Solar photovoltaic installations have now become a common sight across the globe. However, in places with a high level of dust, the panels have not performed as expected. The dust deposition acts to reduce the effective light that the solar cells receive thus reducing the output. As such, cleaning regimes are instituted to improve the performance of the panels. The more often the cleaning, the better the performance, but this could likely imply more e. Solar photovoltaic installations have now become a common sight across the globe. However, in places with a high level of dust, the panels have not performed as expected. The dust deposition acts to reduce the effective light that the solar cells receive thus reducing the output. As such, cleaning regimes are instituted to improve the performance of the panels. The more often the cleaning, the better the performance, but this could likely imply more energy is used for cleaning and the improvement may not be sufficient. In this paper, we develop a model to forecast the output power with respect to variables of cleaning, wind, humidity and temperature. The result shows that it is possible to develop a model to optimise cleaning regimes taking into account meteorological factors. One has to consider the impact of wind, which was found to be positively correlated with photovoltaic power output.••Solar photovoltaic installations have risen substantially in the last decade. Energy demand projections show that adopting renewable energy is essential to ensure that future energy demands are met. This rise has been due to the falling price of photovoltaic modules as well as a global push to reduce carbon emissions,. The solar photovoltaic (PV) capacity has increased from 41 542 MW to 586 434 MW globally between 2010 and 2019. In the UAE, the Dubai Clean Energy Strategy 2050 targets 5000 MW to be generated from solar resources by 2030.However, it has been shown that environmental factors influence solar panel output and include: temperature, humidity, wind speed, wind direction, cloud cover, rainfall and dust deposition,. The Middle East is a particular challenge due to low rainfall, high temperatures and heavy dust deposition. Long term dust deposition in the Middle East can reduce solar panel output by as much as 50%. Developing a reliable model of solar panel electricity generation incorporating meteorological effects in the Middle East poses a significant challenge. The specifics of the site's geographical location can substantially impact the solar panel's performance, resulting in failure to meet the planned return on investment.Several studies have investigated the impact of environmental factors on PV power output. A comprehensive review by Mani and Pillai categorised the studies done on the topic of dust deposition on the surface of solar panels over two timeframes, from 1940–1990 and from 1990 onwards. The study concluded that for research done between 1940 and 1990, the results from experiments were often inconsistent or omitted important factors to accurately model the effect of dust on solar panel performance. For research conducted after 1990, most research either utilises artificial dust in the experiment or does not consider the type and characteristics of the dust deposited on the surface of solar panels. A key finding is that the results found in most studies did not generalise well — results were highly dependent on the location and experimental set-up of each study.Rao et al. devised an experiment to quantify the impact of dust on reducing the power output of solar panels. The study concluded that dust deposition does indeed negatively affect the performance of the solar panels impacting the short-circuit current of the panels but not significantly the open-circuit voltage. Mekhilef et al. aimed to determine the effects dust, humidity, and wind have on solar panel performance and the interdependencies of each variable. The study shows that the three factors are not independent, and that they should a. The methodology comprises of both hardware and modelling components. The hardware component involves collecting data for the power output of small PV arrays which is then used to develop the PV output model. The PV arrays were located on the rooftop of a building in urban Dubai. Data collection spanned 6 weeks. An autoregressive-moving-average (ARM. Before we proceed with a more sophisticated analysis, it is necessary to do a preliminary descriptive study to simply observe the effect of dust cleaning on the energy production output of the panel. As shown in Table 1, the correlation coefficient associated with the solar output of the different panels and the 'days since last cleaned' variable is negative, suggesting a negative relationship between dust and solar output. In other words, panel cleaning is positively associated with solar production. Similarly, temperature appears to be negatively correlated with production, whereas moisture and wind speed appear to be positively correlated with solar production. We also observe that a longer duration of dust deposition will have a negative impact on the solar production output. The correlation shows there is less dependency on cleaning for the weekly as compared with the fortnightly. Effectively the optimal cleaning time is when the correlation is lowest, but that would mean cleaning every day.Fig. 2 shows the variation of temperature over the period and is consistent with the time of the year: temperature is a seasonal factor. The wind speed shown in Fig. 3 shows a slight increase, which may be seasonal as expected in the region. The humidity, however, does not show a seasonal element for the period shown (see Fig. 4). However, at high wind speeds, humidity is seen to be lower.
