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Changes minutes to hours on forecast horizons tables/plots #177

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Changes minutes to hours on forecast horizons tables/plots #177

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2df0e08
#167 Issue: solved
Aug 4, 2024
b671b60
Update: main.py
Arunesh-Tiwari Aug 18, 2024
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20 changes: 17 additions & 3 deletions src/main.py
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Original file line number Diff line number Diff line change
Expand Up @@ -133,14 +133,28 @@ def metric_page():
make_recent_summary_stats(values=y_mae)
make_recent_summary_stats(values=y_rmse, title="Recent RMSE")

def convert_minutes_to_hours(minutes):
return round(minutes / 60, 1)
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we should probably only define this once

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perhaps import from src/plots/forecast_horizon


# Generate forecast horizons in minutes
forecast_horizons_in_minutes = list(range(0, 480, 30)) + list(range(480, 36 * 60, 180))

# Convert them to hours
forecast_horizons_in_hours = [convert_minutes_to_hours(minutes) for minutes in forecast_horizons_in_minutes]

# Default selection also converted to hours
default_selection_in_hours = [convert_minutes_to_hours(minutes) for minutes in [60, 120, 240, 420]]

st.sidebar.subheader("Select Forecast Horizon")
forecast_horizon_selection = st.sidebar.multiselect(
"Select",
# 0-8 hours in 30 mintue chunks, 8-36 hours in 3 hour chunks
list(range(0, 480, 30)) + list(range(480, 36 * 60, 180)),
[60, 120, 240, 420],
forecast_horizons_in_hours,
default_selection_in_hours,
)

# Convert selected forecast horizons back to minutes for further processing
forecast_horizon_selection_in_minutes = [int(hours * 60) for hours in forecast_horizon_selection]

df_mae = pd.DataFrame(
{
"MAE": y_mae,
Expand Down
25 changes: 17 additions & 8 deletions src/plots/forecast_horizon.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,6 +3,9 @@

from plots.utils import line_color, get_x_y, MAE_LIMIT_DEFAULT

def convert_minutes_to_hours(minutes):
return round(minutes / 60, 1)


def make_mae_by_forecast_horizon(
df_mae, forecast_horizon_selection, metric_values_by_forecast_horizon
Expand Down Expand Up @@ -39,12 +42,14 @@ def make_mae_by_forecast_horizon(
}
)

forecast_horizon_hours = convert_minutes_to_hours(forecast_horizon)

fig2.add_traces(
[
go.Scatter(
x=df["datetime_utc"],
y=df["MAE"],
name=f"{forecast_horizon}-minute horizon",
name=f"{forecast_horizon_hours}-hour horizon",
mode="lines",
line=dict(color=line_color[i%len(line_color)]),
)
Expand All @@ -64,19 +69,21 @@ def make_mae_forecast_horizon_group_by_forecast_horizon(
text="Quartz Solar MAE by Forecast Horizon for Date Range(selected in sidebar)"
),
xaxis=go.layout.XAxis(title=go.layout.xaxis.Title(text="MAE (MW)")),
yaxis=go.layout.YAxis(title=go.layout.yaxis.Title(text="Forecast Horizon (minutes)")),
yaxis=go.layout.YAxis(title=go.layout.yaxis.Title(text="Forecast Horizon (hours)")),
)
)
for i, forecast_horizon in enumerate(forecast_horizon_selection):
metric_values = metric_values_by_forecast_horizon[forecast_horizon]
x_mae_horizon = [value.datetime_interval.start_datetime_utc for value in metric_values]
y_mae_horizon = [round(float(value.value), 2) for value in metric_values]

forecast_horizon_hours = convert_minutes_to_hours(forecast_horizon)

df_mae_horizon = pd.DataFrame(
{
"MAE": y_mae_horizon,
"datetime_utc": x_mae_horizon,
"forecast_horizon": forecast_horizon,
"forecast_horizon": forecast_horizon_hours,
}
)

