A new comprehensive approach for cost-optimal building design integrated with the multi-objective model predictive control of HVAC systems. (May 2017)

• Record Type: Journal Article
• Authors: Ascione, Fabrizio; Bianco, Nicola; De Stasio, Claudio; Mauro, Gerardo Maria; Vanoli, Giuseppe Peter
• Is Part Of: Sustainable cities and society. Volume 31(2017)
• Page Start: 136
• Page End: 150
• Publication Date: 2017-05
• Subjects: BPO building performance optimization -- BPS building performance simulation -- GA genetic algorithm -- HVAC heating ventilating and air-conditioning -- MCDM multi criteria decision-making -- MPC model predictive control -- RB reference building; Building simulation-based optimization -- Building energy design -- Model predictive control -- Multi-objective optimization -- Genetic algorithm -- Cost-optimal analysis; Sustainable urban development -- Periodicals; Sustainable buildings -- Periodicals; Urban ecology (Sociology) -- Periodicals; 307.76
• DOI: 10.1016/j.scs.2017.02.010
• Languages: English
• ISSNs: 2210-6707

Abstract

Highlights: A new methodology provides cost-optimal building thermal design solutions. Model predictive control (MPC) is used for HVAC operation in both heating, cooling. A main mono-objective genetic algorithm (GA) finds the cost-optimal solution. Two secondary bi-objective GAs find optimal MPC strategies for HVAC operation. The application shows high energy and cost savings without penalizing comfort. Abstract: A new comprehensive approach is proposed to support cost-optimal design of building envelope's thermal characteristics and HVAC (heating, ventilating and air-conditioning) systems in presence of a simulation-based model predictive control (MPC) for heating and cooling operations. The cost-optimal solution is identified through a main mono-objective genetic algorithm (GA) that minimizes global costs for space conditioning. The explored solutions represent building thermal designs integrated with the MPC of HVAC systems. For defining the MPC strategies, the main GA launches two secondary bi-objective GAs that optimize heating and cooling operations, respectively. These secondary GAs perform Pareto optimizations by minimizing operating costs and thermal discomfort. They provide the optimal hourly set point temperatures for heating and cooling operations, with a day-ahead planning horizon, by considering the forecasts of weather conditions and building use. The optimal control strategies are found based on requirements of users, who set a minimum comfort level to be fulfilled. The GAs are implemented by coupling MATLAB ® with EnergyPlus. The methodology is applied to a new multi-zone residential building in Naples (Southern Italy). It yields primary energy savings around 35.4 kW h/m 2 a and global cost savings around 7000 €, ensuring the same satisfying comfort level, compared to a standard design approach.