Mirrors/EOS
1
0
Fork 0
mirror of https://github.com/Akkudoktor-EOS/EOS.git synced 2025-07-01 10:06:52 +00:00
Code Issues Actions 4 Packages Projects Releases Wiki Activity

Neues Lastprognose Modell mit Korrektur

Browse Source
This commit is contained in:
Bla Bla 2024-08-01 13:47:46 +02:00
parent 24c47020fa
commit afeb80ca08
1 changed files with 98 additions and 6 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

104
flask_server.py
View File

@ -11,6 +11,7 @@ from modules.class_sommerzeit import *
from modules.class_soc_calc import *
from modules.visualize import *
from modules.class_battery_soc_predictor import *
from modules.class_load_corrector import *
import os
from flask import Flask, send_from_directory
from pprint import pprint
@ -18,7 +19,7 @@ import matplotlib
matplotlib.use('Agg') # Setzt das Backend auf Agg
import matplotlib.pyplot as plt
import string
from datetime import datetime
from datetime import datetime, timedelta
from deap import base, creator, tools, algorithms
from modules.class_optimize import *
import numpy as np
@ -32,6 +33,32 @@ opt_class = optimization_problem(prediction_hours=48, strafe=10)
@app.route('/last_correction', methods=['GET'])
def flask_last_correction():
if request.method == 'GET':
year_energy = float(request.args.get("year_energy"))
date_now,date = get_start_enddate(prediction_hours,startdate=datetime.now().date())
###############
# Load Forecast
###############
lf = LoadForecast(filepath=r'load_profiles.npz', year_energy=year_energy)
#leistung_haushalt = lf.get_daily_stats(date)[0,...] # Datum anpassen
leistung_haushalt = lf.get_stats_for_date_range(date_now,date)[0] # Nur Erwartungswert!
gesamtlast = Gesamtlast(prediction_hours=prediction_hours)
gesamtlast.hinzufuegen("Haushalt", leistung_haushalt)
# ###############
# # WP
# ##############
# leistung_wp = wp.simulate_24h(temperature_forecast)
# gesamtlast.hinzufuegen("Heatpump", leistung_wp)
last = gesamtlast.gesamtlast_berechnen()
print(last)
#print(specific_date_prices)
return jsonify(last.tolist())
@app.route('/soc', methods=['GET'])
def flask_soc():
@ -65,6 +92,9 @@ def flask_soc():
return jsonify("Done")
@app.route('/strompreis', methods=['GET'])
def flask_strompreis():
date_now,date = get_start_enddate(prediction_hours,startdate=datetime.now().date())
@ -80,14 +110,50 @@ def flask_strompreis():
def flask_gesamtlast():
if request.method == 'GET':
year_energy = float(request.args.get("year_energy"))
date_now,date = get_start_enddate(prediction_hours,startdate=datetime.now().date())
prediction_hours = int(request.args.get("hours", 48)) # Default to 24 hours if not specified
date_now = datetime.now()
end_date = (date_now + timedelta(hours=prediction_hours)).strftime('%Y-%m-%d %H:%M:%S')
###############
# Load Forecast
###############
# Instantiate LastEstimator and get measured data
estimator = LastEstimator()
start_date = (date_now - timedelta(days=60)).strftime('%Y-%m-%d') # Example: last 60 days
end_date = date_now.strftime('%Y-%m-%d') # Current date
last_df = estimator.get_last(start_date, end_date)
selected_columns = last_df[['timestamp', 'Last']]
selected_columns['time'] = pd.to_datetime(selected_columns['timestamp']).dt.floor('H')
selected_columns['Last'] = pd.to_numeric(selected_columns['Last'], errors='coerce')
cleaned_data = selected_columns.dropna()
# Instantiate LoadForecast
lf = LoadForecast(filepath=r'load_profiles.npz', year_energy=year_energy)
#leistung_haushalt = lf.get_daily_stats(date)[0,...] # Datum anpassen
leistung_haushalt = lf.get_stats_for_date_range(date_now,date)[0] # Nur Erwartungswert!
# Generate forecast data
forecast_list = []
for single_date in pd.date_range(cleaned_data['time'].min().date(), cleaned_data['time'].max().date()):
date_str = single_date.strftime('%Y-%m-%d')
daily_forecast = lf.get_daily_stats(date_str)
mean_values = daily_forecast[0]
hours = [single_date + pd.Timedelta(hours=i) for i in range(24)]
daily_forecast_df = pd.DataFrame({'time': hours, 'Last Pred': mean_values})
forecast_list.append(daily_forecast_df)
forecast_df = pd.concat(forecast_list, ignore_index=True)
# Create LoadPredictionAdjuster instance
adjuster = LoadPredictionAdjuster(cleaned_data, forecast_df, lf)
adjuster.calculate_weighted_mean()
adjuster.adjust_predictions()
# Predict the next hours
future_predictions = adjuster.predict_next_hours(prediction_hours)
leistung_haushalt = future_predictions['Adjusted Pred'].values
gesamtlast = Gesamtlast(prediction_hours=prediction_hours)
gesamtlast.hinzufuegen("Haushalt", leistung_haushalt)
@ -99,9 +165,35 @@ def flask_gesamtlast():
last = gesamtlast.gesamtlast_berechnen()
print(last)
#print(specific_date_prices)
return jsonify(last.tolist())
# # @app.route('/gesamtlast', methods=['GET'])
# # def flask_gesamtlast():
# # if request.method == 'GET':
# # year_energy = float(request.args.get("year_energy"))
# # date_now,date = get_start_enddate(prediction_hours,startdate=datetime.now().date())
# # ###############
# # # Load Forecast
# # ###############
# # lf = LoadForecast(filepath=r'load_profiles.npz', year_energy=year_energy)
# # #leistung_haushalt = lf.get_daily_stats(date)[0,...] # Datum anpassen
# # leistung_haushalt = lf.get_stats_for_date_range(date_now,date)[0] # Nur Erwartungswert!
# # gesamtlast = Gesamtlast(prediction_hours=prediction_hours)
# # gesamtlast.hinzufuegen("Haushalt", leistung_haushalt)
# # # ###############
# # # # WP
# # # ##############
# # # leistung_wp = wp.simulate_24h(temperature_forecast)
# # # gesamtlast.hinzufuegen("Heatpump", leistung_wp)
# # last = gesamtlast.gesamtlast_berechnen()
# # print(last)
# # #print(specific_date_prices)
# # return jsonify(last.tolist())
@app.route('/pvforecast', methods=['GET'])
def flask_pvprognose():
if request.method == 'GET':