def average_carbon_intensity(self, pred_time_dur=None):
if pred_time_dur is not None or not self.carbon_intensities:
ci = intensity.carbon_intensity(self.logger,
time_dur=pred_time_dur)
if not ci.success:
# Unable to retrieve forecasted CO2, fallback on current.
ci.carbon_intensity = self.carbon_intensities[
-1].carbon_intensity
else:
location = self.carbon_intensities[0].g_location.address
intensities = [
ci.carbon_intensity for ci in self.carbon_intensities
]
avg_ci = np.mean(intensities)
msg = (f"Average carbon intensity during training was {avg_ci:.2f}"
f" gCO2/kWh at detected location: {location}.")
self.logger.info(
"Carbon intensities (gCO2/kWh) fetched every "
f"{self.update_interval} s at detected location {location}: "
f"{intensities}")
ci = intensity.CarbonIntensity(carbon_intensity=avg_ci,
message=msg,
success=True)
self.logger.info(ci.message)
self.logger.output(ci.message, verbose_level=2)
return ci
def average_carbon_intensity(self):
if not self.carbon_intensities:
ci = intensity.carbon_intensity(self.logger)
self.carbon_intensities.append(ci)
# Ensure that we have some carbon intensities.
assert (self.carbon_intensities)
location = self.carbon_intensities[-1].address
intensities = [ci.carbon_intensity for ci in self.carbon_intensities]
avg_intensity = np.mean(intensities)
msg = (
f"Average carbon intensity during training was {avg_intensity:.2f}"
f" gCO2/kWh at detected location: {location}.")
avg_ci = intensity.CarbonIntensity(carbon_intensity=avg_intensity,
message=msg,
success=True)
self.logger.info(
"Carbon intensities (gCO2/kWh) fetched every "
f"{self.update_interval} s at detected location {location}: "
f"{intensities}")
self.logger.info(avg_ci.message)
self.logger.output(avg_ci.message, verbose_level=2)
return avg_ci
def _fetch_carbon_intensity(self):
ci = intensity.carbon_intensity(self.logger)
if ci.success and isinstance(
ci.carbon_intensity,
(int, float)) and not np.isnan(ci.carbon_intensity):
self.carbon_intensities.append(ci)
def predict_carbon_intensity(self, pred_time_dur):
ci = intensity.carbon_intensity(self.logger, time_dur=pred_time_dur)
weighted_intensities = [
ci.carbon_intensity for ci in self.carbon_intensities
] + [ci.carbon_intensity]
# Account for measured intensities by taking weighted average.
weight = math.floor(pred_time_dur / self.update_interval)
for _ in range(weight):
weighted_intensities.append(ci.carbon_intensity)
ci.carbon_intensity = np.mean(weighted_intensities)
intensity.set_carbon_intensity_message(ci, pred_time_dur)
self.logger.info(ci.message)
self.logger.output(ci.message, verbose_level=2)
return ci