Index of data ngquocbinh file

Index of data ngquocbinh file tài liệu, giáo án, bài giảng , luận văn, luận án, đồ án, bài tập lớn về tất cả các lĩnh vự...

I NTERNATIONAL J OURNAL OF E NERGY AND E NVIRONMENT Volume 3, Issue 2, 2012 pp.195-208 Journal homepage: www.IJEE.IEEFoundation.org ISSN 2076-2895 (Print), ISSN 2076-2909 (Online) ©2012 International Energy & Environment Foundation. All rights reserved. Evaluation of different weather files on energy analysis of buildings Apostolos Michopoulos, Vassiliki Voulgari, Konstantinos Papakostas, Nikolas Kyriakis Process Equipment Design Laboratory, Mechanical Engineering Department, Aristotle University of Thessaloniki – POB 487 – 541 24 Thessaloniki – Greece. Abstract The building energy demand simulation tools consist the compass of the roadmap towards the energy efficient building. Apart from the software itself, the result of the simulation strongly depends on the degree the data used represent the actual situation, among which the climate data of the area are a key factor. In this work, the energy demand of a large building complex is estimated, using the widely accepted EnergyPlus building simulation software in combination with two, also widely accepted, weather files. The simulation results for heating are compared with the actual fuel consumption of a three-year operation period. The comparison reveals that the weather file and the size of the simulation domain significantly affect the simulation representativeness. Copyright © 2012 International Energy and Environment Foundation - All rights reserved. Keywords: Building simulation; Energy consumption; Climate data; Weather files; Typical meteorological years. 1. Introduction The share of total final energy consumed by the household and service sectors in the European Union (EU-27) was reduced from 42.5% in 1996 to 37.2% in 2008, while the corresponding figures for Greece were 35.5% (1996) and 34.7% (2008) [1, 2]. Space heating and cooling are the major energy consumers in buildings, accounting for about 63% to 70% (residential and tertiary sector, respectively) in Greece, the remaining 37 to 30% being used for space illumination, appliances and electromechanical equipment operation [3, 4]. Obviously, the energy consumption is directly related to both the operational cost of buildings and to their negative effect on the environment. There is therefore a growing interest on the energy efficient design, significantly intensified by the implementation of the European Directive 2002/91/EC [5] and the recast of it, European Directive 2010/31/EC [6], concerning the energy performance of buildings. To this direction, taking into account the aforementioned fact that the major energy consumers of a building are the heating and cooling systems, the evaluation of the relevant energy demands becomes the first step towards reducing the corresponding energy consumption. To this purpose, the long-term simulation of the building and of its systems is required, with the dynamic simulation programs being the main tool for the energy performance prediction [7-10]. A number of such tools has been developed over the last 20 years, used for both the design of new buildings and for the improvement of existing ones [11-14]. The building-specific data required for the prediction include details about: (1) construction (design and materials), (2) design and control characteristics of the HVAC system and (3) usage patterns. These data, International Journal of Energy and Environment (IJEE), Volume 3, Issue 2, 2012, pp.195-208 ISSN 2076-2895 (Print), ISSN 2076-2909 (Online) ©2012 International Energy & Environment Foundation. All rights reserved. 