Building Energy Analysis is the process of assessing and improving a building’s energy performance using online tools that track energy use, help with maintenance, and assist in decision-making during design and renovation.
Solar Energy in Buildings:
Design Methods:
The thermal design of a building, including solar systems, must be based on a predictive model. One basic way to evaluate expected heat flow is by assuming steady-state conditions, using the daily average indoor and outdoor temperature difference to calculate the 24-hour average solar gain. This simple calculation gives an approximation, as outdoor temperatures vary throughout the day, reaching their highest just after noon and lowest just before sunrise. Solar input usually happens for less than a third of the day, peaking at noon and tapering off until sunset. Additionally, cloud cover can change conditions, and indoor temperature needs and internal heat gains can shift during the day. As a result, it’s important to consider how the building’s thermal response changes over time.
Advanced mathematical models have been developed and incorporated into powerful software programs to simulate building thermal behavior. These programs use detailed climate data and track heat flow through various building elements with different exposures and construction types. The time intervals used often range from one hour to as short as ten minutes, with simulations running over a full year to predict indoor temperatures or calculate the energy needed to maintain set temperatures. These tools use hourly weather data, often from a typical meteorological year (TMY) or typical reference year (TRY). Two major methods used for these simulations are finite difference and response factor methods.
In the last decade, with significant improvements in computer processing power, a number of software systems have been developed, integrating sophisticated simulation methods while offering user-friendly interfaces. One early system is DOE-2, a widely accepted free program that predicts energy use and costs for various building types. It uses building details provided by the user, such as layout, construction, usage, and utility rates, along with weather data, to perform hourly simulations and estimate utility bills. Another example is EnergyPlus, a program used by engineers, architects, and researchers to model energy consumption for heating, cooling, lighting, and other building systems. Developed by the U.S. Department of Energy, EnergyPlus has several add-ons and graphical user interfaces (GUIs) available.
These software packages are designed for building thermal response, heating and cooling load calculations, and energy consumption simulations, and they are also suitable for modeling most passive systems. For active solar systems, the best simulation tool is TRNSYS, a modular program from the University of Wisconsin. TRNSYS allows users to define system components and how they are connected, offering great flexibility and the ability to add mathematical models not included in the standard library. TRNSYS is particularly useful for detailed analysis of systems that depend on time, such as solar thermal and photovoltaic systems, renewable energy systems, and HVAC systems.
For designers, simplified methods may be more practical. One commonly used method for active solar systems is the f-chart method, which can be applied manually or with dedicated software. This method calculates the solar fraction, which is the portion of a building’s total load (like heating) that can be met by the solar system. The simplicity of this method makes it easy for designers to test different options and choose the best one. Today, free software like SAM and RETSCREEN is available, incorporating product and weather databases to simplify the process. The System Advisor Model (SAM) is a performance and financial tool used in the renewable energy industry to make decisions about projects. It provides performance predictions for various energy systems and calculates costs based on factors like location, installation costs, financing, and incentives.
Simulation tools are also used for parametric studies, where one design variable is changed at a time to evaluate its impact on system performance. These studies generate sensitivity curves, which help designers make informed decisions.
Evaluating Thermal Resilience of Building Designs Using Building Performance Simulation:
In many of the studies reviewed, thermal zoning was used to divide spaces in simulation models. This approach is common in building energy analysis, where thermal zones are based on the connection to the HVAC system. However, in buildings where the HVAC system is not functioning, zoning based on the building’s architectural layout is preferred, as this allows for room-level analysis without mechanical ventilation influencing air mixing.
For multi-family buildings, simulating apartment units in different orientations helps analyze how orientation affects thermal resilience. For example, south-facing units receive more solar radiation and are more prone to overheating than north-facing ones.
Modeling different building floors also provides insights into temperature differences with height. For instance, top floors tend to have higher air temperatures, as found in a study by Sun et al. In another study, Sun et al. used a detailed energy model to identify rooms most at risk of overheating in a nursing home, revealing potential health risks.
