Genetic algorithm-based approach for optimizing the energy rating on existing buildings

Abstract

This paper presents an innovative method for the building energy-retrofit process. By applying a simple genetic algorithm, the aim is to optimize the cost of intervening in an existing building by fixing the energy rating obtained at a given value. The practical potential of the method presented here is quite extensive, with its greatest exponent being its use by technicians who are unfamiliar with optimization processes. The application of this calculation methodology would simplify the study of projects in the phase of selecting energy-saving measures, given that there are currently many of them, with their independent characteristics, which makes the selection process a slow and ineffective task. In addition, the method's intuitive interface and the fact that it is programmed in MS Excel make it an innovative method with great applicability in the field of building process optimization. AbstractThe problem of improving the energy behaviour of existing buildings is a current topic of interest in scientific research. In recent years, Public Administrations have made an effort to introduce norms that help to reorient the tendency toward increasing energy consumption by buildings. To do so, manufacturers have developed numerous energy efficiency measures that have become widely extended. The main problem when selecting one or various measures is to identify the ones that will provide the best tradeoff between services and implementation costs. This paper presents a study focused on implementing techniques for calculating the heating and cooling energy demand, along with genetic algorithm, to optimize the process of adjusting the building's energy efficiency rating to a determined rating for existing building. The proposed optimization approach is applied to a real case to demonstrate its validity in a real world situation.

A systematic review of genetic algorithm-based multi-objective optimisation for building retrofitting strategies towards energy efficiency

“…• Stall time and generations limit: the number of generations or the time limit with no significant change or where change is inferior to a pre-specified threshold (e.g. by less than 1%) are adopted as stopping criteria in around 16% of PS [9,127,131,136,150,163];…”

“…Constraints are usually formulated as functions of the variables to be optimised and are most frequently employed in this SR to define thermal comfort [122,130,131,145,146,153] as well as budget and payback boundaries in the optimisation process [9,69,105,123,141,143,144,152,155,156]. Secondarily, they target energy consumption and CO2 emissions [140,141,144,145], along with insulation material properties [88,140,150].…”

“…Other customised optimisations were found to use C programming coupled with EnergyPlus [127]. Contreras et al [150] enhanced the utility of combining simplified building models with optimisation tools versus the high computational cost of detailed energy models: the authors code the standard energy calculation approach in ISO 13790 and EN 15217 in MS excel programming and used the GA included in the MS Excel Solver tool for the optimisation. Other optimisation studies coded simplified dynamic models of buildings: Wright et al [121] used the lumped capacitance model to approximate the transient conduction in a ventilation slab system and building fabric.…”

“…Furthermore, the type of insulation material related to walls, roofs, and in a lower degree, ground and basement floors[12,20,143,145,150,155,156,158,22,71,87,107,110,128,140,142] is found to be accountable for one of the most common decision variables studied in the PS. Other variables optimised within the building envelope category include wall configuration encompassing PCM properties[159], air tightness rate variation[9,20,156,160,22,69,76,127,135,136,140,141], sealing options[71,156] and solar shading related variables namely façade installation, shading type and shade factor (interior or exterior shading systems, blinds, overhangs)[20,76,105,109,136,150,152,159,161,162].…”

Revealing Urban Morphology and Outdoor Comfort through Genetic Algorithm-Driven Urban Block Design in Dry and Hot Regions of China

“…Xu et al estimated the impact of open space on the urban micro-climate and validated the open space design strategies through performance-based optimization, however, which need further consideration of the impact of building form, street orientation, and so on [3]. Contreras et al used a genetic algorithm to conduct random variation and combination with building façade, roof, window type, glass type and shade coefficient as genes, to achieve the lowest building energy consumption [42].…”