Modelling daily PV performance as a function of irradiation, ambient temperature, soiling, wind speed, and aging - Applied to PV modules operating in Maui

Busquet, Séverine; Kobayashi, Jonathan

doi:10.1109/pvsc.2018.8548220

Cited by 2 publications

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“…Wind speed: the temperature of the panel may be affected by the speed of the wind surrounding the panel [34,35]. Increased wind speed can also clean the dust off of the panel surface or stir up dust, thereby affecting the irradiance that reaches the panel [36]; • Visibility: this variable is a measurement of the distance at which a light can be seen and identified [37].…”

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“…In Table 1, short is defined as having an effect within a day, medium is on the order of months, and long is an effect that takes a year or more to impact the power output. Busquet et al (2018) primarily studied the medium-and long-term effects of factors, such as aging and soiling; panel age is not commonly used by other studies [35]. Aging describes the amount of time the panel has been installed and exposed to the elements, and soiling describes the dust build-up of the panel's surface.…”

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Machine Learning Modeling of Horizontal Photovoltaics Using Weather and Location Data

et al. 2020

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Solar energy is a key renewable energy source; however, its intermittent nature and potential for use in distributed systems make power prediction an important aspect of grid integration. This research analyzed a variety of machine learning techniques to predict power output for horizontal solar panels using 14 months of data collected from 12 northern-hemisphere locations. We performed our data collection and analysis in the absence of irradiation data—an approach not commonly found in prior literature. Using latitude, month, hour, ambient temperature, pressure, humidity, wind speed, and cloud ceiling as independent variables, a distributed random forest regression algorithm modeled the combined dataset with an R2 value of 0.94. As a comparative measure, other machine learning algorithms resulted in R2 values of 0.50–0.94. Additionally, the data from each location was modeled separately with R2 values ranging from 0.91 to 0.97, indicating a range of consistency across all sites. Using an input variable permutation approach with the random forest algorithm, we found that the three most important variables for power prediction were ambient temperature, humidity, and cloud ceiling. The analysis showed that machine learning potentially allowed for accurate power prediction while avoiding the challenges associated with modeled irradiation data.

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