This work addresses a real-world building facility control problem by using evolutionary algorithms. The variables are facility control parameters, such as the start/stop time of air-conditioning, lighting, and ventilation operation, etc. The problem has six objectives: annual energy consumption, elec-tricity cost, overall satisfaction, thermal satisfaction, indoor air quality satisfaction, and lighting satisfaction. The problem has five constraints: power consumption, temperature, humidity,$\mathbf{CO}_{2}$concentration, and average illuminance. To solve the problem, we utilize IBEA framework. For efficient solution generation, we employ the steady-state model for IBEA. We propose the total constraint win-loss rank for multiple constraints to treat multiple constraints equally. Experimental results on artificial test problems and building facility control problems show that the proposed constraint IBEA with steady-state and total con-straint win-loss rank archives better search performance than conventional representative algorithms.

We propose a fast air-conditioning temperature optimization system using a surrogate solution evaluator. Simulation-based evolutionary optimization does not require the formulation of a mathematical model of the optimization problem and allows the treatment of the problem by considering a black box. However, the simulation-based solution evaluation is often time-consuming. Because evolutionary algorithms need to generate many candidate solutions for optimization, the time-consuming solution evaluation will be a bottleneck, which is encountered in air-conditioning temperature optimization. The building simulator provides the thermal comfort level and the power usage by inputting a candidate air-conditioning temperature setting through a complicated simulation. Although the results are useful compared with those obtained by a simple mathematical model, it is time-consuming. To accelerate the optimization process, we propose a surrogate evaluator based on the time-series predictive long short-term memory, which is a recurrent neural network architecture. Instead of the time-consuming building simulator, the surrogate evaluator outputs the two time-series data of the thermal comfort and power consumption, and schedules are optimized with the multi-objective particle swam-based optimizer OMOPSO. Experimental results show that the proposed system could obtain practical schedule sets, and the optimization was accelerated by using the surrogate evaluator.

This work proposes an evolutionary optimization method of the air-conditioning temperature schedule robust for the forecast temperature errors. To optimize the air-conditioning temperature schedule in office buildings, we need to conduct a building simulation with the air-temperature forecast since the outside air-temperature strongly affects the optimal operation of the air-conditioning system. However, since the forecasted temperatures often involve errors, the air-conditioning schedule optimized for the forecast may not be optimal practically. To acquire practically feasible air-conditioning temperature schedule robust for the air-temperature forecast errors, in this work, we propose an evolutionary multi-objective air-conditioning schedule optimization method for office buildings. In the proposed method, we estimate the temperature forecast errors by using the normal distribution model and apply an improved multi-objective particle swarm optimization algorithm, OMOPSO, to simultaneously optimize the human thermal comfort, the power consumption, and their differences when the air-temperature forecast involves errors. Experimental results show that the proposed method can acquire air-conditioning schedules robust for the uncertainty on the air-temperature forecast.