Review of Web-Based Building Energy Analysis Applications:
Research on building energy modeling often focuses on standalone tools for simulating energy demand, while web-based tools have received less attention. Previous studies, such as those by Crawley et al. and Jarić et al., compared various standalone energy analysis software. In contrast, this study aims to explore web-based energy analysis tools, which have become increasingly popular in recent years. These tools can assist in monitoring energy consumption and improving building performance during design and retrofit processes.
This study will provide an overview of web-based tools, examining their calculation methods, input and output requirements, and capabilities. The goal is to help users compare these tools based on the stage of building design or renovation and to highlight opportunities for future development.
Optimizing Glazing Transmittance for Energy Efficiency and Daylighting:
This study explores the use of simulation and optimization techniques to adjust the glazing’s transmittance based on solar radiation wavelengths to improve both daylighting and energy efficiency in buildings. The approach involves combining software tools like TRNSYS, EnergyPlus, WINDOW, DAYSIM, and RADIANCE to create an optimization model.
The process includes defining the glazing’s solar transmittance, absorbance, and reflectivity as functions of radiation wavelength, rather than as fixed values. Building simulations are conducted for different functional forms to identify the optimal configuration that balances energy efficiency and daylighting. Neural networks or fuzzy logic methods may be used to determine the best control functions for the glazing system.
Thermal Zoning for Building HVAC Design and Energy Simulation: A Literature Review 2 Methodology
This study includes a thorough review of the existing literature on selecting a thermal zoning method for HVAC design and building energy simulation. It focuses on key features, methods, and processes. Specifically, the goals of the study were to: (1) introduce basic concepts and definitions related to thermal zoning as found in the literature; (2) summarize the various thermal zoning methods and practical uses in HVAC system design and building energy analysis; (3) explore how thermal zoning strategies impact the building energy modeling process; and (4) suggest future developments and possible research opportunities in new thermal zoning methods for building energy simulation.
A literature review was conducted using academic search engines such as Google Scholar, Mendeley, and Scopus. The main keywords used for this review included “thermal zone,” “thermal zoning,” “HVAC design,” “building energy simulation,” and “indoor temperature profile.” In addition to research articles, various types of literature, including textbooks, standards, and HVAC handbooks, were also reviewed to cover thermal zoning and related definitions, methods, and applications.
This work is organized into five sections. Section 1 provides an introduction and background on the importance of thermal zoning in building energy simulation. Section 2 explains the overall methodology used for this study. Section 3 presents a literature review of previous research and information related to this study, including a review of building energy simulation tools, the definition of building thermal zones, the connection between thermal zoning and HVAC design, and building thermal zoning methods for energy simulation. Section 4 discusses possible future research directions for thermal zoning methods in building energy simulation. Finally, Section 5 presents the conclusions drawn from this study.
Towards Zero-Energy Buildings in China: A Systematic Literature Review
4.3 Energy Allocation Strategy and Operation Management
Achieving zero-energy buildings depends on using various energy-saving measures and equipment, along with effective energy management strategies. Since energy used during the operational phase accounts for 80–90% of total building energy consumption (Yang, 2012), effective energy management is essential for improving system efficiency and reducing operational energy use. The energy management system for zero-energy buildings consists of three main parts: energy consumption collection and analysis, building energy allocation control strategy, and operation control strategy (Wang et al., 2015).
In China, most energy management systems for zero-energy buildings are intelligent control systems (Liu, 2014b). Li et al. (2015b) analyzed the use of building automation system (BAS) technology in a nearly zero-energy demonstration building at the China Academy of Building Research (CABR). They found that this technology significantly improved the building system’s energy efficiency. Lu et al. (2014) proposed control strategies for air conditioners, phase-change materials, and shading systems in a zero-energy house, using a Mitsubishi programmable logic controller (PLC) for energy management. Huang et al. (Huang and Su, 2018) designed an optical storage micro-grid and intelligent control system based on a PLC for a zero-energy house in Beijing. This system balanced power generation from PV grid connections with system power consumption through intelligent control.
Energy management systems aim to optimize energy distribution by analyzing statistical data to improve the building energy allocation system and make full use of available resources (Ma, 2017). Yu et al. (2017) applied principal component analysis (PCA) in building energy management, showing its usefulness for analyzing building energy consumption and energy management. Long et al. (2018) proposed a six-step energy planning approach to solve the problem of uncoordinated demand and supply for renewable energy in nearly zero-energy buildings.
