3 minute read
Alexis Adjorlolo
Self-adjusting Machine Learning Artificial Neural Network to Automate and Speed up Lighting Simulations
Alexis Adjorlolo
Mentor: Rania Labib School of Architecture
Introduction: Building energy consumption accounts for 30% of global energy consumption [1]. In order to support the development of energy-efficient built environments and cities, architects, urban planners, and engineers have begun to utilize Building performance simulation (BPS). Supporting decision-making and steering the Design towards high performance is crucial in the early design phase, where decisions have the highest impact on the final building’s energy consumption and costs [2–4]. However, BPS tasks are usually time-consuming. Therefore, there is a need for a framework that would speed up the BPS process. This paper aims to develop a Machine Learning algorithm, specifically Artificial Neural Networks (NN), that can potentially speed up the process of Building Performance simulations (BPS) by executing only a small subset of the simulations to predict the performance of daylighting of thousands of design configuration. Furthermore, the paper will investigate the use of an evolutionary algorithm to automatically adjust the parameter of the ANN without the interference of the user, thus allowing easy access for non-technical users. Materials and Methods: To allow access to the ANN algorithm, the PI will integrate the ANN models with popular parametric modeling tools using Python. This will facilitate the application of ML on the datasets that result from executing BPS that is created in the parametric modeling environments. Because the ANN algorithm will be automatically executed, it is necessary to apply an evolutionary algorithm to the ANN model to find the optimized parameters of the model for accurate prediction. A multi-objective evolutionary algorithm (MOEA) is an algorithm used to search for a set of pareto-optimal solutions in a single run. In multi-objective models, a set of pareto-optimal solutions are obtained instead of a single solution that optimizes various objectives. A popular MOEA algorithm is NSGA-II, which was developed in 2002 by Deb et al. [1]. NSGA-II evaluates a set of solutions in a multi-directional search space, step by step. In each step, some of the solutions are chosen to initialize the optimization process; the chosen solutions are called the parent population. To make a new set of solutions, genetic operators that are observed in nature, such as crossovers and mutations, are applied to the parent population to develop a child population. In the next step, new members are selected among the parent and child populations to act as the new parent population. In 2019, Rahimipour et al. used the NSGAII algorithm to design an ANN that can self-adjust to predict traffic patterns [2]. For this research, the PI will examine the use of NSGA-II and other evolutionary algorithms to design an ANN model that self-adjusts without user intervention. The evolutionary algorithm will be used to find the optimal set of multiple ANN parameters, such as the number of epochs, number of layers, number of units per layer, optimizer type, etc. The proposed workflow is shown in Figure 11.
Results and Discussion: The first semester was to train the students in the tools being used for this research. No Results and discussion yet!
Figure 1. Self-adjusting ANN model using NSGA-II
Conclusion(s) or Summary: Research study still in progress
References:
[1] K. Deb, A. Pratap, S. Agarwal, T. Meyarivan, A fast and elitist multiobjective genetic algorithm: NSGAII, IEEE Trans. Evol. Comput. 6 (2002) 182–197. [2] S. Rahimipour, R. Moeinfar, S.M. Hashemi, Traffic prediction using a self-adjusted evolutionary neural network, J. Mod. Transp. 27 (2019) 306–316. https://doi.org/10.1007/s40534-018-0179-5.