Expand All @@ -85,20 +92,21 @@ def make_mae_forecast_horizon_group_by_forecast_horizon(
go.Scatter(
x=df_mae_horizon["MAE"],
y=df_mae_horizon["forecast_horizon"],
name=f"{forecast_horizon}-minute horizon",
name=f"{forecast_horizon_hours}-hour horizon",
mode="markers",
line=dict(color=line_color[i%len(line_color)]),
),
]
)
fig.update_layout(
xaxis=dict(tickmode="linear", tick0=0, dtick=50),
yaxis=dict(tickmode="linear", tick0=0, dtick=60),
yaxis=dict(tickmode="linear", tick0=0, dtick=1),
)
fig.update_layout(xaxis_range=[0, MAE_LIMIT_DEFAULT])
return fig



def make_mae_vs_forecast_horizon_group_by_date(
forecast_horizon_selection, metric_values_by_forecast_horizon
):
Expand All @@ -109,7 +117,7 @@ def make_mae_vs_forecast_horizon_group_by_date(
fig = go.Figure(
layout=go.Layout(
title=go.layout.Title(text="Quartz Solar MAE Forecast Horizon Values by Date"),
xaxis=go.layout.XAxis(title=go.layout.xaxis.Title(text="Forecast Horizon (minutes)")),
xaxis=go.layout.XAxis(title=go.layout.xaxis.Title(text="Forecast Horizon (hours)")),
yaxis=go.layout.YAxis(title=go.layout.yaxis.Title(text="MAE (MW)")),
legend=go.layout.Legend(title=go.layout.legend.Title(text="Date")),
)
Expand All @@ -124,7 +132,7 @@ def make_mae_vs_forecast_horizon_group_by_date(
metric_values = metric_values_by_forecast_horizon[forecast_horizon]
dates = [value.datetime_interval.start_datetime_utc for value in metric_values]
mae_value = [round(float(value.value), 2) for value in metric_values]
forecast_horizons = [value.forecast_horizon_minutes for value in metric_values]
forecast_horizons = [convert_minutes_to_hours(value.forecast_horizon_minutes) for value in metric_values]

# create dataframe for each date with a value for each forecast horizon
data = pd.DataFrame(
Expand Down Expand Up @@ -160,8 +168,9 @@ def make_mae_vs_forecast_horizon_group_by_date(
)
fig.add_traces(traces)
fig.update_layout(
xaxis=dict(tickmode="linear", tick0=0, dtick=60),
xaxis=dict(tickmode="linear", tick0=0, dtick=1),
yaxis=dict(tickmode="linear", tick0=0, dtick=50),
)
fig.update_layout(yaxis_range=[0, MAE_LIMIT_DEFAULT])
return all_forecast_horizons_df, fig

4 changes: 4 additions & 0 deletions src/tables/summary.py
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Original file line number Diff line number Diff line change
Expand Up @@ -42,6 +42,8 @@ def get_recent_daily_values(values):

return day_before_yesterday, yesterday, today

def convert_minutes_to_hours(minutes):
return round(minutes / 60, 1)

def make_forecast_horizon_table(all_forecast_horizons_df, y_plive_mae):
st.subheader("Data - forecast horizon averaged")
Expand All @@ -52,6 +54,8 @@ def make_forecast_horizon_table(all_forecast_horizons_df, y_plive_mae):
df_mae_horizon_mean.rename(columns={"MAE": "mean"}, inplace=True)
df_mae_horizon_std = all_forecast_horizons_df.groupby(["forecast_horizon"]).std().reset_index()
df_mae_horizon_mean["std"] = df_mae_horizon_std["MAE"]
# Convert forecast_horizon from minutes to hours
df_mae_horizon_mean["forecast_horizon"] = df_mae_horizon_mean["forecast_horizon"].apply(convert_minutes_to_hours)
pv_live_mae = np.round(np.mean(y_plive_mae), 2)
st.write(f"PV LIVE Mae {pv_live_mae} MW (intraday - day after)")
st.write(df_mae_horizon_mean)