Photovoltaic (PV) solar panels account for a major portion of the smart grid capacity. On the other hand, the accumulation of solar panels dust is a significant challenge for PV-based systems. The accumulation of solar panels dust results in a significant reduction in the amount of energy produced. Because of the country’s low wind velocity and rainfall, frequent cleaning of solar panels is necessary either by manual or automated means. Cleaning activities should only be initiated when absolutely essential to reduce maintenance costs and increase the power output of solar panels that have been projected to be affected by dust accumulation. In this paper, we develop a deep belief network model to detect the dust particles in the solar panels installed as a large unit. The study takes into account various input metrics that includes solar irradiance, temperature level, and dust level on the panels. These metrics are used for the estimation of the level of dust present in the atmosphere and how often the panels can be cleaned at regular intervals. The simulation is conducted to test the efficacy of the model in cleaning the panels. The results are estimated in terms of accuracy, precision, recall, and F-measure. The results of the simulation show that the proposed model achieves higher accuracy rate of more than 99% than other methods.

The objective of the current work is to develop an automatic tool to identify microbiological data types using computer vision and pattern recognition. Current systems rely on the subjective reading of profiles by a human expert. This process is time-consuming and prone to errors. Bacteriophage (phage) typing & Fluorescent imaging methods are used to extract representative feature profiles and identify the bacterial types. For feature selection of Bacterial identification system, the most successful method seems to be the appearance-based approach, which generally operates directly on images or appearances of bacterial objects. The image segmentation, Linear Discriminant Analysis (LDA), Direct Fractional LDA (DFLDA) and Principal Component Analysis (PCA) are the powerful tools used for feature extraction. Then the principal components are analyzed by DFLDA and simple Nearest Neighbor Classifier technique is used to identify the type of bacteria. The effectiveness of the proposed method has been verified through experimentation using fifty popular bacterial image databases.

Plastic is a resilient, chemically inert, lightweight, and low-cost material. It sticks around in the environment for more than hundred years, threatening nature and spreading toxins. The current study deals with the use of waste polymeric materials and de oiled cake for the production of liquid oil and its blend on the performance and emission characteristics of diesel engine. The tests were conducted in an engine fuelled with diesel and four distinct blends such as 5% (B5), 10% (B10), 15% (B15), and 20% (B20), respectively. The liquid oil was produced by co-pyrolysis of neem de oiled cake (NDC) and waste polystyrene (WPS) in 1 : 2 blend ratio. The raw pyrolysis oil and its different blends were tested for their physical and chemical characteristics in order to find their suitability. Brake power (BP), brake thermal efficiency (BTE), brake-specific fuel consumption (BSFC), emissions of carbon monoxide (CO), hydrocarbon (HC), and nitrogen oxide (NOx) are used to assess the performance of the engine. The experimental results reveal that BTE at all blends is lower than diesel at all loads and the BSFC increases with increasing blend ratio and falls with increasing engine load. At higher loads, the deviation of performance and emission values from baseline diesel up to B10 is very small. It is found from the results that the liquid oil derived from NDC and WPS up to 10% blend will be the promising additive for fossil fuels.

Solar energy forecasting accuracy is essential for increasing the quantity of renewable energy that can be integrated into the existing electrical grid control systems. The availability of data at unprecedented levels of granularity allows for the development of data-driven algorithms to improve the estimation of solar energy generation and production. In this paper, we develop a prediction of solar potential across large photovoltaic panels from the roof tops using a machine learning method. The Restricted Boltzmann Machine (RBM) is the machine learning method used in the study to predict or forecast the solar potential in rooftops. The machine learning model is supplied with training dataset to get trained with the dataset for conversion into the model and then tested with the test dataset for validating the model. The results of simulation are conducted on R-package over various libraries to predict the rooftop solar potential. The results of simulation shows that the proposed method achieves higher rate of prediction accuracy than the other methods. The results of the simulation show that the proposed method achieves a higher rate of prediction accuracy of 99% than the other methods.

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