196 combined with detailed weather Bài tập Lâm Sản Ngồi Gỗ Nhóm 2:Sản phẩm từ tre nứa BỘ GIÁO DỤC VÀ ĐÀO TẠO TRƯỜNG ĐẠI HỌC NƠNG LÂM THÀNH PHỐ HỒ CHÍ MINH KHOA LÂM NGHIỆP MƠN LÂM SẢN NGỒI GỖ NỘI DUNG TRÌNH BÀY: Sưu tầm tất sản phẩm dân dụng chế biến từ Tre nứa Phân tích cơng đoạn chế biến chúng, giá trị lợi việc chế biến việc quản lý tài nguyên Giảng viên: Nguyễn Quốc Bình Nhóm 2: Trần Văn Cường Lê Tiến Dũng Trịnh Hồng Quyên Nguyễn Thị Thu Nguyễn Thị Bích Trâm TP HỒ CHÍ MINH, THÁNG 10 NĂM 2008 -1- Bài tập Lâm Sản Ngồi Gỗ Nhóm 2:Sản phẩm từ tre nứa I Đặt vấn đề: Hiện nay, thị trường có nhiều sản phẩm từ lâm sản ngồi gỗ có giá trị Đặc biệt, sản phẩm dân dụng từ tre nứa đa dạng phong phú người dân sản xuất nhằm mục đích tiêu dùng xuất Chúng mang lại giá trị kinh tế , giá trị xã hội, giá trị môi trường, lợi việc chế biến việc quản lý tài nguyên rừng Tình hình xuất mặt hàng mây tre, lá, thảm, sơn mài kỳ cuối tháng đầu tháng 9/2008 Theo số liệu thống kê sơ bộ, kim ngạch xuất nhóm mặt hàng mây tre lá, thảm, sơn mài Việt Nam kỳ (từ ngày24/08 đến ngày 04/09) đạt 7,5 triệu USD, tăng nhẹ so với kỳ trước Các thị trường xuất chủ yếu kỳ là: Mỹ, Nhật Bản, Đức, Ôxtrâylia, Pháp, Italia, HàLan, Nga, BaLan, Tây Ban Nha, Đài Loan, Hàn Quốc, Anh, Ấn Độ, Mêhicô Trong thị trường xuất chủ yếu nhóm hàng mây tre lá, thảm, sơn mài Việt Nam kỳ , kim ngạch xuất vào thị trường Mỹ đạt cao với 1,1 triệu USD, giảm 3,6% so với kỳ trước Sản phẩm xuất vào thị trường Đức kỳ là: Bát, đĩa mây tren đan, giỏ mây, khay mây, lẵng tre, hộp mây,mành tre, nón Kế đến thị trường Nhật Bản, với kim ngạch xuất kỳ đạt 973 nghìn USD, tăng 12% sovới kỳ trước Các sản phẩm xuất vào thị trường Đức kỳ là: ghế mây, bát đĩa mây tre đan, chậu đan lục bình, rổ đan lục bình; hộp mây tre đan, khay lục bình Các lơ hàng xuất đạt kim ngạch cao kỳ là: khay tre xuất vào thị trường Hà Lan trị giá 78 nghìn USD; bát đĩa tre xuất vào thị trường Mỹ trị giá 65 nghìn USD; hàng tre, nứa, mây, guột, cói, giang xuất vào thị trường Nga trị giá 49 nghìn USD Một số lơ hàng xuất có đơn giá cao tuần là: bàn tre xuất vào thị trường Ơxtrâylia có giá dao động từ 222,36 USD/chiếc đến 595 USD/chiếc – FOB; Mành tre xuất vào thị trường Mỹ có giá dao động từ 180,3 USD/cuộn đến 220,75 USD/cuộn – FOB Ghế tre xuất vào thị trường có giá 188 USD/chiếc – FOB, tủ sơn mài cốt tre xuất vào thị trường Mỹ có giá 150 USD/chiếc – FOB (Theo Trung tâm Thông tin Công nghiệp Thương mại) Tuy nhiên, thực trạng nghiêm trọng khoảng 35-42% sở phải sản xuất cầm chừng tệ hại đối đầu với nguy đóng cửa thiếu khơng chủ động nguyên liệu Hằng năm nhập lượng nguyên liệu lớn tre với giá cao nước từ 15-20% Tài nguyên tre nước có nhiều, cạn kiệt dần khai thác bất hợp lý, khai thác mức nơi điều kiện đường sá cho phép, làm cho số lượng chất lượng nguyên liệu giảm trầm trọng -2- Bài tập Lâm Sản Ngồi Gỗ Nhóm 2:Sản phẩm từ tre nứa Ở nước ta chưa có nhiều nghiên cứu giống mà quan trọng xây dựng sưu tập giống tre thương mại Đặc biệt nghiên cứu chế biến tổng hợp - để tăng tỷ lệ sử dụng nguyên liệu vốn thấp – nhu cầu thiết Một vấn đề hàng mỹ nghệ tre đan mặt hàng thiết yếu ngày cầu thị trường phụ thuộc nhiều vào tinh tế, tính thẩm mỹ, đa dạng giá sản phẩm Do cần phải có tính sáng tạo thiết kế sản phẩm để kích thích tính hiếu kỳ khách hàng tính ham muốn phải sở hữu sản phẩm tre đan Bên cạnh đầu tư vào khoa học công nghệ giống, trồng, khai thác đa dạng hoá chế biến, việc đầu tư nghiên cứu, đào tạo thiết kế sản phẩm, đa dạng hoá sản phẩm giải pháp quan trọng việc xác định chiến lược phát triển ngành hàng tre đan Việt II Giới thiệu sản phẩm dân dụng từ tre nứa:  Dùng thân tre làm giấy thông dụng loại giấy cao cấp  Sản xuất ván nhân tạo từ tre: “ván sợi ép, ván dăm, ván dán từ tre”  Chiết triacidaceticxenluloesther từ tre: Chất dùng công nghệ chế tạo phim ảnh, sợi nhân tạo, đầu lọc thuốc lá, màng phản thấm, chất dẻo, sơn, chất cách điện, chất phụ trợ thuốc trừ sâu  Sản xuất đũa dụng cụ gia đình Tre gắn liền với sống ngày, sản xuất giỏ tre, chiếu tre, thang tre, thùng tre, tăm tre, rổ tre…  Công nghệ phẩm tre Công nghệ đan tre làng nghề thủ công Việt Nam  Nghề điêu khắc tre Như: khảm gỗ đỏ, mành tre vàng nhã  Các dụng cụ văn nghệ thể thao tre: nhạc cụ âm nhạc: đàn