AI Building Energy Consumption Optimization System

Development of an AI system for optimizing building energy consumption

Commercial real estate consumes 40% of the world's electricity. An average-sized building (10,000 m²) consumes 500–2000 MWh/year. Intelligent building management (Building Energy Management System) reduces consumption by 20–35% without sacrificing comfort.

Building models

Physics-informed building model:

The building is described by a thermal RC scheme: - R (thermal resistance): thermal insulation of walls, windows - C (thermal capacity): thermal mass of structures - Q_internal: heat emission of people, lighting, equipment - Q_solar: solar influx through windows

ML-parameter identification: based on historical temperature and consumption data → LightGBM identifies the thermal inertia of a specific building (1–4 hours typical).

import numpy as np
from scipy.integrate import odeint
from scipy.optimize import minimize
import pandas as pd

class BuildingThermalModel:
    """Упрощённая RC-модель теплодинамики здания"""

    def __init__(self, thermal_resistance=0.5, thermal_capacity=3e7):
        self.R = thermal_resistance   # °C/Вт
        self.C = thermal_capacity     # Дж/°C

    def simulate(self, T_init, t_hours, T_outdoor, Q_hvac, Q_internal, Q_solar):
        """
        Симуляция температуры в здании.
        Q_hvac: мощность HVAC [Вт], положительное = нагрев
        Q_internal: внутренние тепловыделения [Вт]
        Q_solar: солнечный приток [Вт]
        """
        def dT_dt(T, t):
            t_idx = min(int(t * 60), len(T_outdoor)-1)  # индекс по минутам
            Q_loss = (T_outdoor[t_idx] - T[0]) / self.R
            return [(Q_hvac[t_idx] + Q_internal[t_idx] + Q_solar[t_idx] + Q_loss) / self.C]

        t_sec = np.arange(0, t_hours * 3600, 60)  # каждую минуту
        T_sim = odeint(dT_dt, [T_init], t_sec)
        return T_sim.flatten()

    def calibrate(self, historical_temps, historical_inputs):
        """Подбор R и C по историческим данным (inverse problem)"""
        def residuals(params):
            self.R, self.C = params
            T_sim = self.simulate(**historical_inputs)
            return np.mean((T_sim - historical_temps)**2)

        result = minimize(residuals, x0=[0.5, 3e7], method='Nelder-Mead')
        self.R, self.C = result.x

Model Predictive Control for HVAC

A building's thermal inertia is an opportunity for optimization. If pre-cooling is implemented during low-cost hours, the HVAC will operate less during peak (high-cost) hours:

from scipy.optimize import minimize

def mpc_hvac_controller(
    building_model,
    current_temp,
    setpoint,           # целевая температура [°C]
    outdoor_forecast,   # прогноз уличной температуры на 24ч
    electricity_tariff, # тариф электроэнергии по часам [руб/кВтч]
    comfort_band=1.5    # допустимое отклонение от уставки [°C]
):
    N = 24  # горизонт 24 часа
    max_power = 500000  # Вт максимальная мощность HVAC

    def cost_function(Q_hvac_schedule):
        # Симулировать температуру при данном расписании мощностей
        T_sim = building_model.simulate(
            T_init=current_temp,
            t_hours=N,
            T_outdoor=outdoor_forecast,
            Q_hvac=Q_hvac_schedule,
            Q_internal=np.full(N*60, 50000),   # типичное тепловыделение
            Q_solar=np.zeros(N*60)              # ночью
        )
        # Энергетическая стоимость
        energy_cost = sum(
            Q_hvac_schedule[h] / 1000 * electricity_tariff[h]  # кВтч × тариф
            for h in range(N)
        )
        # Штраф за выход из зоны комфорта
        T_hourly = T_sim[::60][:N]
        comfort_violation = sum(max(0, abs(T_hourly[h] - setpoint) - comfort_band)**2
                               for h in range(N))

        return energy_cost + 10000 * comfort_violation  # взвешенная сумма

    Q0 = np.full(N, max_power * 0.3)  # начальное приближение
    bounds = [(0, max_power)] * N
    result = minimize(cost_function, Q0, method='SLSQP', bounds=bounds)
    return result.x[0]  # мощность на следующий час

Building subsystems

Lighting:

A combination of motion sensors + lux meters + ML-schedule: - Presence in the room (PIR sensors) → automatic switch-off after 10 minutes - Daylight harvesting: measure natural light → add exactly the required amount - ML predicts the occupancy of meeting rooms according to the calendar → pre-heat + pre-light before the start

Lighting savings: 40–60% of base consumption in office buildings.

Elevators:

Elevator Group Control algorithm: - Prediction of traffic patterns (down in the morning, up in the evening, chaotic lunchtime) - Predetermined positioning of cabins in anticipation of the peak - Reduction of average waiting time by 20–35%

Integration and monitoring

BAS (Building Automation System) integration:

• BACnet/IP, Modbus, KNX — standard building system protocols - Digital twin of the building: visualization in BIM (Autodesk Revit, Bentley Buildings) - Carbon footprint: automatic generation of Energy Performance Certificate

Consumption anomalies:

Isolation Forest monitors hourly consumption by system → alerts for abnormal consumption. Typical causes: an air conditioner left behind on a weekend, a coolant leak, or a faulty heating element.

Development time: 3-5 months for BMS AI extension with HVAC MPC optimization and consumption analytics.