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The current non-renewable energy-based transportation system is getting us places while simultaneously killing us. The global oil and gas markets are the most sizeable - 4677.45 billion-dollar industries - in the world in terms of revenue due to human dependency on fast transportation. In pre-industrial revolution times, the average rate of global temperature increase was 0.13-degrees Fahrenheit. The introduction of fossil fuel-based vehicles has more than doubled those numbers, thus resulting in accelerated global warming. Electrical vehicles have a few challenges of their own that we have discussed in this paper, finding solutions to them are based on Yoichi Hori's paper to make our model more energy efficient. Our model has been validated by researches done in the past, this paper combines the findings of formerly published papers to produce a working model of our current design with proof of concept. To reduce the carbon footprint of coal and fossil fuel-based electricity production, solar energy has been considered to charge the vehicle. It is not only an efficient choice, but it is also economical. It saves the consumer around $1778 annually in fuel cost and 38.5g/km in carbon emissions. Using a wireless charging station solves a major UX design problem, making charging an effortless experience. This method is 88.05% efficient at 31.5A. Supercapacitors have high power densities, therefore, being a good power source for the EV in theory, however, research shows that current supercapacitor technology is unable to perform as a standalone power source. Hence our model combines supercapacitors with conventional batteries to power the EV, additionally a break recovery system stores energy in the supercapacitor. This powers the car's acceleration. Our model priorities the use of materials that can be repurposed hence carbon-based electrodes are used - Graphene is a potential choice as it consists of high energy cycles. [ABSTRACT FROM AUTHOR]

Purpose: The environmental impacts of electricity generation are a critical issue towards sustainability and thus an important research topic in several countries. The life cycle assessment methodology has been widely employed to assess electricity generation. However, there are still gaps in research to be explored within this theme. Therefore, this paper aims to conduct a systematic theoretical analysis of the state of the art of the scientific research on LCA of electricity generation systems in the world. Methods: A critical review of 47 studies was conducted. The study is comprehensive in the analysis of the main aspects of the identified high impact studies as follows: authors, countries, universities, keywords, journals, number of citations, life cycle impact assessment methods, impact categories, software tools, and databases. The Methodi Ordinatio was applied to rank the studies in terms of impact factor and number of citations, pointing out high impact research. Results and discussion: Wind and solar powers have two of the smallest impact indices in their generation in terms of global warming, compared to other sources. The ecoinvent database was the most used among the studies analyzed, providing data for potential environmental impacts. The most frequently used impact category in the assessments was climate change. The studies are not equally distributed but most of them are concentrated in European countries. In some countries, clean sources seem promising due to their capacity to generate electricity in places with high wind incidence and high capacity for sunlight capture. Conclusions: The conclusions of this article summarize the characteristics of existing literature and provide suggestions for future work. The results of the study can also be used to promote development actions and foment changes in energy matrices in a global context. The main studies in this area point that in the future, the main sources for electricity generation will be renewable ones, since life cycle assessment of electricity generation systems has been seeking to generate knowledge to support informed decision-making. [ABSTRACT FROM AUTHOR]

Summary: This paper is concerned with innovative approaches to renewable energy sources computation methodologies, which provide more refined results than the classical alternatives. Such refinements provide additional improvements especially for replacement of fossil energy usages that emit greenhouse gas (GHG) into the atmosphere leading to climate change impact. Current knowledge gap among each renewable energy source calculation is rather missing fundamentals of plausible, rational, and logical explanations for the interpretation of results. In the literature, there are rather complicated and mechanically applicable methodologies, which require input and output measurement data match with missing physical explanations. The view taken in this review paper is to concentrate on quite plausible, logical, rational, and effectively applicable innovative energy calculation methodologies with simplistic fundamentals. For this purpose, a set of renewable energy methodological approaches is revisited with their innovative structures concerning solar, wind, hydro, current, and geothermal energy resources. With the increase in the renewable energy utilizations to combat the undesirable impacts of global warming and climate change, there is a need for better models that will include physical environmental conditions and data properties in the probabilistic, statistical, stochastic, logical, and rational senses leading to refined and more reliable estimations with application examples in the text. Finally, new research directions are also recommended for more refined innovative energy system calculations. [ABSTRACT FROM AUTHOR]

Climate change is stated as one of the largest issues of our time, resulting in many unwanted effects on life on earth. Cloud fractional cover (CFC), the portion of the sky covered by clouds, might affect global warming and different other aspects of human society such as agriculture and solar energy production. It is therefore important to improve the projection of future CFC, which is usually projected using numerical climate methods. In this paper, we explore the potential of using machine learning as part of a statistical downscaling framework to project future CFC. We are not aware of any other research that has explored this. We evaluated the potential of two different methods, a convolutional long short-term memory model (ConvLSTM) and a multiple regression equation, to predict CFC from other environmental variables. The predictions were associated with much uncertainty indicating that there might not be much information in the environmental variables used in the study to predict CFC. Overall the regression equation performed the best, but the ConvLSTM was the better performing model along some coastal and mountain areas. All aspects of the research analyses are explained including data preparation, model development, ML training, performance evaluation and visualization. [ABSTRACT FROM AUTHOR]

