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Effects of soiling on photovoltaic (PV) modules in the Atacama Desert R. R. Cordero, A. Damiani, D. Laroze, S. MacDonell, J. Jorquera, E. Sepúlveda, S. Feron, P. Llanillo, F. Labbe, J. Carrasco, J. Ferrer & G. Torres  Published: 17 September 2018 Abstract Soiling by dry deposition affects the power output of photovoltaic (PV) modules, especially under dry and arid conditions that favor natural atmospheric aerosols (wind-blown dust). In this paper, we report on measurements of the soiling effect on the energy yield of grid-connected crystalline silicon PV modules deployed in five cities across a north-south transect of approximately 1300 km in the Atacama Desert ranging from latitude 18°S to latitude 30°S. Energy losses were assessed by comparing side-by-side outputs of four co-planar PV modules. Two of the PV modules of the array were kept clean as a control, while we allowed the other two to naturally accumulate soiling for 12 months (from January 2017 to January 2018). We fou...

Photovoltaics (PV) System Energy Forecast on the Basis of the Local Weather Forecast: Problems, Uncertainties and Solutions  by Kristijan Brecl *OrcID andMarko TopičOrcID Laboratory of Photovoltaics and Optoelectronics—LPVO, Faculty of Electrical Engineering, University of Ljubljana, Tržaška 25, SI-1000 Ljubljana, Slovenia Published: 4 May 2018 Abstract When integrating a photovoltaic system into a smart zero-energy or energy-plus building, or just to lower the electricity bill by rising the share of the self-consumption in a private house, it is very important to have a photovoltaic power energy forecast for the next day(s). While the commercially available forecasting services might not meet the household prosumers interests due to the price or complexity we have developed a forecasting methodology that is based on the common weather forecast. Since the forecasted meteorological data does not include the solar irradiance information, but only the weather condition, the uncertaint...

Robust PV Degradation Methodology and Application Published in: IEEE Journal of Photovoltaics ( Volume: 8 , Issue: 2 , March 2018 ) Dirk C. Jordan  National Renewable Energy Laboratory, Golden, CO, USA; Chris Deline  ; Sarah R. Kurtz ; Gregory M. Kimball  ; Mike Anderson  Abstract: The degradation rate plays an important role in predicting and assessing the long-term energy generation of photovoltaics (PV) systems. Many methods have been proposed for extracting the degradation rate from operational data of PV systems, but most of the published approaches are susceptible to bias due to inverter clipping, module soiling, temporary outages, seasonality, and sensor degradation. In this paper, we propose a methodology for determining PV degradation leveraging available modeled clear-sky irradiance data rather than site sensor data, and a robust year-over-year rate calculation. We show the method to provide reliable degradation rate estimates even in the case of sensor dri...

Short-term Memory Solar Energy Forecasting at University of Illinois  Adele Kuzmiakova ”akuzmiakova”, Gael Colas ”colasg”, Alex McKeehan ”mckeehan” December 2017  Abstract:  Climate change and energy crisis have motivated the use and development of solar power generation. Since solar power generation is highly intermittent and dependent on local weather characteristics, we apply a variety of linear and non-linear ensemble machine learning models to predict short-term solar energy output at the University of Illinois campus. Implemented models include weighted linear regression with and without dimension reduction, boosting regression trees, and artificial neural networks with and without vanishing temporal gradient. Additionally, we apply a variety of variable selection techniques, which suggest that air temperature, relative humidity, and dew point are the most informative weather parameters for predicting the solar energy generation at the Illinois power plant. Findings...

An investigation of the key parameters for predicting PV soiling losses Leonardo Micheli, Matthew S. Muller Published 2017 Chemistry Progress in Photovoltaics One hundred and two environmental and meteorological parameters have been investigated and compared with the performance of 20 soiling stations installed in the USA, in order to determine their ability to predict the soiling losses occurring on PV systems. The results of this investigation showed that the annual average of the daily mean particulate matter values recorded by monitoring stations deployed near the PV systems are the best soiling predictors, with coefficients of determination (R2) as high as 0.82. The precipitation pattern was also found to be relevant: among the different meteorological parameters, the average length of dry periods had the best correlation with the soiling ratio. A preliminary investigation of two-variable regressions was attempted and resulted in an adjusted R2 of 0.90 when a combination of PM2.5 ...

PV monitoring and fault detection: Evaluation of machine learning for prediction of PV soiling in Northern Cape, South-Africa Gard Inge Rosvold Published 2017 Engineering Renewable energy sources, and thus PV are experiencing exponential growth due to most current energy production still relies on fossil fuels, and energy demands are steadily increasing. If the performance of PV could be increased, the result will be more production per installation. One significant performance loss for PV is soiling on the modules. Research has been done to statistically indicate optimal cleaning intervals. Some attempts using conventional methods to predict soiling have been conducted as well, suggesting environmental features like wind and humidity are relevant factors for predicting soiling. With the increase in popularity and availability of machine learning – is it possible to use machine learning to predict soiling? If it is possible, this could lead to quick and precise implementation of algori...

A novel model to estimate the cleaning frequency for dirty solar photovoltaic (PV) modules in desert environmen YuJiang LinLu HaoLu Highlights •A novel model is developed to estimate the cleaning frequency for dirty PV modules in desert areas. •The cleaning criterion was 5% reduction in power with the accumulated dust density of 2 g/m2. •The cleaning time for PV modules in desert regions is about 20 days. •The effect of environmental parameters on the cleaning frequency are discussed respectively. Abstract Accumulated dust on solar photovoltaic (PV) modules can significantly decrease their energy output in desert environment. Therefore, cleaning the deposited dust on the PV module surface is crucial in engineering applications to maintain the high power output of solar power plants, especially in desert areas. Nevertheless, it is difficult to predict the reasonable cleaning frequency for PV modules by traditional methods. In this paper, a novel model to simply estimate the cleaning fre...