T’rưng, đàn Klong Pút, sáo trúc…, dụng cụ thể dục: xà đơn, xà kép, vợt cầu lơng  Ơ dù, quạt mành tre  Chế thuốc chữa viêm, chảy máu mũi, nôn mửa đọt tre chữa viêm sốt, viêm họng Ngồi ra, người ta chiết từ tinh tre xuất nước  Thảm sàn khắc chữ lên tre Lợi dụng đặc tính thấm nước, phân ly sợi người ta chế dây thừng Ngày nay, người ta bệnh thành thảm có hiệu kinh tế  Sử dụng bột trúc: người ta nghiền tre thành bột Bột tre dùng công nghệ chế tạo chất hấp phụ, chất trang trí bề mặt giá trị -3- Bài tập Lâm Sản Ngồi Gỗ Nhóm 2:Sản phẩm từ tre nứa  Chất bổ sung: Do tính hóa học tre ổn định làm chất bổ sung công nghệ phẩm vải sơn dầu, cột điện, tạo hình mơ hình xi măng Khi chế tạo dụng cụ đồ điện dùng làm bột tre làm chát cách điện giảm tính dẫn điện Hiện nước ta dùng bột tre chế tạo gỗ cách điện cao cấp chế dụng ... Chapter 2 Building Tables of Data After completing this chapter, you will be able to:  Understand the ADO.NET classes used to create tables  Create strongly typed columns within a table  Indicate the primary key for a table  Design a table graphically or in code The focus of all data in ADO.NET is the table—or more correctly, the DataTable. This class, located at System.Data.DataTable, defines a single table in which strongly typed column definitions and runtime data rows appear. By itself, a DataTable isn’t very interesting; it’s just a memory-based repository for data. It becomes useful only when you start employing ADO.NET and standard .NET Framework methods and tools to process the data stored in each table and data row. Note Some of the exercises in this chapter use the same sample project, a tool that exposes the structure of a DataTable. Although you can run the application after each exercise, the expected results for the full application might not appear until you complete all related exercises in the chapter. Implementing Tables As with everything else in .NET, tables in ADO.NET exist as instantiated objects. Whether hand-written by you, produced by dragging and dropping items in the development envi- ronment, or generated by one of the Visual Studio tools, the ADO.NET code you include in your application exists to create and manage DataTable objects and other related objects. Logical and Physical Table Implementations ADO.NET’s DataTable object represents a logical implementation of a table of data. When you visualize the data values within the table, you have an image of a spreadsheet-like table, with distinct cells for each text, numeric, date/time, or other type of value. Dwonloaded from: iDATA.ws 18 The physical implementation of a DataTable object is somewhat different. Instead of one large grid layout, ADO.NET maintains tabular data as a collection of collections. Each DataTable object contains a collection of ordered rows, each existing as an instance of a DataRow object. Each row contains its own collection of items that holds the row’s (and ulti- mately the table’s) actual data values. A set of column definitions exists separately from the actual column values, although the definitions influence the values. Figure 2-1 shows the difference between the logical and physical structures of a data table. Logical Implementation Physical Implementation ID First Name Birth Date 0 11 George 8/3/1985 1 96 Annette 2/12/2003 Row 0 1 2 ID 11 96 27 First Name George Annette Toru Birth Date 8/3/1985 2/12/2003 12/30/1948 2 27 Toru 12/30/1948 FIGURE 2-1 Logical and physical table layouts. The DataTable Class The three main classes that make up a data table are DataTable, DataColumn, and DataRow. As expected, these classes define a table, its columns, and its data rows, respectively. The main discussion for the DataRow class appears in Chapter 3, “Storing Data in Memory.” To define a table, create a new DataTable object, optionally supplying a table name. C# System.Data.DataTable unnamedTable = new System.Data.DataTable(); System.Data.DataTable namedTable = new