MEVO: A Metamodel-Based Evolutionary Optimizer for Building Energy Optimization

Abstract

A deep energy retrofit of building envelopes is a vital strategy to reduce final energy use in existing buildings towards their net-zero emissions performance. Building energy modeling is a reliable technique that provides a pathway to analyze and optimize various energy-efficient building envelope measures. However, conventional optimization analyses are time-consuming and computationally expensive, especially for complex buildings and many optimization parameters. Therefore, this paper proposed a novel optimization algorithm, MEVO (metamodel-based evolutionary optimizer), developed to efficiently identify optimal retrofit solutions for building envelopes while minimizing the need for extensive simulations. The key innovation of MEVO lies in its integration of evolutionary techniques with design-of-computer experiments, machine learning, and metaheuristic optimization. This approach continuously refined a machine learning model through metaheuristic optimization, crossover, and mutation operations. Comparative assessments were conducted against four alternative metaheuristic algorithms and Bayesian optimization, demonstrating MEVO’s effectiveness in reliably finding the best solution within a reduced computation time. A hypothesis test revealed that the proposed algorithm is significantly better than Bayesian optimization in finding the best cost values. Regarding computation time, the proposed algorithm is 4–7 times faster than the particle swarm optimization algorithm and has a similar computational speed as Bayesian Optimization.

The impact of the new EPBD “Green Houses” in the Italian building context: Technical-economic analysis on improving the energy class of existing buildings

“…In scientific literature several approaches to building energy retrofit or energy retrofit measures (ERMs) are present. In [19], an innovative metamodel-based optimization approach is presented to efficiently identify optimal retrofit interventions for building envelopes while minimizing the necessity of extensive energy simulations. It is also well known that active and passive strategies can be leveraged to obtain energy efficiency upgrade in buildings [20].…”