Category Terms and Definitions

Color Plates

NREL

 

Best Research-Ce Efficiencies

 

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Thin-Film Technologies

 

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image004
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VThin-film crystal

 

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VThin-film crystal

 

SunPower

(96x)

 

^28

 

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о 24

 

UNSW.

 

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UNSW

 

Stanford

 

Variant UNSW

 

—-”■“■’’’University

So. Florida’ ARCO. Boeing

 

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T^^Photon Energy

,—“^ARCO і |nitoH c

 

Matsushita

 

Monosolar

 

Solarex

 

1 University of Maine

 

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(a)

 

Polycrystalline PV modules

 

Fixed-tilt PV arrays

 

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(c) 1-Axis tracking PV arrays
(d) Thin-film PV roof shingles

(e) Concentrating PV on 2-axis tracker (f) Building integrate...

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Biography

Tomas Cebecauer is an expert in solar resource assessment, satellite remote sensing, geoinformatics, meteorology, and photovoltaic electricity modelling. He has received PhD in geography and geoinformatics, and is co-author of more than 70 scientific publications. He is one of authors of the decision support online system PVGIS, which notably contributed to development of photovoltaics in Europe.

He is technical director of GeoModel Solar. He has key role in technical development and operation of global online system SolarGIS, which delivers solar resource and meteo data, and software services for planning, monitoring and forecasting of solar power. Tomas Cebecauer is principal author of the high-accuracy satellite-based solar model implemented in SolarGIS...

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Solar Energy Forecasting and Resource Assessment

Solar power is widely acknowledged to be the fastest-growing energy industry in the world. As technological improvements steadily progress toward the erasure of cost and efficiency barriers, two issues are coming to the forefront of public discourse on solar energy—variability and reliability. Solar-project developers and their financiers are increasingly scrutinizing the accuracy of long-term resource projections; as well, grid operators’ concerns about variable short-term power generation are growing. These issues have made the field of solar forecasting and resource assessment pivotally important, and to date, there has been no comprehensive single text devoted to it...

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STOCHASTIC-LEARNING USING EXOGENOUS VARIABLES: THE NATIONAL DIGITAL FORECASTING DATABASE

In this last section, we present some results for the forecasting of solar irra – diance for longer forecasting horizons (>24 h) using exogenous variables. For such time horizons, models based solely on imaging (either local or remote) are not applicable, and we need to resort to NWP or fully stochastic models. NWP models solve the physical laws of thermodynamics using conservation prin­ciples on a discrete spatial grid for chosen domains (See Chapter 12). Purely stochastic-learning models rely on the approaches described in this chapter...

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Deterministic Results

We compare 1 min averaged forecast results for DNI for 3-15 min time hori­zons. A forecast value is computed for each cloud fraction Xi and each time horizon Th, j :

Ух, (t + Th, j) = DNIcs(t + Th, j) • (1 – Xi) where DNIcs (t) is the clear-sky model for DNI.

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FIGURE 15.11 Main image-processing steps. Left: original 8-bit image in grayscale; middle: image projected to a rectangular grid using image-to-sky mapping and velocity fields computed by the PIV algorithm; right: cloud decision image. Notice the 7 “ladder” elements used for the forecasting and how they are aligned with mean cloud velocity.

TABLE 15.3 RMSE Computed for 3-

-15 min Forecasting Horizons (kW/m2)

N

Forecast

horizon

Dull

Persistence

X1

X2

X3

X4

X5

X6

Improvement w. r.t. Dull

persi...

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Image Processing

Cloud-cover information needs to be extracted from sky images and incorpo­rated in the forecasting model. The image processing—designated as wind – ladder sector—comprises the following steps:

Step 1. The image is converted from a spherical to a rectangular grid.

Step 2. Pairs of images are used in a particle-image velocimetry (PIV) algorithm that determines the apparent velocity field for the cloud motion.

Step 3. A representative velocity vector is chosen by applying k-means clus­tering to the distribution of velocity vectors.

Step 4. Each pixel in the image is classified as cloud or clear-sky.

Step 5...

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SKY-IMAGING DATA AS EXOGENOUS VARIABLES FOR SOLAR FORECASTS

When considering exogenous variables for the forecasting of solar irradiance and related phenomena, the most important one is sky condition and, in particular, cloud cover. Cloud-cover information can be obtained from satel­lite images (e. g., http://www. goes. noaa. gov/browsw. html) or from ground – based sky imagers—usually a CCD camera taking pictures of a convex mirror that reflects the entire sky, or one fitted with a fisheye lens pointed upward. Remote sensing provides images with very large fields of view (worldwide if necessary) but with medium spatial and temporal resolutions, whereas sky imaging provides images of high spatial and temporal resolution for small fields of view (usually not more than 10-20 km)...

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Quantitative Performance of ARIMA, kNN, ANN, GA/ANN

Models built on the historical data of 2009 and 2010 (the shaded area in Figure 15.7) are applied to the 2011 data (unshaded area) without modifications or retraining. Given that, as seen in Figure 15.1, there is a strong seasonality in power-output variability, we expect a strong seasonality in the accuracy of the predicted values as well. To study the influence of this factor, we consider three solar-variability seasons, or periods, that are subsets of the total error- evaluation dataset. The three periods are defined based on the solar- variability study summarized in Figure 15.1 as

• High variability, from January 1, 2011, to April 30, 2011 (P1)

• Medium variability, from May 1, 2011, to June 30, 2011 (P2)

• Low variability, from July 1, 2011, to August 15, 2011 (P3)

All of the s...

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