System.Data.DataTable("Customer"); Visual Basic Dim unnamedTable As New System.Data.DataTable() Dim namedTable As New System.Data.DataTable("Customer") Dwonloaded from: iDATA.ws Chapter 2 Building Tables of Data 19 After you create a DataTable, you can modify its TableName property and other relevant properties as needed. Note Both Visual Basic and C# include features that let you use namespace elements as if they were globally named elements. Visual Basic accomplishes this on a file-by-file basis with the Imports keyword; C# includes the Using keyword for the same purpose. Visual Basic also includes Sample page from NUMERICAL RECIPES IN C: THE ART OF SCIENTIFIC COMPUTING (ISBN 0-521-43108-5) Copyright (C) 1988-1992 by Cambridge University Press.Programs Copyright (C) 1988-1992 by Numerical Recipes Software. Permission is granted for internet users to make one paper copy for their own personal use. Further reproduction, or any copying of machine- readable files (including this one) to any servercomputer, is strictly prohibited. To order Numerical Recipes books,diskettes, or CDROMs visit website http://www.nr.com or call 1-800-872-7423 (North America only),or send email to trade@cup.cam.ac.uk (outside North America). Chapter 14. Statistical Description of Data 14.0 Introduction In this chapter and the next, the concept of data enters the discussion more prominently than before. Dataconsist of numbers, of course. Butthese numbers are fed intothecomputer, not produced by it. These are numbers to be treated with considerable respect, neither to be tampered with, nor subjected to a numerical process whose character you do not completely understand. You are well advised to acquire a reverence for data that is rather different from the “sporty” attitude that is sometimes allowable, or even commendable, in other numerical tasks. The analysis of data inevitably involves some trafficking with the field of statistics, that gray area which is not quite a branch of mathematics — and just as surely not quite a branch of science. In the following sections, you will repeatedly encounter the following paradigm: • apply some formula to the data to compute “a statistic” • compute where the value of that statistic falls in a probability distribution that is computed on the basis of some “null hypothesis” • if it falls in a very unlikely spot, way out on a tail of the distribution, conclude that the null hypothesis is false for your data set If a statistic falls in a reasonable part of the distribution, you must not make the mistake of concluding that the null hypothesis is “verified” or “proved.” That is the curse of statistics, that it can never prove things, only disprove them! At best, you can substantiate a hypothesis by ruling out, statistically, a whole long list of competing hypotheses, every one that has ever been proposed. After a while your adversaries and competitors will give up trying to think of alternative hypotheses, or else they will grow old and die, and then your hypothesis will become accepted. Sounds crazy, we know, but that’s how science works! In this book we make a somewhat arbitrary distinction between data analysis procedures that are model-independent and those that are model-dependent.Inthe former category, we include so-called descriptive statistics that characterize a data set in general terms: its mean, variance, and so on. We also include statistical tests that seek to establish the “sameness” or “differentness” of two or more data sets, or that seek to establish and measure a degree of correlation between two data sets. These subjects are discussed in this chapter. 