Multi-Objective Optimization for Energy Performance Improvement of Residential Buildings: A Comparative Study

Abstract

Numerous conflicting criteria exist in building design optimization, such as energy consumption, greenhouse gas emission and indoor thermal performance. Different simulation-based optimization strategies and various optimization algorithms have been developed. A few of them are analyzed and compared in solving building design problems. This paper presents an efficient optimization framework to facilitate optimization designs with the aid of commercial simulation software and MATLAB. The performances of three optimization strategies, including the proposed approach, GenOpt method and artificial neural network (ANN) method, are investigated using a case study of a simple building energy model. Results show that the proposed optimization framework has competitive performances compared with the GenOpt method. Further, in another practical case, four popular multi-objective algorithms, e.g., the non-dominated sorting genetic algorithm (NSGA-II), multi-objective particle swarm optimization (MOPSO), the multi-objective genetic algorithm (MOGA) and multi-objective differential evolution (MODE), are realized using the propose optimization framework and compared with three criteria. Results indicate that MODE achieves close-to-optimal solutions with the best diversity and execution time. An uncompetitive result is achieved by the MOPSO in this case study.

Economic Optimization of the Energy Supply for a Logistics Center Considering Variable-Rate Energy Tariffs and Integration of Photovoltaics

“…[17] in a single-objective optimization (SOO) process based on the NPC with a LCC perspective. This approach can be found in similar studies using multi-objective optimization (MOO) processes as performed by Li et al [24] and Asadi et al [25].…”

Multi-Objective Optimization of Building Energy Design to Reconcile Collective and Private Perspectives: CO2-eq vs. Discounted Payback Time

“…In very recent years, several derivative-free, stochastic heuristic, multi-objective approaches for building energy design were proposed, as outlined by comprehensive review studies [14][15][16][17]. Most of these approaches were based on Pareto optimization [8] thereby aiming at the achievement of the Pareto front, that is the set of non-dominated solutions, which represent trade-off solutions among contrasting objective functions.…”

On Characterizations of Directional Derivatives and Subdifferentials of Fuzzy Functions

“…Jia et al [9] presented a new algorithm for solving the optimization problem based on the stock exchange. Afterwards, in [10,15,16], a multiple genetic algorithm and multi-objective differential evolution were used to solve multiple optimization problems efficiently. Moreover, Wah et al [17] applied a genetic algorithm to optimize flow rectification efficiency of the diffuser element based on the valveless diaphragm micropump application.…”

“…In order to solve complex optimization problems in real life, various optimization algorithms have been presented in [9,10,[15][16][17][18][19][20][21][22][23]. Jia et al [9] presented a new algorithm for solving the optimization problem based on the stock exchange.…”

“…Later, Zadeh proposed the fuzzy number [2][3][4] and put forward the theory of the fuzzy numerical function together with Chang [5]. These theories and those associated with optimization theory have been extensively studied in some fields, such as economics, engineering, the stock market, greenhouse gas emissions and management science [6][7][8][9][10][11][12][13][14].…”