Bangladesh has among the lowest per capita energy (240 kg oil equivalents) consumption in the world and is severely dependent on additional environmentally friendly renewable energy resources in the future. Among the possible energy resources that could be explored is the potential geothermal energy in regions of higher geothermal gradients with favorable geo-tectonic setting and ideal petro-physical properties. A preliminary examination of bottom hole temperatures of a large number of onshore wells spread over a vast area in the eastern part of the country, especially in Thakurgaon-Mymensingh-Sunamgonj-Sylhet through in the Bengal fore deep, strongly suggests that several other areas are of great interest for further studies in order to determine their geothermal energy potential. Bangladesh has witnessed a high demand for uninterrupted electricity due to rapid civilization in the last few years. Bangladesh needs now a reliable green energy sources as its power sector beset by many infrastructural problems (inefficient transmission system, very old power stations and cumbersome decision making process). Bangladesh has taken initiative to generate 25000MW electricity within 2021. In this regard, geothermal energy can be a viable and useful alternative and this paper proposes the prospects of its introduction to the power sector of Bangladesh. In this paper, a study is presented that shows the suitable locations in Bangladesh where geothermal power plants can be set up easily. Recently, the Ministry of Power, Energy and Mineral Resources has approved the establishment of the first ever geothermal power plant (200MW) in the country. A total of approximately 1000 MW can be added into the energy grid of Bangladesh through geothermal power systems. The geothermal energy is green, indigenous, locally occurring and continuously available independent of climatic changes. It will help to reduce the huge oil bill that the country is facing now, provided the national planners give adequate attention and support for the development of geothermal energy at a rapid pace to reduce the severe electricity crisis in Bangladesh as other energy resources like peat, hydropower, nuclear, wind, tidal / waves are not significant at present.

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Electricity production from photovoltaic (PV) systems has accelerated in the last few decades. Numerous environmental factors, particularly the buildup of dust on PV panels have resulted in a significant loss in PV energy output. To detect the dust and thus reduce power loss, several techniques are being researched, including thermal imaging, image processing, sensors, cameras with IoT, machine learning, and deep learning. In this study, a new dataset of images of dusty and clean panels is introduced and applied to the current state-of-the-art (SOTA) classification algorithms. Afterward, a new convolutional neural network (CNN) architecture, SolNet, is proposed that deals specifically with the detection of solar panel dust accumulation. The performance and results of the proposed SolNet and other SOTA algorithms are compared to validate its efficiency and outcomes where SolNet shows a higher accuracy level of 98.2%. Hence, both the dataset and SolNet can be used as benchmarks for future research endeavors. Furthermore, the classes of the dataset can also be expanded for multiclass classification. At the same time, the SolNet model can be fine-tuned by tweaking the hyperparameters for further improvements.

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