Generation of a reduced temperature year as an input data for building climatization purposes: an application for Palermo (Italy)

Transcription

1 Generation of a reduced temperature year as an input data for building climatization purposes: an application for Palermo (Italy) S. Barbaro, S. Costanzo & C. Giaconia Department of Energy and Environment Research, Palermo University, Italy Abstract An assessment of the thermal behaviour of a building-plant system in non-steady state conditions can be carried out by simulation programs using as their input the time series of weather data for the location considered. However, reliable data are available for a limited number of locations. For example, Palermo has no reference year deduced from hourly values for a long time period (25-30 years). The aim of the present paper is to set up a reference year of reduced temperature data for Palermo called RTY. The method adopted here was derived by Erbs et al. [l] using data from nine North American locations and has already been applied by some authors to 29 European cities having the test reference year (TRY) [5]; the methodology allows us to draw a reduced climatic sequence starting from widely available monthly average data of the air temperature and the amplitude of diurnal variations. Statistical analysis of data provided by three selected measurement stations in Palermo (urban, suburban and extra urban) for a time period of about 20 years allows us to determine the local typical meteorological year (TMY), that is the most probable occurring year, and the coefficients required by the proposed relation. A good accordance has been found comparing the generated temperature sequences RTY of the monthly mean days for the site with those referring to the year 1975, adopted here as the TMY. A better output is not obtained when introducing the coefficients available in the literature so the exposed methodology can be successfully extended to all the locations having an

2 54 Energv and the Environment appropriate set of temperature data. The reliability of the RTY proposed here has been tested through the comparison of the yearly energy demand, calculated using both temperature values of the RTY and the TRY for three building modules with different thermal properties. 1 Introduction Nowadays, many computer programs allow the determination of the energy performance of a given building requiring as an input the hourly weather data of the site. In the applications, two fundamental problems arise concerning the availability of reliable data, which is limited to a restricted number of sites in industrialized countries, and the time-expensive use of hourly data in the modelling process. In the early steps of the thermal design, when it is basic to check and compare many design hypotheses in a short time, a reduced set of meteorological data can be used. In particular, referring to the parameter "air temperature", a reference year of reduced data called a reduced temperature year (RTY) could be used; this is a set of 24x12 values of temperatures which may be assumed as the typical daily set for each month and introduced as an input with other climatic parameters. 2 Description of the methodology used With the aim of generating a RTY for the city of Palermo, a method deduced by Erbs and Klein [l] using data from nine North American locations and also applied by other authors to evaluate the daily and monthly energy needs of residential buildings in Canadian sites [2] was adopted. This method allows values of the hourly ambient temperature starting from the mean monthly temperature and amplitude of diurnal variations, to be calculated by means of the following equation: 4 T,, = Tm + 4, zak k=l where: Ta,h = hourly air temperature; T, = mean monthly air temperature; cos(t I- b, ) (1) t ak, bk = hour of the day; = coefficients related to given locations;

3 Energy and the Environment 55 A, = mean monthly value of the diurnal amplitude for the air temperature expressed as follows: n with: n = number of days in each month; T = maximum value of the air temperature for each day; T i i = minimum value of the air temperature for each day. Table 1: Available data for the considered meteorological stations. Station Astronomical Observatory A0 Altitude (s.1.m.) 31m Latitude 38006' Boccadifalco Airport BA 1 65 m 38" 06 ' I Punta Raisi Airport PRA I 5 m 1 38" 11 ' Using the same procedure as adopted by Erbs for nine U.S. locations, Cannistraro et al. [3] have calculated the coefficients ak and bk of the eqn (1) for the 29 European locations whose Test Reference Years (TRYs) were produced and published in 1985 by a special Commission of the European Community [5]. The reliability of these coefficients has been verified by the same authors, comparing the yearly energy demands for heating and refrigerating purposes for three modules with different thermal characteristics and located in the 29 European sites. With this aim, the SUNCODE-PC computer program has been employed utilizing as its input the two series of weather data formed by the TRY and those built-up by eqn (1). In the first step of this work, the European coefficients for Palermo have been taken into consideration. By using the values of hourly temperatures of the year 1999 recorded by a meteorological station located in the centre of Palermo, the corresponding hourly values of the mean monthly temperatures calculated by eqn (1) have been compared with the real temperature data given by the same station and for the same year. The results, reported in Figure 1, exhibit a high degree of difference among the measured and computed data. 3 Selection of the typical meteorological year Temperature max, rnin hourly The RTY for Palermo has been built-up by using the temperature data of three selected meteorological stations located downtown, in the suburb and near the city of Palermo (see Figure 2): the meteorological station of the Astronomic Observatory "S. G. Vaiana" whose daily values of mean maximum and

