Building Energy Management and HVAC Optimization

Module: Sustainable Tech | Difficulty: Premium

Building Energy Balance

where is the overall heat transfer coefficient, is the surface area, and is the temperature difference.

Comparison

| System | COP | EER | Annual Cost ($/m2) | |--------|-----|-----|-------------------| | Standard AC | 3.0-3.5 | 10-12 | 15-25 | | High-efficiency AC | 4.5-5.5 | 15-18 | 8-15 | | Ground-source HP | 4.0-5.0 | 14-17 | 5-12 | | VRF system | 3.5-4.5 | 12-15 | 10-18 |

Python Implementation

import numpy as np
from scipy.optimize import minimize

class BuildingEnergyOptimizer:
    def __init__(self, floor_area, envelope_u_value):
        self.area = floor_area
        self.u_value = envelope_u_value

    def heat_loss(self, T_in, T_out):
        return self.u_value * self.area * (T_in - T_out)

    def optimize_schedule(self, outdoor_temps, electricity_prices, comfort_bounds=(18, 26)):
        n_hours = len(outdoor_temps)
        schedule = np.zeros(n_hours)
        for h in range(n_hours):
            if electricity_prices[h] < np.percentile(electricity_prices, 30):
                schedule[h] = comfort_bounds[0]
            elif electricity_prices[h] > np.percentile(electricity_prices, 70):
                schedule[h] = comfort_bounds[1]
            else:
                schedule[h] = 21.0
        return schedule

Research Insight: MPC combined with RL can reduce building energy consumption by 20-40% compared to rule-based thermostats.