import numpy as np import pandas as pd import matplotlib.pyplot as plt #Time setup hours = np.arange(24) time_labels = [f"{h}:00" for h in hours] #Simulated hourly electricity prices (€/kWh) #Real hourly electricity prices for April 28, 2025 (in €/kWh) # Real hourly electricity prices for April 28, 2025 (in €/kWh) prices = [ 0.0750, 0.0385, 0.0221, 0.0125, 0.0150, 0.0158, 0.0372, 0.0560, 0.1704, 0.2382, 0.1791, 0.0888, 0.0436, 0.0377, 0.0283, 0.0331, 0.0450, 0.0687, 0.0888, 0.1314, 0.1106, 0.0699, 0.0601, 0.0319 ] #Power ratings (kW) water_heater_power = 2.0 # Runs 3 hours/day dishwasher_power = 1.5 # Runs 1 hour/day #Run appliances in lowest-price hours sorted_indices = np.argsort(prices) #Pick cheapest 3 hours for water heater, 1 for dishwasher water_heater_hours = sorted_indices[:3] dishwasher_hour = sorted_indices[3] #Create appliance schedule schedule = pd.DataFrame({ "Hour": hours, "Electricity Price (€/kWh)": prices, "Water Heater (kW)": [water_heater_power if h in water_heater_hours else 0 for h in hours], "Dishwasher (kW)": [dishwasher_power if h == dishwasher_hour else 0 for h in hours], }) #Energy use and cost schedule["Total Load (kW)"] = schedule["Water Heater (kW)"] + schedule["Dishwasher (kW)"] schedule["Cost (€)"] = schedule["Total Load (kW)"] * schedule["Electricity Price (€/kWh)"] #Total cost total_cost = schedule["Cost (€)"].sum() #Results print(f"Total cost with optimized schedule: €{total_cost:.2f}") display(schedule) plt.figure(figsize=(12,5)) plt.plot(hours, prices, label="Electricity Price", marker='o') plt.bar(hours, schedule["Total Load (kW)"], alpha=0.5, label="Appliance Load") plt.title("Optimized Appliance Schedule") plt.xlabel("Hour") plt.ylabel("kW / €/kWh") plt.xticks(hours) plt.legend() plt.grid(True) plt.tight_layout() plt.show()