 4008 and hour < 5688:
        toy = 'Smr'
    # high summer is 6 weeks and 2 days
    # (6 * 7 + 2) * 24 + 5688 = 6744
    elif hour >= 5688 and hour < 6744:
        toy = 'Hsr'
    # autumn is the period up to clock change
    # 25 august to 27 october
    # 64 days
    # 64 * 24 + 6744 = 8280
    elif hour >= 6744 and hour < 8280:
        toy = 'Aut'
    # then into winter
    else:
        toy = 'Wtr'

    return toy


def day_of_week(year, hour):

    # first day of year
    # a = datetime.datetime(year, 1, 1)
    # first_day = a.strftime('%A')
    year = str(year)
    data = pd.date_range('1/1/' + year, periods=8760, freq='h')
    day = data[hour].strftime('%A')
    if day == 'Saturday':
        day = 'Sat'
    elif day == 'Sunday':
        day = 'Sun'
    else:
        day = 'Wd'
    return day


def year_time_series(year):

    df = import_elexon_profile()
    demand = []
    for hour in range(8760):
        toy = time_of_year(hour)
        dow = day_of_week(year, hour)
        column_name = toy + ' ' + dow
        hour_day = hour % 24
        demand.append(df[column_name][hour_day])
    # plt.plot(demand)
    # plt.show()
    return demand


def profile_from_input(year: int, root: Path, subname: str):
    file1 = Path(root).resolve() / INDIR / subname / "heating.pkl"
    heating = pd.read_pickle(file1)

    standard_profile = year_time_series(year)
    standard_profile = np.array(standard_profile)

    profiles = []
    aggregate = 0
    number_profiles = len(heating)
    for p in range(number_profiles):
        number_of_type = heating['Number of type'][p]
        profiles.append(standard_profile * number_of_type)
        aggregate += standard_profile * number_of_type

    # plt.plot(aggregate)
    # plt.show()

    return aggregate


def predicted_demand(year, name, subname):

    def movingaverage(values, window):
        weights = np.repeat(1.0, window) / window
        sma = np.convolve(values, weights, 'valid')
        return sma

    y = profile_from_input(year, name, subname)

    window = 7
    yMA = movingaverage(y, window)
    yMA_max = np.amax(yMA)
    y_max = np.amax(y)
    ratio = y_max / yMA_max
    inserting = np.array(yMA[: window - 1])
    inserting = ratio * inserting
    yMA = np.insert(yMA, 1, inserting)

    path = ifiles('pylesa').joinpath('data', 'demand.predicted_elec_demand.csv')
    np.savetxt(path, yMA, delimiter=",", fmt='%.3e')


def plot_demand():
    path = ifiles('pylesa').joinpath('data', 'demand.predicted_elec_demand.csv')
    df = pd.read_csv(path, header=None, names=['dem'])

    plt.plot(df['dem'][24:72], 'b', linewidth=1)
    plt.ylabel('Energy (kWh)')
    plt.xlabel('Hour')
    plt.show()