46 lines
1.6 KiB
Python
46 lines
1.6 KiB
Python
import numpy as np
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from Utilities.Time import generate_timestrings, index_peak_times
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def get_no_of_peaks(peak_bounds):
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peak_occurences = np.random.randint(peak_bounds["min"], peak_bounds["max"], 1)
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return peak_occurences
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def generate_peak_info(c, dt):
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no_of_peaks = get_no_of_peaks(c["site_info"]["no_of_peaks"])
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operating_hours = generate_timestrings(
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c["site_info"]["operating hours"]["start"],
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c["site_info"]["operating hours"]["end"],
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dt,
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)
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peak_times = np.random.choice(operating_hours, no_of_peaks, replace=False)
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peak_durations = np.random.randint(
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c["site_info"]["peak_duration"]["min"],
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c["site_info"]["peak_duration"]["max"],
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no_of_peaks,
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)
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return peak_times, peak_durations
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def get_load_profile(c, dt, batch_start_time, batch_process_duration):
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# Generate load profile for each site
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# c is the configuration dictionary
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# dt is the time step in seconds
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# batch_start_time is the start time for the batch process in seconds since the epoch
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# batch_process_duration is the duration of the batch process in seconds
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# start with indexing all the peak occurences
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# generate timeseries from start to end time
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start_time = batch_start_time
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end_time = start_time + batch_process_duration
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timestamps = np.arange(start_time, end_time, dt)
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# Generate peak times and durations
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peak_times, peak_durations = generate_peak_info(c, dt)
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# Generate peak times and durations
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peak_indices = index_peak_times(timestamps, peak_times, peak_durations)
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