4 56 Energv and the Environment minimum temperatures for the period are available, the Boccadifalco Airport and Falcone Borsellino Airport meteorological stations, from both three hourly and daily values of maximum and minimum temperature referring to the period are available. I Jan Feb Mar Apr May Jun E Jut Aug L Sep oct Nov Dec Figure 1: Piazza Castelnuovo. A comparison between the real temperature data of the 1999 and the monthly mean daily temperatures computed by using and European coefficients. Figure 2: Distribution of selected weather stations.

5 Energy and the Environmmt 57 A first analysis and elaboration of the daily temperature data has been carried out to determine the local typical meteorological year (TMY) for every station. That is, the statistically most probably occurring real year. Applying the procedure proposed by Holmes and Hitchin [8], and also used by Wong and Ngan [9] for selecting the TMY of Hong Kong-based on the estimated standard deviation for the long-term average of a specific meteorological parameter-the year 1975 has been found as the TMY for all the three stations. Table 2: Coefficients ak and bk for the considered stations in Palermo. Astronomical Observatory ad bl= a2= b2= a3= b3=-1.g54 a4= b4= Boccadifalco Airport a1= a2= a3= a4= bl= b2= b3= b4= Punta Raisi Airport al=o a2= a3= a4= b,= b2= b3= b4= Generation of the RTY for Palermo In order to calculate the coefficients ak and bk for Palermo required in the eqn (l), we used the hourly temperature values of the 1975, which is the TMY determined above. By using the TMY (1975) data, for all months and for the three weather stations, we have calculated the average normalized diurnal temperature variation given by: Then, by means of a non-linear regression program, the new coefficients ak and bk for every station have been found and the results are reported in Table 2. Figure 3 shows the normalized average diurnal temperature obtained with the new coefficients of the station AO. A comparison between the two profiles of the monthly mean daily temperatures, computed by using new coefficients (RTY) and mean temperature values obtained from TMY (1975) for each station, are shown in Figures 4, 5 and 6. For all three stations we can see a good agreement between the TMY (1975) data and those calculated with the new coefficients.

6 58 Energv and the Environment a 5!.,.,.,.,.,.,.,.,.,. L., l6 l Hours Figure 3: Astronomical Observatory. Course of the normalized average diurnal temperatures p 20 U 1 3 '5! g l 0 5 Figure 4: Astronomical Observatory. A comparison of the courses of the monthly mean daily temperatures computed by using the new coefficients and the ~ ~~(1$75) data. ' Jan ' Feb ' Mar ' Apr ' May ' Jun ' Jul ' AUS ' Sep ' O d ' Nov ' Dec Figure 5: Punta Raisi. A comparison of the courses of the monthly mean daily temperatures computed by using the new coefficients and the TMY(1975) data.

7 Energy and the Environment C Jen ' Feb ' Mar ' &r ' May ' Jun ' Jur ' Aug ' Sep ' 0cl ' Nov ' Oec Figure 6: Boccadifalco. A comparison of the courses of the monthly mean daily temperatures computed by using the new coefficients and the TMY(1975) data. The results for the stations PRA and A0 show little difference between the RTY temperatures and those of the TMY (1975), generally below unity. In particular, for the meteorological station located in Punta Raisi, the use of new coefficients leads to an underestimation of the temperature values by about "C in the first hours of the day from January to April and from August to December, and of about 0.2"C in the central times of the day. For the other hours the temperatures result in an overestimation of less than 0.5"C. For the Astronomic Observatory station, Figure 4 shows that the RTY data leads to an underestimation of the temperature of about 0.6"C in the first hours of the day in fall and winter, while an overestimation at the same level occurs for the other seasons. In the middle hours the temperatures are generally underestimated by about "C. The course of the monthly average temperatures of the station BA (Figure 6) shows a difference between the TMY (1975) temperatures and those calculated by using the new coefficients that is higher than the other two weather stations. This is probably because for Boccadifalco station we used three-hourly temperature data to generate the normalized course. For every month, in fact, we have found a maximum error, always more than 1 C for 6.00 a.m. For the Boccadifalco station another comparison between the hourly temperature data of 1998 and 1999 and RTY data has been carried out. The Figures 7 and 8 show that the temperature values obtained by using eqn (1) with the new coefficients leads to an underestimation of "C in the period a.m. and in the period p.m., while an overestimation occurs in the same level in the period a.m. For all the others hours of the day the error is about of 0.5 "C.