Towards Integrating Occupant Behaviour Modelling in Simulation-Aided Building Design: Reasons, Challenges and Solutions
1.2 Existing Reviews
Several review articles have examined important aspects of occupant behavior (OB) modeling research. For example, Berger et al. [10] reviewed studies that suggest OB is a major factor in the performance gap and evaluated the evidence. Harputlugil et al. [11] focused on categorizing different types of occupants, understanding their attributes, and exploring the interactions between people and buildings. Wu et al. [12] provided formal definitions for OB, the factors influencing OB, and the effects of OB on building energy analysis. They also began exploring BPS tools used to represent common OB behaviors. Stazi et al. [13] offered a deeper understanding of OB drivers and how environmental and time-related factors influence OB. They reviewed how this information is incorporated into OB model variables. Various studies have discussed OB modeling methods and their applications [14–18], highlighting their strengths and weaknesses or providing a general overview of the field and its impact on energy-saving potential. Osman et al. [19] focused on using Time Use Survey (TUS) data for developing OB models and their application to building energy use. Some researchers have concentrated on OB modeling in specific contexts, such as residential buildings [20], offices [3], and urban-scale buildings [21,22]. Carlucci et al. [4] performed a systematic review on OB modeling approaches for a wide range of building types, climates, and occupant behaviors.
When it comes to integrating OB models into building design, Yu et al. [23] focused on the key criteria for comparing and selecting modeling approaches and improving OB model performance. Hong et al. [1] reviewed how OB models can be integrated into BPS tools, looking at their advantages and drawbacks, how to choose the right model based on the OB behavior, and the capabilities of related commercial software. Lastly, Azar et al. [5] explored simulation-aided occupant-centric design, defining key concepts and supporting mechanisms for occupant-focused design methodologies.
Despite these efforts, most articles focus on the OB research field itself, with few discussing its application in simulation-aided building design. The reasons, challenges, and solutions for applying OB models are scattered across the literature.
Development and Improvement of Occupant Behavior Models Towards Realistic Building Performance Simulation: A Review
7 Discussion
7.1 Selection of Modeling Methods
OB models are crucial for improving the accuracy of energy demand predictions in building energy simulations. However, choosing the right modeling method is a challenge because different methods have varying advantages and limitations. A random selection of methods can lead to ineffective or even failed OB model development and application. For example, Haldi and Robinson (2011) stated that stochastic models are not essential for total energy performance simulations, but they are useful when predicting peak demand distributions.
To assist in selecting the appropriate method, it is necessary to compare different modeling approaches in detail, considering their capabilities (e.g., prediction accuracy) and requirements (e.g., computational time) for various purposes (e.g., building system control). There is a lack of comparative studies on OB models, with most existing studies limited to predicting window behavior. Haldi and Robinson (2009) compared different models for predicting window behavior, including logistic regression, Markov chain, and hybrid models. Their comparison showed significant differences in predicted window opening behavior, with the hybrid model producing smaller percentages of opening and closing durations compared to the Markov chain model. There is still no consensus on the best approach for different simulation scenarios, likely because OB models have various applications, and specific guidelines may not be possible.
Moreover, the lack of proper documentation and validation of OB models is another reason why comparative studies are rare. To fill this research gap, future studies should look into different simulation scenarios, spatial scales, and application domains (e.g., heating and cooling demand) to assess the performance differences brought about by various OB models. These differences should be evaluated based on performance indicators such as total energy demand versus hourly demand.
A few studies have focused on developing criteria and frameworks for selecting modeling methods. Gaetani, Hoes, and Hensen (2016) and Yan et al. (2017) proposed a “fit-for-purpose” (FFP) framework, emphasizing model complexity (predictability of different aspects of OB) in method selection. However, the criteria proposed in previous studies are not consistent, and no uniform set of criteria exists for selecting the right model.
This review proposes six general selection criteria for OB models, considering their overall performance. The performance of each modeling method is compared based on these criteria. These criteria include model complexity, computational efficiency, variable selection, flexibility, integration capability, and expertise requirements.
