Introduction to Building Energy Modeling
There is good reason that so much attention is paid to the concept of mathematical model in engineering and physics curriculum. Simple regressions derived from empirical data, differential equations based on first-principles, or detailed computational flu
- PDF / 977,823 Bytes
- 12 Pages / 439.37 x 666.142 pts Page_size
- 68 Downloads / 230 Views
Introduction to Building Energy Modeling
1.1 Why Modeling? There is good reason that so much attention is paid to the concept of mathematical model in engineering and physics curriculum. Simple regressions derived from empirical data, differential equations based on first-principles, or detailed computational fluid dynamic simulations each provide an analytical framework that yields insight into the behavior of physical systems. In turn, those insights can lead to design decisions that have real impact on safety, cost, and performance of the cars we drive, the power grids that deliver our electricity, and the energy efficiency of the buildings we live and work in. The cost/benefit of modeling has varied across markets and over time. A few examples include: • 1960s Aerospace: The aerospace industry was early to embrace model-based design in order to manage the incredible cost of prototyping aircraft while protecting the lives of test pilots. • 1970s Automotive: Increasingly stringent fuel efficiency and emissions standards, coupled with reliance on complex electronic controls drove engine and car manufacturers to adopt sophisticated model-centric processes to minimize development cost and time to market. • 1980s Financial: While statistical analysis had long been used to assess trends and risk in financial markets, widespread integration of computers into financial transactions put greater pressure on real-time modeling and analytics to maximize profit in both short and long terms. • 2000s Power: Beside the issues of load growth and emissions reduction, the power sector was faced with myriad challenges ranging from increased penetration of renewable energy resources to the introduction of demand response strategies that introduced volatility to the grid. Increasingly sophisticated models of generation, transmission, distribution, and demand systems were required to plan capital expenditures, schedule power reserves, etc. © Springer International Publishing AG, part of Springer Nature 2018 L. Brackney et al., Building Energy Modeling with OpenStudio, https://doi.org/10.1007/978-3-319-77809-9_1
1
2
1 Introduction to Building Energy Modeling
Fig. 1.1 U.S. Energy consumption by sector with end use breakdowns. (Data source U.S. Energy Information Administration 2012)
That is not to say these (and other) sectors did not make use of mathematical models earlier – they did. This brief list is meant to point out significant historical events such as the space race, the 70s fuel crisis, advent of the personal computer, etc. that were significant drivers towards the adoption of rigorous mathematical modeling to meet market challenges. Fortuitously, these needs were enabled by improvements in the computing capability required to perform increasingly sophisticated analysis. So, what of the topic of this book, the built environment? In a 2012 U.S. Energy Information Administration survey buildings consumed nearly half of the 95.1 Quadrillion BTUs of energy produced in the United States. Figure 1.1 shows overall consumpt
Data Loading...