Modeling and sensitivity analysis of the building energy consumption using the Monte Carlo method

Throughout the world, research and algorithms are being developed every day to try to predict and estimate, by means of mathematical models, the consumption of electrical energy in facilities such as buildings, homes and urbanizations. This allows developing the ability to identify, represent and model problems, with the objective of improving processes. This research was developed in the buildings of UTC La Mand campus. The mathematical model consists of an algorithm that simulates the energy consumption or demand of the main electrical loads of a building. For the development, it is intended to have initial measurements, which are the historical values of the characteristics of their loads knowing which is the particular and group behavior. This information was collected and analyzed using statistical methods, Monte Carlo and the assignment of distribution probabilities to predict its use. A scenario analysis was performed to identify weaknesses and possible improvements in the energy management of the building. As a result, daily consumption profiles and their data matrices were obtained. These were used to calculate the stochastic energy consumed by the building. The research offers a proposal to improve the building's consumption profile, in order to show a better electrical load profile. Energy management is innovative in this consumer world, identifying the best scenario to optimize demand using Monte Carlo, with this project the management of awareness of the rational use of energy will be carried out, providing a bonus for the sector that best apply the project (C) 2022 The Author(s). Published by Elsevier Ltd.

Automated summary

Research and algorithms are being developed worldwide to predict and estimate the consumption of electrical energy in buildings and other facilities. This particular study developed a mathematical model to simulate the energy consumption of a building's main electrical loads. By collecting and analyzing data, the researchers proposed a solution to optimize the building's energy consumption profile.