mirror of
https://github.com/Akkudoktor-EOS/EOS.git
synced 2025-04-19 08:55:15 +00:00
136 lines
4.9 KiB
Python
136 lines
4.9 KiB
Python
from flask import Flask, jsonify, request
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import numpy as np
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from modules.class_load import *
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from modules.class_ems import *
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from modules.class_pv_forecast import *
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from modules.class_akku import *
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from modules.class_strompreis import *
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from modules.class_heatpump import *
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from modules.class_load_container import *
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from modules.class_sommerzeit import *
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from modules.class_soc_calc import *
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from modules.visualize import *
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from modules.class_battery_soc_predictor import *
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import os
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from flask import Flask, send_from_directory
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from pprint import pprint
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import matplotlib
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matplotlib.use('Agg') # Setzt das Backend auf Agg
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import matplotlib.pyplot as plt
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import string
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from datetime import datetime
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from deap import base, creator, tools, algorithms
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from modules.class_optimize import *
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import numpy as np
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import random
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import os
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from config import *
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app = Flask(__name__)
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opt_class = optimization_problem(prediction_hours=48, strafe=10)
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@app.route('/soc', methods=['GET'])
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def flask_soc():
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# MariaDB Verbindungsdetails
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config = db_config
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# Parameter festlegen
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voltage_high_threshold = 55.4 # 100% SoC
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voltage_low_threshold = 46.5 # 0% SoC
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current_low_threshold = 2 # Niedriger Strom für beide Zustände
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gap = 30 # Zeitlücke in Minuten zum Gruppieren von Maxima/Minima
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bat_capacity = 33 * 1000 / 48
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# Zeitpunkt X definieren
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zeitpunkt_x = (datetime.now() - timedelta(weeks=3)).strftime('%Y-%m-%d %H:%M:%S')
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# BatteryDataProcessor instanziieren und verwenden
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processor = BatteryDataProcessor(config, voltage_high_threshold, voltage_low_threshold, current_low_threshold, gap,bat_capacity)
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processor.connect_db()
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processor.fetch_data(zeitpunkt_x)
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processor.process_data()
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last_points_100_df, last_points_0_df = processor.find_soc_points()
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soc_df, integration_results = processor.calculate_resetting_soc(last_points_100_df, last_points_0_df)
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#soh_df = processor.calculate_soh(integration_results)
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processor.update_database_with_soc(soc_df)
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#processor.plot_data(last_points_100_df, last_points_0_df, soc_df)
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processor.disconnect_db()
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return jsonify("Done")
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@app.route('/strompreis', methods=['GET'])
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def flask_strompreis():
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date_now,date = get_start_enddate(prediction_hours,startdate=datetime.now().date())
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filepath = os.path.join (r'test_data', r'strompreise_akkudokAPI.json') # Pfad zur JSON-Datei anpassen
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#price_forecast = HourlyElectricityPriceForecast(source=filepath)
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price_forecast = HourlyElectricityPriceForecast(source="https://api.akkudoktor.net/prices?start="+date_now+"&end="+date+"", prediction_hours=prediction_hours)
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specific_date_prices = price_forecast.get_price_for_daterange(date_now,date)
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#print(specific_date_prices)
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return jsonify(specific_date_prices.tolist())
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@app.route('/gesamtlast', methods=['GET'])
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def flask_gesamtlast():
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if request.method == 'GET':
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year_energy = float(request.args.get("year_energy"))
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date_now,date = get_start_enddate(prediction_hours,startdate=datetime.now().date())
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###############
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# Load Forecast
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###############
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lf = LoadForecast(filepath=r'load_profiles.npz', year_energy=year_energy)
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#leistung_haushalt = lf.get_daily_stats(date)[0,...] # Datum anpassen
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leistung_haushalt = lf.get_stats_for_date_range(date_now,date)[0] # Nur Erwartungswert!
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gesamtlast = Gesamtlast(prediction_hours=prediction_hours)
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gesamtlast.hinzufuegen("Haushalt", leistung_haushalt)
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# ###############
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# # WP
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# ##############
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# leistung_wp = wp.simulate_24h(temperature_forecast)
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# gesamtlast.hinzufuegen("Heatpump", leistung_wp)
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last = gesamtlast.gesamtlast_berechnen()
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print(last)
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#print(specific_date_prices)
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return jsonify(last.tolist())
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@app.route('/optimize', methods=['POST'])
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def flask_optimize():
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if request.method == 'POST':
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parameter = request.json
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# Erforderliche Parameter prüfen
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erforderliche_parameter = [ 'strompreis_euro_pro_wh', "gesamtlast",'pv_akku_cap', "einspeiseverguetung_euro_pro_wh", 'pv_forecast_url', 'eauto_min_soc', "eauto_cap","eauto_charge_efficiency","eauto_charge_power","eauto_soc","pv_soc","start_solution","pvpowernow","haushaltsgeraet_dauer","haushaltsgeraet_wh"]
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for p in erforderliche_parameter:
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if p not in parameter:
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return jsonify({"error": f"Fehlender Parameter: {p}"}), 400
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# Simulation durchführen
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ergebnis = opt_class.optimierung_ems(parameter=parameter, start_hour=datetime.now().hour) # , startdate = datetime.now().date() - timedelta(days = 1)
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return jsonify(ergebnis)
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@app.route('/visualisierungsergebnisse.pdf')
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def get_pdf():
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return send_from_directory('', 'visualisierungsergebnisse.pdf')
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if __name__ == '__main__':
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app.run(debug=True, host="0.0.0.0")
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