load profile generation wip

Utilities/LoadProfile.py

@@ -1,5 +1,6 @@
 import numpy as np
-from Utilities.Time import generate_timestrings, index_peak_times
+from Utilities.Time import generate_timestrings, index_peak_times, index_operating_hours
+from scipy.optimize import root_scalar
 
 
 def get_no_of_peaks(peak_bounds):
@@ -24,6 +25,23 @@ def generate_peak_info(c, dt):
     return peak_times, peak_durations
 
 
+def generate_out_of_hours_consumption_ratio(c):
+    # Generate a random ratio for out-of-hours consumption
+    ratio = np.random.uniform(
+        c["site_info"]["out_of_hours_consumption"]["min"],
+        c["site_info"]["out_of_hours_consumption"]["max"],
+    )
+    return ratio
+
+
+def generate_realistic_profile(site, peak_indices, out_of_hours_ratio):
+    pass
+
+
+def objective(x):
+    pass
+
+
 def get_load_profile(c, dt, batch_start_time, batch_process_duration):
     # Generate load profile for each site
 
@@ -36,10 +54,19 @@ def get_load_profile(c, dt, batch_start_time, batch_process_duration):
     # generate timeseries from start to end time
     start_time = batch_start_time
     end_time = start_time + batch_process_duration
-    timestamps = np.arange(start_time, end_time, dt)
+    timestamps = np.arange(start_time, end_time + 1, dt)
+    operating_hours_indices = index_operating_hours(
+        timestamps, c["site_info"]["operating hours"]
+    )
 
-    # Generate peak times and durations
+    # loop through each site in the configuration
-    peak_times, peak_durations = generate_peak_info(c, dt)
+    for site in c["site_info"]["sites"]:
+        # Generate peak times and durations
+        peak_times, peak_durations = generate_peak_info(c, dt)
 
-    # Generate peak times and durations
+        # Generate peak times and durations
-    peak_indices = index_peak_times(timestamps, peak_times, peak_durations)
+        peak_indices = index_peak_times(timestamps, peak_times, peak_durations)
+
+        # Generate out-of-hours consumption ratio
+        # The % of energy used outside of the operating hours
+        out_of_hours_ratio = generate_out_of_hours_consumption_ratio(c)

Utilities/Time.py

@@ -4,9 +4,12 @@ import numpy as np
 import matplotlib.pyplot as pl
 
 
-def get_current_time():
+def get_start_time():
     """Returns the current time in seconds since the epoch."""
-    return time.time()
+    current_date = datetime.now()
+    # set to 00:00
+    current_date = current_date.replace(hour=0, minute=0, second=0, microsecond=0)
+    return int(current_date.timestamp())
 
 
 def generate_timestrings(start_time_str, end_time_str, dt):
@@ -40,15 +43,34 @@ def index_peak_times(timestamps, peak_times, peak_durations):
         )
         peak_start = int(full_datetime.timestamp())
         peak_end = peak_start + duration * 60  # duration in minutes to seconds
-        peak_timestamps = np.arange(peak_start, peak_end, dt, dtype=int)
+        peak_timestamps = np.arange(peak_start, peak_end + 1, dt, dtype=int)
 
-        common, indices_ts, _ = np.intersect1d(
+        indices = np.where(np.isin(timestamps, peak_timestamps))[0]
-            timestamps, peak_timestamps, return_indices=True
+        peak_indices[indices] = peak_occurence_no
-        )
-        peak_indices[indices_ts] = peak_occurence_no
         peak_occurence_no += 1
 
-    pl.plot(peak_indices, label="Peak Indices")
+    return peak_indices
-    pl.show()
 
-    return processed_times
+
+def index_operating_hours(timestamps, operating_hours):
+    """Indexes the operating hours in the timestamps."""
+    # operating_hours is a dictionary with "start" and "end" keys in HH:MM format
+
+    operating_indices = np.zeros(len(timestamps), dtype=int)
+    start_time = datetime.strptime(operating_hours["start"], "%H:%M")
+    end_time = datetime.strptime(operating_hours["end"], "%H:%M")
+
+    # get date from timestamp variable
+    start_date = datetime.fromtimestamp(timestamps[0]).date()
+    start_datetime = datetime.combine(start_date, start_time.time())
+    end_datetime = datetime.combine(start_date, end_time.time())
+
+    # convert to seconds since epoch
+    start_time = int(start_datetime.timestamp())
+    end_time = int(end_datetime.timestamp())
+
+    for i, ts in enumerate(timestamps):
+        if start_time <= ts <= end_time:
+            operating_indices[i] = 1  # mark as operating hour
+
+    return operating_indices

YAMLs/site_info.yaml

@@ -32,3 +32,6 @@ peak_duration:
   unit: minutes
   min: 1
   max: 4
+out_of_hours_consumption:
+  min: 0.05
+  max: 0.15

main.py

@@ -1,13 +1,13 @@
 import yaml
-from Utilities.Time import get_current_time
+from Utilities.Time import get_start_time
-from LoadProfile import get_load_profile
+from Utilities.LoadProfile import get_load_profile
 
 # read config file
 c = yaml.safe_load(open("YAMLs/config.yml"))
 
 ## simulation time setup
 # get current time
-c["sim_start_time"] = get_current_time()
+c["sim_start_time"] = get_start_time()
 # get time step in minutes, then convert to seconds
 dt = c["sim_time"]["time_step_minutes"] * 60
 # compute end time based on duration in days

requirements.txt

@@ -1,6 +1,16 @@
+contourpy==1.3.2
+cycler==0.12.1
+fonttools==4.58.5
+kiwisolver==1.4.8
+matplotlib==3.10.3
 numpy==2.3.1
+packaging==25.0
 pandas==2.3.1
+pillow==11.3.0
+pyparsing==3.2.3
 python-dateutil==2.9.0.post0
 pytz==2025.2
+PyYAML==6.0.2
+scipy==1.16.0
 six==1.17.0
 tzdata==2025.2