A Multi-objective Harmony Search Algorithm for Optimal Energy and Environmental Refurbishment at District Level Scale

Abstract

Nowadays municipalities are facing an increasing commitment regarding the energy and environmental performance of cities and districts. The multiple factors that characterize a district scenario, such as: refurbishment strategies' selection, combination of passive, active and control measures, the surface to be refurbished and the generation systems to be substituted will highly influence the final impacts of the refurbishment solution. In order to answer this increasing demand and consider all above-mentioned district factors, municipalities need optimisation methods supporting the decision making process at district level scale when defining cost-effective refurbishment scenarios. Furthermore, the optimisation process should enable the evaluation of feasible solutions at district scale taking into account that each district and building has specific boundaries and barriers. Considering these needs, this paper presents a multi-objective approach allowing a simultaneous environmental and economic assessment of refurbishment scenarios at district scale. With the aim at demonstrating the effectiveness of the proposed approach, a real scenario of Gros district in the city of Donostia-San Sebastian (North of Spain) is presented. After analysing the baseline scenario in terms of energy performance, environmental and economic impacts, the multi-objective Harmony Search algorithm has been employed to assess the goal of reducing the environmental impacts in terms of Global Warming Potential (GWP) and minimizing the investment cost obtaining the best ranking of economic and environmental refurbishment scenarios for the Gros district.

An Evaluation Framework to Support Optimisation of Scenarios for Energy Efficient Retrofitting of Buildings at the District Level

“…These criteria are defined through the inputs provided by the users in terms of targets and boundaries to the optimisation problem. A harmony search optimisation algorithm allows repeating the generation of groups and scenarios and their evaluation in terms of the cost and benefit functions [25]. This algorithm generates new iterations in the form of groups of candidate retrofitting scenarios, which are created based on the results that are obtained from the previous execution and, therefore, optimising them in a continuous loop.…”

Integration of Prioritisation Criteria in the Design of Energy Efficient Retrofitting Projects at District Scale: A Case Study

“…Outcomes export. Finally, the users are presented with a set of best retrofitting scenarios in a Pareto front [23], allowing them to select the desired scenario or scenarios and exporting the outcomes produced by the tool in the form of reports for the baseline and selected scenario(s), list and detailed description of the ECMs considered by the scenario, and models of these ECMs that can then be visualised and used by other external tools. generating populations of candidate retrofitting scenarios as combinations of ECMs and following certain rules of incompatibility among them, and also only considering those measures that have been checked and approved by the users of the tool, to avoid proposing solutions not compatible with the users' expectations.…”

“…Outcomes export. Finally, the users are presented with a set of best retrofitting scenarios in a Pareto front [23], allowing them to select the desired scenario or scenarios and exporting the outcomes produced by the tool in the form of reports for the baseline and selected scenario(s), list and detailed description of the ECMs considered by the scenario, and models of these ECMs that can then be visualised and used by other external tools. All these four processes together allow for delivering more informed decisions and reducing time and costs of the design process, having also an impact on the subsequent stages of the process through elements as the use of BIM.…”

Aplicación de técnicas de toma de decisión multicriterio a la rehabilitación energética de áreas urbanas residenciales

“…permite establecer un proceso de generación de escenarios y evaluarlos en términos de las funciones de coste y beneficio [91]. Este algoritmo genera nuevas iteraciones en forma de grupos de escenarios candidatos de rehabilitación, que se crean en función de los resultados que se obtienen de la ejecución anterior y, por lo tanto, los optimiza en un bucle continuo.…”

“… Exportación de resultados (4). Finalmente, a los usuarios se les presenta un conjunto de mejores escenarios de rehabilitación en un frente de Pareto [128], lo que les permite seleccionar el escenario o escenarios deseados y exportar los resultados producidos por la herramienta en forma de informes para la línea base y el escenario seleccionado), lista y descripción detallada de las MAES considerados en el escenario y los modelos de etas MAES que luego pueden ser visualizados y utilizados por otras herramientas externas.…”