materials presented in chapter 9 are based on (Yang et al., 2014a).
1.3
Data
Data is essential to modeling and forecasting. Beside chapter4where typical meteorological year 3 data are used, all other chapters use Singapore data. The choices of datasets are explained in this section. I note that the choices of datasets do not constrain the appli- cabilities of the proposed methods. All methods proposed and discussed in this thesis are general.
1.3.1
Difference between resource assessment and forecasting
Solar irradiance measurements come from two complementary data sources (Vignola et al., 2012): (1) ground–based instruments and (2) remote sensing satellites. Before the popu- larization of the satellite–based methods (Perez et al.,2002), the ground–based monitoring networks are the primarily sources for solar resource assessment. For example, Fig. 1.4 shows the New Energy and Industrial Technology Development Organization (NEDO) me- teorological network http://app7.infoc.nedo.go.jp/. 834 ground–based weather stations are distributed spatially in Japan. Today, ground–based data are still used to adjust the biased irradiance estimates from satellite–based models (Escobar et al.,2014;Nonnenmacher et al., 2014; Polo et al., 2014).
Although the total number of monitoring stations in the NEDO network is generous, those stations are too sparse to fully capture the fast–changing cloud conditions. Some clouds experience creation, propagation and extinction within the spatial resolution. In fact, most of the networks today are sparse. Other examples of low spatial resolution networks include National Solar Radiation Data Base (NSRDB)http://rredc.nrel.gov/solar/ old_data/nsrdb/and the network in Brazil (Martins and Pereira,2011). Such networks are useful for resource assessment; they are not suitable for irradiance forecasting.
120 125 130 135 140 145 150 25 30 35 40 45 Admin area Lat/Lon grid Station 4000 4500 5000 5500 MJ m2⋅year
Fig. 1.4 Geographical locations of 834 weather stations in Japan. Each colored pixel denotes a station, with the color indicating the yearly insolation value in MJ/m2.
8and9. Singapore has a total land area of 714.3 km2; the main island of Singapore measures 50 km in the East–West direction and 26 km in the North–South direction. Fig.1.5shows the locations of these 25 stations. The monitoring network was completed in 2013 December. Therefore during the time of publication, not all stations were available3. Consequently,
partial networks are used in chapters 5and9. Nevertheless, chapter8demonstrates the full network.
1.3.2
Solar irradiance measuring instruments
The performance of a PV system is determined by two factors, namely, PV system efficiency and weather (Meydbray et al.,2012a). We are therefore interested in two types of measure- ments: (1) measurement of PV efficiency at reference conditions and (2) solar radiometric measurement.
3The paper (Yang et al., 2014a) is accepted in 2014 January. As one year worth of data are usually
required for publications, only stations built prior to 2013 January are utilized in that study, which is not the full network.
1.3 Data 12
Fig. 1.5 Locations of 25 irradiance monitoring stations in Singapore. Source: Google Maps. Recall the irradiance components shown in Fig. 1.3, there are three components (Idir,
Idif and Iglo) on a horizontal surface and four components (It,dir, It,dif, It,refl and It) on a tilted surface. These irradiance components can be collected using an instrument called pyranometer (directly or indirectly). Pyranometers are thermopile–based instruments that convert heat to an electrical signal which can then be recorded. A pyranometer is typically used to measure GHI (Iglo); if equipped with an additional shadow band to block the direct irradiance, it can also record DHI (Idif); DNI (Idir) can thus be calculated deterministically. Some pyranomters, such as the SPN1 Sunshine Pyranometer, have the capability of mea- suring GHI and DHI simultaneously. Occasionally for research purposes, pyranometers are used to measure the tilted global irradiance (It) as well. Instead of measuring the DHI using pyranometers with shadow bands, we can measure DNI using an instrument called pyrheliometer with a solar tracking system that aims the instrument at the sun. Pyra- nometers and pyrheliometers are used for solar radiometric measurements (Meydbray et al., 2012a,b;Yang et al.,2014b). The price range of industrial-grade pyranometers can reach a few thousand US dollars. Therefore, it is not economic to build sensor networks using such instruments for operational forecasting.
The alternative reference cell is a PV device, which converts a flux of photons directly into an electric current, working similarly to a PV module. Most reference cells are silicon– based; they are less accurate than thermopile–based devices (the major loss mechanisms are discussed in chapter7. Hundreds of reference cell types are available on the market and are cheaper than pyranometers (about two hundred US dollars). This type of sensor is therefore often used to measure the plane of array irradiance (It) at a PV site in order to assess the system performance (Meydbray et al., 2012a,b; Yang et al., 2014b); it is also common to install a collection (network) of reference cells within a PV site. To utilize the reference cell data, inverse transposition models (convert irradiance from tilt to horizon) are needed.
Two datasets are used in this thesis to demonstrate the irradiance conversion algorithms. The dataset collected at the first zero energy house in Singapore (FZEHS) is used in chap- ter 6. It consists of data from one pyranometer and two reference cells. The second dataset comes from the rooftop of SERIS, see chapter 7. It comprises data from two pyranometers and five reference cells. The datasets will be described in details in the respective chapters.