609 610 Chapter 14. Statistical Description of Data Sample page from NUMERICAL RECIPES IN C: THE ART OF SCIENTIFIC COMPUTING (ISBN 0-521-43108-5) Copyright (C) 1988-1992 by Cambridge University Press.Programs Copyright (C) 1988-1992 by Numerical Recipes Software. Permission is granted for internet users to make one paper copy for their own personal use. Further reproduction, or any copying of machine- readable files (including this one) to any servercomputer, is strictly prohibited. To order Numerical Recipes books,diskettes, or CDROMs visit website http://www.nr.com or call 1-800-872-7423 (North America only),or send email to trade@cup.cam.ac.uk (outside North 610 Chapter 14. Statistical Description of Data Sample page from NUMERICAL RECIPES IN C: THE ART OF SCIENTIFIC COMPUTING (ISBN 0-521-43108-5) Copyright (C) 1988-1992 by Cambridge University Press.Programs Copyright (C) 1988-1992 by Numerical Recipes Software. Permission is granted for internet users to make one paper copy for their own personal use. Further reproduction, or any copying of machine- readable files (including this one) to any servercomputer, is strictly prohibited. To order Numerical Recipes books,diskettes, or CDROMs visit website http://www.nr.com or call 1-800-872-7423 (North America only),or send email to trade@cup.cam.ac.uk (outside North America). In the other category, model-dependent statistics, we lump the whole subject of fitting data to a theory, parameter estimation, least-squares fits, and so on. Those subjects are introduced in Chapter 15. Section 14.1 deals with so-called measures of central tendency, the moments of a distribution,the median and mode. In §14.2 we learn to test whether different data sets are drawn from distributions with different values of these measures of central tendency. This leads naturally, in §14.3, to the more general question of whether two distributions can be shown to be (significantly) different. In §14.4–§14.7, we deal with measures of association for two distributions. We want to determine whether two variables are “correlated” or “dependent” on one another. If they are, we want to characterize the degree of correlation in some simple ways. The distinction between parametric and nonparametric (rank) methods is emphasized. Section 14.8 introduces the concept of data smoothing, and discusses the particular case of Savitzky-Golay smoothing filters. This chapter draws mathematically on the material on special functions that was presented in Chapter 6, especially §6.1–§6.4. You may wish, at this point, to review those sections. CITED REFERENCES AND FURTHER READING: Bevington, P.R. 1969, Data Reduction and Error Analysis for the Physical Sciences (New York: McGraw-Hill). Stuart, A., and Ord, J.K. 1987, Kendall’s Advanced Theory of Statistics , 5th ed. (London: Griffin and Co.) [previous eds. published as Kendall, M., and Stuart, A., The Advanced Theory of Statistics ]. Norusis, M.J. 1982, SPSS Introductory Guide: Basic Statistics and Operations ; and 1985, SPSS- X Advanced Statistics Guide (New York: McGraw-Hill). Dunn, O.J., and Clark, V.A. 1974, Applied Statistics: Analysis of Variance and Regression (New York: Wiley). 14.1 Moments of a Distribution: Mean, Variance, Skewness, and So Forth When aset of values has a sufficientlystrongcentral tendency, that is, a tendency to cluster around some particular value, then it may be useful to characterize the set by a few numbers that are related to its moments, the sums of integer powers of the values. Best known is the mean of the values x 1 , .,x N , x= 1 N N  j=1 x j (14.1.1) which estimates the value around which central clustering occurs. Note the use of an overbar to denote the mean; angle brackets are an equally common notation,e.g., x. You should be aware that the mean is not the only available estimator of this 14.1 Moments of a Distribution: Mean, Variance, Skewness 611 Sample page from NUMERICAL RECIPES IN C: THE ART OF SCIENTIFIC COMPUTING (ISBN 0-521-43108-5) Copyright (C) 1988-1992 by Cambridge University Press.Programs Copyright (C) 1988-1992 by Numerical Recipes Software. Permission is granted for internet users to make one paper copy for their own personal use. Further reproduction, or any copying of machine- readable files (including this one) to any servercomputer, is strictly prohibited. To order Numerical Recipes books,diskettes, or CDROMs visit website http://www.nr.com or 14.2 Do Two Distributions Have the Same Means or Variances? 