An anisotropic hourly diffuse radiation model for sloping surfaces: Description, performance validation, site dependency evaluation

Perez, Richard; Stewart, Ronald E.; Arbogast, Chris; Seals, R. K.; Scott, Jason D.

doi:10.1016/0038-092x(86)90013-7

Cited by 274 publications

(79 citation statements)

References 6 publications

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“…Although both sources used similar algorithms, either the original or the enhanced Perez model [31][32][33] to calculate solar radiation, Weather Analytics data sets lack high quality aerosol data which can lead to a high bias of modeled solar radiation under certain cloudy / high humidity conditions. This explains that, in Table 4, Miami (a humid climate) and San Francisco (with frequent morning fog) have the greatest deviations in solar radiation between the average AMYs and the TMY3.…”

Section: Discrepancies Of Weather Data From Different Sources and Difmentioning

confidence: 99%

A fresh look at weather impact on peak electricity demand and energy use of buildings using 30-year actual weather data

2013

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Buildings consume more than one third of the world's total primary energy. Weather plays a unique and significant role as it directly affects the thermal loads and thus energy performance of buildings. The traditional simulated energy performance using Typical Meteorological Year (TMY) weather data represents the building performance for a typical year, but not necessarily the average or typical long-term performance as buildings with different energy systems and designs respond differently to weather changes. Furthermore, the single-year TMY simulations do not provide a range of results that capture yearly variations due to changing weather, which is important for building energy management, and for performing risk assessments of energy efficiency investments. This paper employs large-scale building simulation (a total of 3162 runs) to study the weather impact on peak electricity demand and energy use with the 30-year (1980 to 2009) Actual Meteorological Year (AMY) weather data for three types of office buildings at two design efficiency levels, across all 17 ASHRAE climate zones. The simulated results using the AMY data are compared to those from the TMY3 data to determine and analyze the differences. Besides further demonstration, as done by other studies, that actual weather has a significant impact on both the peak electricity demand and energy use of buildings, the main findings from the current study include: 1) annual weather variation has a greater impact on the peak electricity demand than it does on energy use in buildings; 2) the simulated energy use using the TMY3 weather data is not necessarily representative of the average energy use over a long period, and the TMY3 results can be significantly higher or lower than those from the AMY data; 3) the weather impact is greater for buildings in colder climates than warmer climates; 4) the weather impact on the medium-sized office building was the greatest, followed by the large office and then the small office; and 5) simulated energy savings and peak demand reduction by energy conservation measures using the TMY3 weather data can be significantly underestimated or overestimated. It is crucial to run multi-decade simulations with AMY weather data to fully assess the impact of weather on the long-term performance of buildings, and to evaluate the energy savings potential of energy conservation measures for new and existing buildings from a life cycle perspective.

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Section: Discrepancies Of Weather Data From Different Sources and Difmentioning

confidence: 99%

A fresh look at weather impact on peak electricity demand and energy use of buildings using 30-year actual weather data

2013

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“…From the models of Hay (1979) [10], Gueymard (1987) [11], Skartveit et al (1985) [12] and Perez et al (1986) [13], we can calculate the global and diffuse radiation on an inclined (slope) surface. Hay's model distinguishes from the three other models by its simple structure and the small number of input values.…”

Section: Methodsmentioning

confidence: 99%

Modeling of Diffuse Solar Radiation and Impact of Complex Terrain over Pakistan Using RS/GIS

Sultan¹,

Wu²,

Ahmad³

et al. 2014

JGIS

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Diffuse solar radiation is subject to the combined influence of ground and sky factors, such as topography, geography of the area and cloud cover. This study attempts to quantify the impacts of topography, sky factors and the cloud cover on the distribution of diffuse solar radiation over Pakistan. Distributed modeling approach by considering anisotropy scattering mechanism was adopted. Digital elevation model and observed data are used to derive average monthly diffuse solar radiation values over the rugged terrains of Pakistan. Extraterrestrial solar radiation model, sky view factor model (openness model) and digital elevation model (DEM) are applied to investigate the impacts of ground factors, while diffuse solar radiation model for horizontal surface was considered for sky factors. Furthermore, corrected MODIS cloud fraction data are incorporated using GIS plat form. Results show that the highest amount of diffused solar radiation occurs during the monsoon months along the eastern side of the River Indus, when the sky is covered by clouds of various heights and densities. The variation due to topography is evident in mountainous areas, particularly in the North Pakistan and over the Baluchistan Plateau.

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“…Collares-Pereira and Rabl 9,10 Horizontal to tilted irradiation Whillier, Liu and Jordan 11,12 Hourly irradiance from daily values Pérez 13,14 Diffuse irradiance Lorenzo 15 Hourly ambient temperatures from daily min and max values Single-exponential [16][17] Current and cell voltage Copetti 8 Battery's charge and discharge voltages Jantsch and Schmid 18 Inverter's efficiency Some particular features of this application deserve further comment. On the one hand, the PV generator model, 16 valid only for crystalline Si technology, assumes the effect of parallel resistance to be negligible, while the series resistance R SG is related to the rated fill factor, FF * , by:…”

Section: Modelling the Battery Voltage Distributionmentioning

confidence: 99%

The battery voltage distribution: a possible tool for surveying the state of health of stand-alone PV systems

Lorenzo

Labed

2005

Prog. Photovolt: Res. Appl.

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Degradation phenomena can remain unnoticed over long periods in stand-alone PV systems, before eventually causing service interruption. Problems include battery capacity reduction because of ageing, PV array capacity reduction because of wiring corrosion or excessive dirtiness, and mis-adjustment of controller set-points. This underlines the practical interest of an easily applicable method of early detection, so that preventive measures can be taken in good time.This paper proposes the use of the battery voltage distribution, as a tool for surveying the state of health of stand-alone PV systems. Expected distributions can be derived by judicious modelling; while observed distributions can easily be obtained from systematic voltage measurements which are already made in many PV systems worldwide. Comparison between expected and observed distributions allows relatively simple diagnosis rules to be derived.

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An anisotropic hourly diffuse radiation model for sloping surfaces: Description, performance validation, site dependency evaluation

Cited by 274 publications

References 6 publications

A fresh look at weather impact on peak electricity demand and energy use of buildings using 30-year actual weather data

A fresh look at weather impact on peak electricity demand and energy use of buildings using 30-year actual weather data

Modeling of Diffuse Solar Radiation and Impact of Complex Terrain over Pakistan Using RS/GIS

The battery voltage distribution: a possible tool for surveying the state of health of stand-alone PV systems

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