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TwoDayPriceClassification.py
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TwoDayPriceClassification.py
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import numpy as np
from datetime import datetime, time, timedelta
from appdaemon.plugins.hass.hassapi import Hass
STATE_UNKNOWN = 'unknown'
class TwoDayPriceClassification(Hass):
def initialize(self):
# Schedule the update method to run every hour
self.run_hourly(self.update, time(minute=0, second=0))
# Call the update method at the start
self.update({})
from datetime import datetime, timedelta
def binned_classification(self, today_prices, tomorrow_prices=None):
# Clear the state of the sensor
self.set_state('sensor.Electricity_TwoDay_classification', state=STATE_UNKNOWN, attributes={})
# Convert single float value to a list
if isinstance(today_prices, float):
today_prices = [today_prices]
# If tomorrow's prices are available, concatenate today's and tomorrow's prices
if tomorrow_prices is not None:
prices = np.concatenate((today_prices, tomorrow_prices))
# If tomorrow's prices are not available, use today's prices
else:
prices = np.array(today_prices)
# Round down to two decimal places
prices = np.floor(prices * 100) / 100
# Divide the prices into 7 bins based on the range of the prices
bins = np.linspace(min(prices), max(prices) + 0.01, 8) # Add a small buffer to the maximum value
# Classify the prices based on which bin they fall into
classified_prices_binned_today = np.digitize(today_prices, bins) # Classes from 1 to 7
if tomorrow_prices is not None:
classified_prices_binned_tomorrow = np.digitize(tomorrow_prices, bins)
else:
classified_prices_binned_tomorrow = None
# Set the state of the sensor with timeslots as keys and class levels as values
if tomorrow_prices is not None:
date_str_today = datetime.now().strftime("%Y-%m-%d")
date_str_tomorrow = (datetime.now() + timedelta(days=1)).strftime("%Y-%m-%d")
else:
date_str_today = (datetime.now() - timedelta(days=1)).strftime("%Y-%m-%d")
date_str_tomorrow = datetime.now().strftime("%Y-%m-%d")
hourly_data_binned_today = {}
for hour, value in enumerate(classified_prices_binned_today):
time_slot = f"{date_str_today} {hour:02d}:00-{(hour+1)%24:02d}:00"
hourly_data_binned_today[time_slot] = f"Class {value}"
hourly_data_binned_tomorrow = {}
if classified_prices_binned_tomorrow is not None:
for hour, value in enumerate(classified_prices_binned_tomorrow):
time_slot = f"{date_str_tomorrow} {hour:02d}:00-{(hour+1)%24:02d}:00"
hourly_data_binned_tomorrow[time_slot] = f"Class {value}"
# Concatenate classifications for both days
if classified_prices_binned_tomorrow is not None:
attributes = {**hourly_data_binned_today, **hourly_data_binned_tomorrow}
else:
attributes = hourly_data_binned_today
current_hour_value = attributes.get(f"{date_str_today} {datetime.now().hour:02d}:00-{(datetime.now().hour+1)%24:02d}:00", STATE_UNKNOWN)
self.set_state('sensor.Electricity_TwoDay_classification', state=current_hour_value.strip(), attributes=attributes)
def update(self, kwargs):
# Fetch today's prices from your sensor
today_prices = self.get_state('sensor.nordpool_kwh_se4_sek_3_10_025', attribute='today')
# Check if the sensor data is valid
if today_prices == STATE_UNKNOWN:
self.log("Today's sensor data is not available")
return
# Convert the prices from string to float and replace 'unknown' with np.nan
if isinstance(today_prices, list):
today_prices = [float(price) if price != 'unknown' else np.nan for price in today_prices]
else:
today_prices = [float(price) if price != 'unknown' else np.nan for price in today_prices.split(',')]
# Fetch tomorrow's prices from your sensor
tomorrow_prices_partial = self.get_state('sensor.nordpool_kwh_se4_sek_3_10_025', attribute='tomorrow')
# Check if tomorrow's prices are available
if tomorrow_prices_partial is not None:
# Convert tomorrow's prices from string to float and replace 'unknown' with np.nan
if isinstance(tomorrow_prices_partial, list):
tomorrow_prices_partial = [float(price) if price != 'unknown' else np.nan for price in tomorrow_prices_partial]
else:
tomorrow_prices_partial = [float(price) if price != 'unknown' else np.nan for price in tomorrow_prices_partial.split(',')]
# Call the classification methods for both today and tomorrow if prices are available
self.binned_classification(today_prices, tomorrow_prices_partial)
else:
# Call the classification methods for today if tomorrow's prices are not available
self.binned_classification(today_prices)
def update(self, kwargs):
# Fetch today's prices from your sensor
today_prices = self.get_state('sensor.nordpool_kwh_se4_sek_3_10_025', attribute='today')
# Check if the sensor data is valid
if today_prices == STATE_UNKNOWN:
self.log("Today's sensor data is not available")
return
# Convert the prices from string to float and replace 'unknown' with np.nan
if isinstance(today_prices, list):
today_prices = [float(price) if price != 'unknown' else np.nan for price in today_prices]
else:
today_prices = [float(price) if price != 'unknown' else np.nan for price in today_prices.split(',')]
# Fetch tomorrow's prices from your sensor
tomorrow_prices_partial = self.get_state('sensor.nordpool_kwh_se4_sek_3_10_025', attribute='tomorrow')
# Check if tomorrow's prices are available
if tomorrow_prices_partial is not None:
# Convert tomorrow's prices from string to float and replace 'unknown' with np.nan
if isinstance(tomorrow_prices_partial, list):
tomorrow_prices_partial = [float(price) if price != 'unknown' else np.nan for price in tomorrow_prices_partial]
else:
tomorrow_prices_partial = [float(price) if price != 'unknown' else np.nan for price in tomorrow_prices_partial.split(',')]
# Call the classification methods for both today and tomorrow if prices are available
self.binned_classification(today_prices, tomorrow_prices_partial)
else:
# Call the classification methods for today if tomorrow's prices are not available
self.binned_classification(today_prices)