615 Sample page from NUMERICAL RECIPES IN C: THE ART OF SCIENTIFIC COMPUTING (ISBN 0-521-43108-5) Copyright (C) 1988-1992 by Cambridge University Press.Programs Copyright (C) 1988-1992 by Numerical Recipes Software. Permission is granted for internet users to make one paper copy for their own personal use. Further reproduction, or any copying of machine- readable files (including this one) to any servercomputer, is strictly prohibited. To order Numerical Recipes books,diskettes, or CDROMs visit website http://www.nr.com or call 1-800-872-7423 (North America only),or send email to trade@cup.cam.ac.uk (outside North America). that this is wasteful, since it yields much more information than just the median (e.g., the upper and lower quartile points, the deciles, etc.). In fact, we saw in §8.5 that the element x (N+1)/2 can be located in of order N operations. Consult that section for routines. The mode of a probability distribution function p(x) is the value of x where it takes on amaximum value. The modeisuseful primarilywhenthereisa single, sharp maximum, in which case it estimates the central value. Occasionally, a distribution will be bimodal, with two relative maxima; then one may wish to know the two modes individually. Note that, in such cases, the mean and median are not very useful, since they will give only a “compromise” value between the two peaks. CITED REFERENCES AND FURTHER READING: Bevington, P.R. 1969, Data Reduction and Error Analysis for the Physical Sciences (New York: McGraw-Hill), Chapter 2. Stuart, A., and Ord, J.K. 1987, Kendall’s Advanced Theory of Statistics , 5th ed. (London: Griffin and Co.) [previous eds. published as Kendall, M., and Stuart, A., The Advanced Theory of Statistics ], vol. 1, § 10.15 Norusis, M.J. 1982, SPSS Introductory Guide: Basic Statistics and Operations ; and 1985, SPSS- X Advanced Statistics Guide (New York: McGraw-Hill). Chan, T.F., Golub, G.H., and LeVeque, R.J. 1983, American Statistician , vol. 37, pp. 242–247. [1] Cram´er, H. 1946, Mathematical Methods of Statistics (Princeton: Princeton University Press), § 15.10. [2] 14.2 Do Two Distributions Have the Same Means or Variances? Not uncommonly we want to know whether two distributions have the same mean. For example, a first set of measured values may have been gathered before some event, a second set after it. We want to know whether the event, a “treatment” or a “change in a control parameter,” made a difference. Our first thought is to ask “how many standard deviations” one sample mean is from the other. That number may in fact be a useful thing to know. It does relate to the strength or “importance” of a difference of means if that difference is genuine. However, by itself, it says nothing about whether the difference is genuine, that is, statistically significant. A difference of means can be very small compared to the standard deviation, and yet very significant, if the number of data points is large. Conversely, a difference may be moderately large but not significant, if the data are sparse. We will be meeting these distinct concepts of strength and significance several times in the next few sections. A quantity that measures the significance of a difference of means is not the number of standard deviations that they are apart, but the number of so-called standard errors that they are apart. The standard error of a set of values measures the accuracy with which the sample mean estimates the population (or “true”) mean. Typically the standard error is equal to the sample’s standard deviation divided by the square root of the number of points in the sample. 616 Chapter 14. Statistical Description of Data Sample page from NUMERICAL RECIPES IN C: THE ART OF