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9 Energy and the Environmmt Applications The reliability of the RTY for Palermo has been verified through a comparison between the annual energy demands (winter heating and summer cooling) of three sample modules computed by using the RTY data and the annual energy demands obtained using the TMY (1975) data. For each of the three stations, the two series of weather data, the TMY and the RTY, have been used as inputs for the SUNCODE-PC [7] computer program. The simulation has been performed on three identical modules (bearing, dimensions and glazed area) and only different in regard to the thermal properties of the structural materials. The temperature set-point of the monozone modules was fixed at 20 C both in winter and summer. Figure 9 shows a sketch of the module used in the simulations. On the basis of a previous work of some of the authors [4], for the thermal properties of the envelope, we have assumed that the total thermal resistance must be in the range between 0.1 and 2.5 m2k/w and the total thermal capacity between 50 and 1500 J /~~K; subdividing both fields into three logarithmic parts and taking up the center value of each part, it is possible to single out three values of R,,C,, which can be assigned to three multilayer walls. For the sake of simplicity, we have built up three structural elements composed of three layers of materials usually employed in the building industry [6] and using the same structure for the walls, roof and floor. The thermal characteristics of the three chosen thermal structures are given in Table 3. Depending on the values of RJ&, walls (and modules equipped therewith) are termed "fast", "medium" and "slow". Table 3: Thermal characteristics of the materials used for the three modules. Slow R,tCtot=350 [hours] Heavy concrete plaster Sandstone Light concrete plaster l

10 62 Energv and the Environment Table 4: Relative percentage error for two weather stations. I Relative percentage error (RPE) I Astronomical Observatory Module Fast I Medium I Slow Boccadifalco Airport Module Fast I Medium I Slow In order to estimate the degree of accuracy for the adopted method, a comparison of annual energy demands for the three modules is given in terms of the relative per cent error: RTY, - TMY, RPE = 100 TMY, where RTY, and TMY, are the yearly values drawn from two different simulations by using, respectively, reduced and typical data. In Table 4 the relative percentage error (RPE) values for two stations and for different TMYs are reported. The Astronomic Observatory data, computed by using the TMY (1975), show that the yearly energy demands obtained with the RTY temperatures are underestimated by 5.7% for the fast module, 6.7% for the medium module and 5.2% for the slow module. For Boccadifalco the yearly energy demands have been computed by using temperature data recorded in the year The errors are generally low, with a maximum of 2.6% for the medium module, 2.4% for the fast module and 1.9% for the slow module. References [l] Erbs D., Klein S. & Beckman W. Estimation of degree-days and ambient temperature bin data from monthly-average temperatures. ASHRAE Journal, 25, pp , [2] Bahadori M. & Chamberlain M. A simplification of weather data to evaluate daily and monthly energy needs of residential buildings, Solar Energy, 36, No. 6, pp ,1986. [3] Cannistraro G., Giaconia C., Pietrafesa M. & Rizzo G. Reduced weather data for building climatization and application to 29 European locations, Energy, 20, No. 7, pp , [4] Barbaro S., Giaconia C., Orioli A. & Trapani S. Applicabilith in regime vario della parete non omogenea termicamente equivalente, CDA, 36, No. 8, pp ,1992. [5] CEC, Test Reference Years TRY, Weather Data Sets for Computer Sirnulations of Solar Energy and Energy Consurnptions in Buildings. Directorate General XI1 for Science, Research and Development, 1985.

11 Energy and the Environment 63 [6] C.N.R. Repertorio delle caratteristiche termofisiche dei componenti edilizi opachi e trasparenti. Progetto Finalizzato Energetica, Milano PEG, 1982 [7] Palmiter L. & Wheeling T. SUN COD^?, Ecotope, Seattle, WA, [S] Holmes M.J. & Hitchin E.R. An example year for the calculation of energy demand in buildings, Build. Sew. Eng., 45, No. 9, [9] Wong W.L. & Ngan K.H. Technical note. Selection of an "Example Weather Year" for Hong Kong, Energy and Buildings, No. 19, pp , 1993.

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