Most common practices for solving building retrofit problems lack efficiency and overall robustness. Knowledge of novel methods that support decision-making (DM) for retrofitting is critical for sustainability and energy performance improvement. This systematic review for the first time provides a large evidence-base to assess the potential of Multi-objective optimisation (MOO) using Genetic algorithm (GA) for supporting the development of retrofitting strategies and its DM process. From 557 screened studies, 57 were reviewed focusing on outcomes, current trends, and the method's potential, challenges, and limitations. Key findings reveal a strong suitability for solving a wide range of building retrofit MOO problems, based on robust outcomes with significant objectives improvement. However, results also indicate that yielding optimal retrofit solutions may require GA-mixed techniques or modified GA, due to time-consuming and effectiveness issues. Heritage buildings, where qualitative objective function definition is particularly challenging, have been little addressed. Further challenges include: lack of standard systematic approach; complex switch between modelling and optimisation environment; high expertise needed to perform MOO and manage software; and lack of confidence in results. While GA-based MOO's robust evaluation for supporting building retrofit and its DM process needs further research, promising potential is shown overall, when complemented with auxiliary techniques.

Abstract:Integrating daylight and energy performance with optimization into the design process has always been a challenge for designers. Most of the building environmental performance simulation tools require a considerable amount of time and iterations for achieving accurate results. Moreover the combination of daylight and energy performances has always been an issue, as different software packages are needed to perform detailed calculations. A simplified method to overcome both issues using recent advances in software integration is explored here. As a case study; the optimization of external shadings in a typical office space in Australia is presented. Results are compared against common solutions adopted as industry standard practices. Visual comfort and energy efficiency are analysed in an integrated approach. The DIVA (Design, Iterate, Validate and Adapt) plug-in for Rhinoceros/Grasshopper software is used as the main tool, given its ability to effectively calculate daylight metrics (using the Radiance/Daysim engine) and energy consumption (using the EnergyPlus engine). The optimization process is carried out parametrically controlling the shadings' geometries. Genetic Algorithms (GA) embedded in the evolutionary solver Galapagos are adopted in order to achieve close to optimum results by controlling iteration parameters. The optimized result, in comparison with conventional design techniques, reveals significant enhancement of comfort levels and energy efficiency. Benefits and drawbacks of the proposed strategy are then discussed. OPEN ACCESSBuildings 2015, 5 561

Climate change is expected to increase the frequency and duration of hot weather and its associated adverse health effects. In dense urban areas, these phenomena will be exacerbated by the Urban Heat Island (UHI) effect and indoor overheating. This paper assesses population exposure and vulnerability to high summer temperatures by exploring the geospatial connection between the UHI, housing energy efficiency and overheating risk, and social vulnerability indicators, such as income and the elderly population. Focusing on Madrid and London, two European cities with strong UHIs but contrasting drivers of indoor heat risk, the spatial distribution of selected indicators were analysed by means of Geographical Information Systems, and areas with the highest vulnerability towards summer energy poverty were identified. It was found that while 'hot and vulnerable' areas are present in both Madrid and London, there are significant differences in climate, socioeconomic distribution and housing between the two cities. In warmer climates such as Madrid, energy poverty-traditionally defined by wintertime heating-requires its definition to be broadened to include summertime cooling needs; in the context of climate change and urban warming trends, this may soon also be the case in northern cities such as London.

Understanding the indoor environmental conditions of livable architectural heritage such as vernacular dwellings is a key step towards its conservation. Yet, there is a lack of large-sample studies that assess indoor conditions using long-term quantitative and qualitative data complying with monitoring standards. This paper addresses this gap in Portuguese vernacular dwellings using long-term mixed methods, by analyzing the thermal performance, indoor air quality, and illuminance of 22 case studies. Key findings highlight the role of thermal mass in damping the outdoor thermal wave and providing thermal stability, night ventilation, and lack of windows. Summer thermal performance bettered that of winter, but occupant control strategies negatively impacted thermal stability and overheating. In winter, the most prevalent heating system, electric, performed less efficiently than radiant heating, leaving occupants exposed to thermal discomfort and health risks from cold, mold, and toxins from wood-burning and cooking. Important discrepancies were found between the illuminance monitored and survey data, indicating the significance of cultural practices in indoor environment acceptability and expectations.

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