wip load profie simulation

Utilities/LoadProfile.py

@@ -1,6 +1,7 @@
 import numpy as np
 from Utilities.Time import generate_timestrings, index_peak_times, index_operating_hours
 from scipy.optimize import root_scalar
+import pandas as pd
 
 
 def get_no_of_peaks(peak_bounds):
@@ -34,12 +35,15 @@ def generate_out_of_hours_consumption_ratio(c):
     return ratio
 
 
-def generate_realistic_profile(site, peak_indices, out_of_hours_ratio):
+def recompute_load_profile(
-    pass
+    load_profile,
+    offset,
+):
 
+    # apply offset to the load profile
+    load_profile += offset
 
-def objective(x):
+    return load_profile
-    pass
 
 
 def get_load_profile(c, dt, batch_start_time, batch_process_duration):
@@ -54,19 +58,53 @@ 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
+    batch_process_duration_hours = batch_process_duration / 3600  # convert to hours
     timestamps = np.arange(start_time, end_time + 1, dt)
-    operating_hours_indices = index_operating_hours(
+    idx_operating_hours = index_operating_hours(
         timestamps, c["site_info"]["operating hours"]
     )
+    no_of_operating_hours = np.sum(idx_operating_hours > 0)
 
     # loop through each site in the configuration
     for site in c["site_info"]["sites"]:
+        # Initialise the load profile
+        load_profile = np.zeros(len(timestamps))
+
+        # generate noise to make the profile more realistic
+        noise = np.random.normal(
+            1 - c["noise"]["range"], 1 + c["noise"]["range"], len(timestamps)
+        )
         # Generate peak times and durations
         peak_times, peak_durations = generate_peak_info(c, dt)
 
         # Generate peak times and durations
-        peak_indices = index_peak_times(timestamps, peak_times, peak_durations)
+        idx_peak = 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)
+
+        # start by computing average consumption during operating hours
+        # and outside of operating hours
+        operating_hour_consumption = site["daily_consumption_kWh"] * (
+            1 - out_of_hours_ratio
+        )
+        out_of_hours_consumption = site["daily_consumption_kWh"] * out_of_hours_ratio
+
+        avg_operating_hour_consumption = (
+            operating_hour_consumption / no_of_operating_hours
+        )
+        avg_out_of_hours_consumption = out_of_hours_consumption / (
+            batch_process_duration_hours - no_of_operating_hours
+        )
+
+        # assign base load profile
+        load_profile[idx_operating_hours > 0] = avg_operating_hour_consumption
+        load_profile[idx_operating_hours == 0] = avg_out_of_hours_consumption
+
+        # apply peak loads
+        for i in range(1, np.max(idx_peak) + 1):
+            load_profile[idx_peak == i] = site["maximum_demand_kW"]
+
+        # apply noise to the load profile, including max demand
+        load_profile = load_profile * noise

YAMLs/config.yml

@@ -3,5 +3,8 @@ sim_time:
   batch_process_hours: 24 # compute load profile of 24 hours at a time
   duration_days: 60
 
+noise:
+  range: 0.15
+
 paths:
   site_info: YAMLs/site_info.yaml