Journal of Applied physics,Vol.33 : 1962年に発表されたレオロジー関連の論文

The energy delivery of a solar-energy system is generally associated with the sun's available irradiance and spectral content, as well as a variety of environmental and climatic factors and inherent system and component performances. However, other external factors relating to geographical location and conditions can have even greater impacts on system performance. Among these, soiling is a commonly overlooked or underestimated issue that can be a showstopper for the viability of a solar installation. This paper provides a comprehensive overview of soiling problems, primarily those associated with “dust” (sand) and combined dust–moisture conditions that are inherent to many of the most solar-rich geographic locations worldwide. We review and evaluate key contributions to the understanding, performance effects, and mitigation of these problems. These contributions span a technical history of almost seven decades. We also present an inclusive literature survey/assessment. The focus is on both transmissive surfaces (e.g., those used for flat-plate photovoltaics or for concentrating lenses) and reflective surfaces (e.g., mirrors or heliostats for concentrating power systems).

A comprehensive review of the impact of dust on the use of solar energy: History, investigations, results, literature, and mitigation approaches

The power output delivered from a photovoltaic module highly depends on the amount of irradiance, which reaches the solar cells. Many factors determine the ideal output or optimum yield in a photovoltaic module. However, the environment is one of the contributing parameters which directly affect the photovoltaic performance. The authors review and evaluate key contributions to the understanding, performance effects, and mitigation of power loss due to soiling on a solar panel. Electrical characteristics of PV (Voltage and current) are discussed with respect to shading due to soiling. Shading due to soiling is divided in two categories, namely, soft shading such as air pollution, and hard shading which occurs when a solid such as accumulated dust blocks the sunlight. The result shows that soft shading affects the current provided by the PV module, but the voltage remains the same. In hard shading, the performance of the PV module depends on whether some cells are shaded or all cells of the PV module are shaded. If some cells are shaded, then as long as the unshaded cells receive solar irradiance, there will be some output although there will be a decrease in the voltage output of the PV module. This study also present a few cleaning method to prevent from dust accumulation on the surface of solar arrays.

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Power loss due to soiling on solar panel: A review

Solar photovoltaic generation forecasting methods: A review

Integration of photovoltaics into power grids is difficult as solar energy is highly dependent on climate and geography; often fluctuating erratically. This causes penetrations and voltage surges, system instability, inefficient utilities planning and financial loss. Forecast models can help; however, time stamp, forecast horizon, input correlation analysis, data pre and post-processing, weather classification, network optimization, uncertainty quantification and performance evaluations need consideration. Thus, contemporary forecasting techniques are reviewed and evaluated. Input correlational analyses reveal that solar irradiance is most correlated with Photovoltaic output, and so, weather classification and cloud motion study are crucial. Moreover, the best data cleansing processes: normalization and wavelet transforms, and augmentation using generative adversarial network are recommended for network training and forecasting. Furthermore, optimization of inputs and network parameters, using genetic algorithm and particle swarm optimization, is emphasized. Next, established performance evaluation metrics MAE, RMSE and MAPE are discussed, with suggestions for including economic utility metrics. Subsequently, modelling approaches are critiqued, objectively compared and categorized into physical, statistical, artificial intelligence, ensemble and hybrid approaches. It is determined that ensembles of artificial neural networks are best for forecasting short term photovoltaic power forecast and online sequential extreme learning machine superb for adaptive networks; while Bootstrap technique optimum for estimating uncertainty. Additionally, convolutional neural network is found to excel in eliciting a model's deep underlying non-linear input-output relationships. The conclusions drawn impart fresh insights in photovoltaic power forecast initiatives, especially in the use of hybrid artificial neural networks and evolutionary algorithms.

A review and evaluation of the state-of-the-art in PV solar power forecasting:Techniques and optimization

Short–mid-term solar power prediction by using artificial neural networks

As known that efficiency of photovoltaic cells is not high as desired level. Efficiency of PVs could be improved by selecting convenient locations that have high solar irradiation, sunshine duration, mild temperature, low level air pollution and dust concentration. Additionally, some environmental parameters called derating factors effect to decrease PV efficiencies such as cloud, high temperature, aerosol optical depth, high dust concentration, shadow, snow, humidity etc. In this paper, all parameters that effect PV efficiency are considered in detail under climatic conditions of Istanbul. A 750 Wp PV system with measurement devices is constructed in Maslak campus of Istanbul Technical University. Keywords—Efficiency, Derating Factor, Istanbul, Photovoltaic.

Effect of Derating Factors on Photovoltaics under Climatic Conditions of Istanbul

Wind power is one of the major renewable energy sources, and this source has reached to compete with conventional energies. Wind speed has very complex variations during different time horizons. Prediction of wind speed shows some uncertainties depending on atmospheric parameters, such as temperature, pressure, solar irradiation, and relative humidity. Additionally, wind turbines could not generate electricity at all wind speed values that are less than cut-in and greater than cut-out. These conditions add new uncertainties to the prediction of this meteorological parameter. In this case, wind power prediction from generated electricity data will be better than direct wind speed or other meteorological parameters. Generally, in engineering applications wind power prediction is based on hourly mean, in other words, one hour time horizon data. This time horizon is accepted as a reference and representative but physically this horizon does not represent data homogeneity and should be changed depending on loc...

Bihter Yerli

Papers

Short–mid-term solar power prediction by using artificial neural networks

Effect of Derating Factors on Photovoltaics under Climatic Conditions of Istanbul

Determination of the Representative Time Horizons for Short-term Wind Power Prediction by Using Artificial Neural Networks