vis

v2/architecture.py

@@ -10,7 +10,7 @@ from supplier import Supplier, precalculate_supplier
 from data_processing import read_datasets, add_production_field, interpolate_and_join, SolarParameters
 from bess import BatteryModel
 from evolution_strategies import evolution_strategies_optimizer
-
+from visualization import plotly_visualize_simulation, plotly_visualize_monthly
 
 DO_VIS = False
 
@@ -211,8 +211,8 @@ def simulator(battery_model, prod_cons, decider):
 
 
 def optimizer(decider_class, battery_model, all_data_with_predictions, precalculated_supplier):
-    number_of_generations = 3
-    population_size = 100
+    number_of_generations = 1
+    population_size = 10
     collected_loss_values = []
     def objective_function(params):
         print("Simulating with parameters", params)
@@ -285,7 +285,7 @@ def main():
     time_interval_h = time_interval_min / 60
 
     # for faster testing:
-    DATASET_TRUNCATED_SIZE = 10000
+    DATASET_TRUNCATED_SIZE = None
     if DATASET_TRUNCATED_SIZE is not None:
         print("Truncating dataset to", DATASET_TRUNCATED_SIZE, "datapoints, that is", DATASET_TRUNCATED_SIZE * time_interval_h / 24, "days")
         all_data = all_data.iloc[:DATASET_TRUNCATED_SIZE]
@@ -326,5 +326,13 @@ def main():
     decider = decider_class(best_params, precalculated_supplier)
     results, total_network_fee = simulator(battery_model, all_data_with_predictions, decider)
 
+    date_range = ("2021-01-01", "2021-02-01")
+    date_range = ("2021-07-01", "2021-08-01")
+    plotly_fig = plotly_visualize_simulation(results, date_range=date_range)
+    plotly_fig.show()
+
+    plotly_fig_2 = plotly_visualize_monthly(results)
+    plotly_fig_2.show()
+
 if __name__ == '__main__':
     main()

