Optimization of an HVAC system with a strength multi-objective particle-swarm algorithm

Abstract

A data-driven approach for the optimization of a heating, ventilation, and air conditioning (HVAC) system in an office building is presented. A neural network (NN) algorithm is used to build a predictive model since it outperformed five other algorithms investigated in this paper. The NN-derived predictive model is then optimized with a strength multi-objective particle-swarm optimization (S-MOPSO) algorithm. The relationship between energy consumption and thermal comfort measured with temperature and humidity is discussed. The control settings derived from optimization of the model minimize energy consumption while maintaining thermal comfort at an acceptable level. The solutions derived by the S-MOPSO algorithm point to a large number of control alternatives for an HVAC system, representing a range of trade-offs between thermal comfort and energy consumption.

Introduction

Heating, ventilating, and air conditioning (HVAC) systems, account for over 50% of the energy consumed by buildings [1]. Therefore, balancing energy consumption and thermal comfort is a major concern in the management of HVAC systems.

Energy consumption for HVAC systems has been widely discussed in the literature. Many researchers centered on mathematical models and simulation approaches. Nassif et al. [2], [3], [4] proposed a supervisory control strategy to optimize the set points of local-loop controllers used in a multi-zone HVAC system. Gebreslassie et al. [5] applied a mathematical programming approach to design environmentally conscious absorption cooling systems. Integrating building energy simulation software EnergyPlus with a generic optimization program GenOpt, Djuric et al. [6] built a model to optimize parameters influencing energy, thermal comfort, and investment cost. Tashtoush et al. [7] discussed deriving a dynamic model of an HVAC system for control analysis. Mossolly et al. [8] examined optimal control strategies of a variable air volume air conditioning system using a genetic algorithm.

HVAC systems are complex, nonlinear, and large-scale systems involving numerous constraints, and thus many studies focused on using data mining approaches to build predictive models. Ari et al. [9] applied fuzzy logic and a neural network to approximate indoor comfort and energy optimization. Ben-Nakhi et al. [10] used general regression neural networks to optimize air conditioning setback scheduling in public buildings. To decrease the computational cost, Magnier et al. [11] developed a predictive model with TRNSYS simulations integrated by NSGA-II for HVAC systems. The methodology has been implemented successfully. Kusiak et al. [12], [13], [14] presented dynamic models to predict energy consumption and thermal comfort at current time and future time periods using neural networks. Chang et al. [15] employed the Hopfield neural network to determine the chilled water supply temperatures in chillers. Kusiak et al. [16] presented a data mining approach for the optimization of an HVAC system.

This paper proposes a next-generation dynamic predictive model derived with data mining algorithms. A similar model has been studied in [14]. This model is optimized with a strength multi-objective particle-swarm optimization algorithm shown to be particularly suitable for solving complex, nonlinear, discrete, and large-scale systems. Many applications in the medical, marketing, manufacturing, and industrial sectors can benefit from data mining and PSO [17], [18], [19], [20], [21]. In this research data mining algorithms are applied to build a dynamic model based on a data set obtained from an HVAC system. A strength multi-objective particle-swarm optimization algorithm is used to find the optimal strategies for control of the HVAC system.