Impact of Reference Years on the Outcome of Multi-Objective Optimization for Building Energy Refurbishment

Abstract

Abstract:There are several methods in the literature for the definition of weather data for building energy simulation and the most popular ones, such as typical meteorological years and European test reference years, are based on Finkelstein-Schafer statistics. However, even starting from the same multi-year weather data series, the developed reference years can present different levels of representativeness, which can affect the simulation outcome. In this work, we investigated to which extent the uncertainty in the determination of typical weather conditions can affect the results of building energy refurbishment when cost-optimal approach is implemented for the selection of energy efficiency measures by means of the NSGA-II genetic algorithm coupled with TRNSYS simulations. Six different reference years were determined for two north Italy climates, Trento and Monza, respectively in the Alpine and in the continental temperate regions. Four types of energy efficiency measures, related to both building envelope and HVAC system, were applied to six existing building typologies. Results showed how the choice of reference year can alter the shape of the Pareto fronts, the number of solutions included and the selection among the alternatives of the energy efficiency measures, for the entire front and, in particular, for energy and economic optima.

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

“…Sobol sequences allow reducing the random behaviour of GA in the initial population generation and avoiding oversampling of the same regions that can occur with random sampling [117]. It is also employed in [148] where NSGA-II is modified with customised sampling, crossover, mutation and selection procedures with the purpose of further increasing its performance. SSS is chosen since it produces uniform samples for high population sizes [168] and the random starting point is obtained through the pseudo-random generator [169].…”

“…SSS is chosen since it produces uniform samples for high population sizes [168] and the random starting point is obtained through the pseudo-random generator [169]. In the PS it is used in particular to apply the population mutation mechanism through random gene alteration: a gene is randomly selected and replaced by a random value from a uniform distribution that meets the gene range [10,148]. Finally, a smart sampling or smart exhaustive sampling technique is utilised in around 10% of the PS [105,106,109,132,153] at the postoptimisation stage, as a way to conduct constrained cost-optimal analyses for DM regarding the Pareto front solutions found through GA implementation.…”

Influence of the Meteorological Record Length on the Generation of Representative Weather Files

“…Further research has been carried out on the topic, extending the TMY generation methodology using on weighting coefficients, for example in [23], which provided a study on the representativeness of reference years obtained from sequentially reduced MY. Other aspects of the selection of the weather data have been investigated in a more recent study on the use weighting coefficients [24] and the effect of the different procedures of generation of representative years on the outcome of multi-objective optimization for building energy refurbishment has been studied in [25].…”

Effects of Recent Climate Change on Hourly Weather Data for HVAC Design: A Case Study of Osaka

“…In addition, the comparison of TMY and TRY weather data with those collected by the urban stations showed non-negligible seasonal discrepancies in terms of the main microclimate monitored parameters such as dry-bulb temperature, relative humidity, solar radiation, and wind. A study on finding the optimal cost-effective solutions for buildings by comparing data from six different reference years was carried out, considering a group of simplified building configurations located in Trento, northern Italy [15]. The results showed changes for both Pareto fronts and optimal retrofit solutions.…”