From White to Black-Box Models: A Review of Simulation Tools for Building Energy Management and Their Application in Consulting Practices

Abstract

Buildings consume significant energy worldwide and account for a substantial proportion of greenhouse gas emissions. Therefore, building energy management has become critical with the increasing demand for sustainable buildings and energy-efficient systems. Simulation tools have become crucial in assessing the effectiveness of buildings and their energy systems, and they are widely used in building energy management. These simulation tools can be categorized into white-box and black-box models based on the level of detail and transparency of the model’s inputs and outputs. This review publication comprehensively analyzes the white-box, black-box, and web tool models for building energy simulation tools. We also examine the different simulation scales, ranging from single-family homes to districts and cities, and the various modelling approaches, such as steady-state, quasi-steady-state, and dynamic. This review aims to pinpoint the advantages and drawbacks of various simulation tools, offering guidance for upcoming research in the field of building energy management. We aim to help researchers, building designers, and engineers better understand the available simulation tools and make informed decisions when selecting and using them.

Optimization of Double-Closed-Loop Control of Variable-Air-Volume Air-Conditioning System Based on Dynamic Response Model

“…This type of modeling also has a wide range of applications in modeling air-conditioning systems, including data-driven empirical and physical-empirical hybrid models based on data-driven models, where empirical models are known as black-box models [16,17] that do not consider the physical processes and are based on the analysis of input and output time series only and physical-empirical hybrid models, also known as grey-box models [18], are based on the energy balance equation to determine the structure and form of the model. When using neural networks or genetic algorithms to identify the model parameters, retaining the physical properties of the system while using the actual operating data to improve the accuracy of the model, the previous data-driven model is often incomplete data, with too much chance and other problems, resulting in the empirical model not being able to accurately reflect the system; thus, the model is not very generalizable [19]. This paper will tackle the prevalent issue of room temperature lag in variable-airvolume air-conditioning systems within public buildings, taking the end device variableair-supply regulation as an example, and firstly, start from the physical model, from the point of view of differential equations, transfer functions, and state space equations.…”

Estimating Spatio-Temporal Building Power Consumption Based on Graph Convolution Network Method

“…Physical or "white box" models dynamically describe the thermal behavior of a building using heat and mass transfer equations. EnergyPlus, TRNSYS, and DOE-2 are some of the available software packages for building energy modeling [3]. Hybrid or "gray box" methods incorporate physical and data-driven approaches to predict building energy consumption, such as the proposal by Dong et al [4], which combines building geometry (physical) and historical power consumption data to predict air conditioning and total power consumption for a group of residences.…”