v2/visualization.py

@@ -0,0 +1,158 @@
+import plotly
+import plotly.subplots
+import plotly.express as px
+import plotly.graph_objects as go
+
+import matplotlib.pyplot as plt
+
+import data_processing
+
+def visualize_simulation(results, date_range):
+    start_date, end_date = date_range
+
+    fig = plt.figure()
+    results = results.loc[start_date: end_date]
+
+    x = results.index
+    y = [results.consumption_from_solar, results.consumption_from_network, results.consumption_from_bess]
+    plt.plot(x, y[0], label='Demand served by solar', color='yellow', linewidth=0.5)
+    plt.plot(x, y[0]+y[1], label='Demand served by network', color='blue', linewidth=0.5)
+    plt.plot(x, y[0]+y[1]+y[2], label='Demand served by BESS', color='green', linewidth=0.5)
+    plt.fill_between(x, y[0]+y[1]+y[2], 0, color='green')
+    plt.fill_between(x, y[0]+y[1], 0, color='blue')
+    plt.fill_between(x, y[0], 0, color='yellow')
+
+    # plt.xlim(datetime.datetime.fromisoformat(start_date), datetime.datetime.fromisoformat(end_date))
+
+    plt.legend()
+    return fig
+
+
+MARGIN = dict(
+    l=0,
+    r=0,
+    b=0,
+    t=0,
+    pad=0
+)
+
+
+def plotly_visualize_simulation(results, date_range):
+    start_date, end_date = date_range
+    results = results.loc[start_date: end_date]
+    '''
+    fig = px.area(results, x=results.index, y="consumption_from_network")
+    return fig'''
+
+    fig = plotly.subplots.make_subplots(specs=[[{"secondary_y": True}]])
+    fig.update_layout(yaxis2=dict(range=[0.0, 110]))
+
+    fig.add_trace(go.Scatter(
+        x=results.index, y=results['consumption_from_network'],
+        hoverinfo='x+y',
+        mode='lines',
+        line=dict(width=0.5, color='blue'),
+        name='Network',
+        stackgroup='one' # define stack group
+    ))
+    fig.add_trace(go.Scatter(
+        x=results.index, y=results['consumption_from_solar'],
+        hoverinfo='x+y',
+        mode='lines',
+        line=dict(width=0.5, color='orange'),
+        name='Solar',
+        stackgroup='one'
+    ))
+    fig.add_trace(go.Scatter(
+        x=results.index, y=results['consumption_from_bess'],
+        hoverinfo='x+y',
+        mode='lines',
+        line=dict(width=0.5, color='green'),
+        name='BESS',
+        stackgroup='one'
+    ))
+    fig.add_trace(go.Scatter(
+        x=results.index, y=results['soc_series'] * 100,
+        hoverinfo='x+y',
+        mode='lines',
+        line=dict(width=1.5, color='red'),
+        name='State of charge'),
+        secondary_y=True
+    )
+    # could not kill the huge padding this introduces:
+    # fig.update_layout(title=f"Simulation for {start_date} - {end_date}")
+    fig.update_layout(height=400, yaxis_title="Consumption [kW]", yaxis2_title="State of charge [%]", yaxis2_showgrid=False)
+
+    return fig
+
+
+def plotly_visualize_monthly(result):
+    consumption = data_processing.monthly_analysis(result)
+    # months = monthly_results.index
+    months = list(range(1, 13))
+    fig = go.Figure()
+    fig.add_trace(go.Scatter(
+        x=months, y=consumption[:, 0], # monthly_results['consumption_from_network'],
+        hoverinfo='x+y',
+        mode='lines',
+        line=dict(width=0.5, color='blue'),
+        name='Network',
+        stackgroup='one' # define stack group
+    ))
+    fig.add_trace(go.Scatter(
+        x=months, y=consumption[:, 1], # y=monthly_results['consumption_from_solar'],
+        hoverinfo='x+y',
+        mode='lines',
+        line=dict(width=0.5, color='orange'),
+        name='Solar',
+        stackgroup='one'
+    ))
+    fig.add_trace(go.Scatter(
+        x=months, y=consumption[:, 2], # y=monthly_results['consumption_from_bess'],
+        hoverinfo='x+y',
+        mode='lines',
+        line=dict(width=0.5, color='green'),
+        name='BESS',
+        stackgroup='one'
+    ))
+    fig.update_layout(
+        yaxis_title="Monthly consumption in [MWh]",
+        height=400
+    )
+    return fig
+
+
+def monthly_visualization(consumptions_in_mwh):
+    percentages = consumptions_in_mwh[:, :3] / consumptions_in_mwh.sum(axis=1, keepdims=True) * 100
+    bats = 0
+    nws = 0
+    sols = 0
+
+    print("[Mwh]")
+    print("==========================")
+    print("month\tnetwork\tsolar\tbess")
+    for month_minus_1 in range(12):
+        network, solar, bess = consumptions_in_mwh[month_minus_1]
+        print(f"{month_minus_1+1}\t{network:0.2f}\t{solar:0.2f}\t{bess:0.2f}")
+        bats += bess
+        nws += network
+        sols += solar
+    print(f"\t{nws:0.2f}\t{sols:0.2f}\t{bats:0.2f}")
+
+
+    fig, ax = plt.subplots()
+
+    ax.stackplot(range(1, 13),
+                  percentages[:, 0], percentages[:, 1], percentages[:, 2],
+                  labels=["hálózat", "egyenesen a naptól", "a naptól a BESS-en keresztül"])
+    ax.set_ylim(0, 100)
+    ax.legend()
+    plt.title('A fogyasztás hány százalékát fedezte az adott hónapban?')
+    plt.show()
+
+    plt.stackplot(range(1, 13),
+                  consumptions_in_mwh[:, 0], consumptions_in_mwh[:, 1], consumptions_in_mwh[:, 2],
+                  labels=["hálózat", "egyenesen a naptól", "a naptól a BESS-en keresztül"])
+    plt.legend()
+    plt.title('Mennyi fogyasztást fedezett az adott hónapban? [MWh]')
+    plt.show()