Genetic and Swarm Algorithms for Optimizing the Control of Building HVAC Systems Using Real Data: A Comparative Study

Abstract

Buildings consume a considerable amount of electrical energy, the Heating, Ventilation, and Air Conditioning (HVAC) system being the most demanding. Saving energy and maintaining comfort still challenge scientists as they conflict. The control of HVAC systems can be improved by modeling their behavior, which is nonlinear, complex, and dynamic and works in uncertain contexts. Scientific literature shows that Soft Computing techniques require fewer computing resources but at the expense of some controlled accuracy loss. Metaheuristics-search-based algorithms show positive results, although further research will be necessary to resolve new challenging multi-objective optimization problems. This article compares the performance of selected genetic and swarm-intelligence-based algorithms with the aim of discerning their capabilities in the field of smart buildings. MOGA, NSGA-II/III, OMOPSO, SMPSO, and Random Search, as benchmarking, are compared in hypervolume, generational distance, ε-indicator, and execution time. Real data from the Building Management System of Teatro Real de Madrid have been used to train a data model used for the multiple objective calculations. The novelty brought by the analysis of the different proposed dynamic optimization algorithms in the transient time of an HVAC system also includes the addition, to the conventional optimization objectives of comfort and energy efficiency, of the coefficient of performance, and of the rate of change in ambient temperature, aiming to extend the equipment lifecycle and minimize the overshooting effect when passing to the steady state. The optimization works impressively well in energy savings, although the results must be balanced with other real considerations, such as realistic constraints on chillers’ operational capacity. The intuitive visualization of the performance of the two families of algorithms in a real multi-HVAC system increases the novelty of this proposal.

Optimizing the Three-Dimensional Multi-Objective of Feeder Bus Routes Considering the Timetable

“…The performance of the algorithm is evaluated using various metrics such as the generational distance (GD), spacing metric (SP) [48], and hypervolume indicator (HV) [49]. A smaller GD value indicates better convergence of the algorithm towards the Pareto frontier, while a smaller SP value implies a more evenly distributed solution set.…”

Energy Management Model for HVAC Control Supported by Reinforcement Learning

“…In [33], a data-driven mathematical model using contextual sensor data is used to optimize multi-HVAC systems that serve the same space. In [34], several optimization models are compared for multi-HVAC systems management considering the outdoor temperature, building ambient temperature, and occupancy. The compared models were a multiobjective genetic algorithm, two nondominated sorting genetic algorithms, an optimized multiobjective particle swarm optimization, a speed-constrained multi-objective particle swarm optimization, and a random search.…”

A Multi-Task Decomposition-Based Evolutionary Algorithm for Tackling High-Dimensional Bi-Objective Feature Selection

“…There are also many other kinds of excellent MOEAs [27][28][29], including the novel multi-objective particle swarm optimization algorithm proposed by Leung et al [30], which adopted a hybrid global leader selection strategy with two leaders: one for exploration and the other for exploitation. Moreover, MOEAs have also been used to solve many real-world optimization problems [31][32][33], such as system control [34,35], community detection [36,37], network construction [38][39][40], task allocation [41,42], and feature selection [43,44]. Generally speaking, feature selection is normally used to select useful feature subsets for classification [45], while the bi-objective feature selection problem usually seeks to minimize both the classification error and the number of selected features [46].…”