Several theoretical studies have been reported in recent years which have indicated that large-scale wind farms can have an impact on local and regional meteorology, possibly on climate. Now evidence of it based on field observations has also begun to emerge. The present paper traces the evolution of this knowledge. It discusses the theoretical studies on the possibility of wind turbines effecting climate change, and summarizes the emerging confirmation of those predictions. The concluding part of the paper assesses the implications of these findings in the context of the world׳s past experience on global warming and its present thrust to meet substantial portions of its energy needs with renewables. [ABSTRACT FROM AUTHOR]

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Abstract The advantage of accurate forecasts is that it solves the main problem related to renewable energies: their variability. Indeed, while renewable energies has not yet replaced fossil fuels, in spite of the efforts of many governments, it is because of their intermittent nature, hence the importance of prediction in this field. The new approach for energy prediction that we propose in this paper, is founded on the analysis of the dynamical behavior of the photovoltaic production of the Faculty of Sciences and Technology of Beni Mellal, Morocco. It consists in performing the phase space reconstruction, which allowed us later to build a database for the input of the neural network and thus take into account the dynamics of the system in the forecasting process. Then, in search of more precision, we introduce the wavelet transformation, to simplify the database constructed from phase space reconstruction. Finally, comparing between the predictions and the actual observations confirmed the efficiency of our approach. Highlights • PV production forecast has an major role in its integration into electricity mix and thus in the fight against climate change. • In this work, we propose a new approach for forecasting, based on the exploitation of the phase space reconstruction. • The proposed model is a hybrid method combining the phase space reconstruction, DWT method and recurrent neural network. • The method is tested on real observations to confirm the efficiency of our approach. • Comparison between predictions and actual data confirmed the effectiveness of our approach given the improved results. [ABSTRACT FROM AUTHOR]

Solar energy harvesting using Photovoltaic (PV) systems is one of the most popular sources of renewable energy, however the main drawback of PV systems is their low conversion efficiency. An optimal system operation requires an efficient tracking of the Maximum Power Point (MPP), which represents the maximum energy that can be extracted from the PV panel. This paper presents a novel control approach for the Maximum Power Point Tracking (MPPT) based on the differential flatness property of the Boost converter, which is one of the most used converters in PV systems. The underlying idea of the proposed control approach is to use the classical flatness-based trajectory tracking control where a reference voltage will be defined in terms of the maximum power provided by the PV panel. The effectiveness of the proposed controller is assessed through numerical simulations and experimental tests. The results show that the controller based on differential flatness is capable of converging in less than 0.15 s and, compared with other MPPT techniques, such as Incremental Conductance and Perturb and Observe, it improves the response against sudden changes in load or weather conditions, reducing the ringing in the output of the system. Based on the results, it can be inferred that the new flatness-based controller represents an alternative to improve the MPPT in PV systems, especially when they are subject to sudden load or weather changes. [ABSTRACT FROM AUTHOR]

Global warming has led to substantially earlier spring leaf‐out in temperate‐zone deciduous trees. The interactive effects of temperature and daylength underlying this warming response remain unclear. However, they need to be accurately represented by earth system models to improve projections of the carbon and energy balances of temperate forests and the associated feedbacks to the Earth's climate system. We studied the control of leaf‐out by daylength and temperature using data from six tree species across 2,377 European phenological network (www.pep725.eu), each with at least 30 years of observations. We found that, in addition to and independent of the known effect of chilling, daylength correlates negatively with the heat requirement for leaf‐out in all studied species. In warm springs when leaf‐out is early, days are short and the heat requirement is higher than in an average spring, which mitigates the warming‐induced advancement of leaf‐out and protects the tree against precocious leaf‐out and the associated risks of late frosts. In contrast, longer‐than‐average daylength (in cold springs when leaf‐out is late) reduces the heat requirement for leaf‐out, ensuring that trees do not leaf‐out too late and miss out on large amounts of solar energy. These results provide the first large‐scale empirical evidence of a widespread daylength effect on the temperature sensitivity of leaf‐out phenology in temperate deciduous trees. [ABSTRACT FROM AUTHOR]

What are we doing to our climate? Emissions from fossil fuel burning have raised carbon dioxide concentrations 35 percent higher than in the past millions of years. This increase is warming our planet via the greenhouse effect. What is climate change doing to and for us? Dry regions are drier and wet ones wetter. Wildfires have increased threefold, hurricanes more violent, floods setting record heights, glaciers melting, and seas rising. Parts of Earth are increasingly uninhabitable. Climate change requires us to act as a global community. Climate justice enjoins emitters to pay the social-environmental costs of fossil fuel burning. This would expedite green solar, wind, and next-generation nuclear energy sources. Individuals should conserve resources, waste less food, and eat a plant-rich diet. [ABSTRACT FROM AUTHOR]

Climate change and global warming have triggered a global increase in the use of renewable energy for various purposes. In recent years, the photovoltaic (PV)-system has become one of the most popular renewable energy technologies that captures solar energy for different applications. Despite its popularity, its adoption is still facing enormous challenges, especially in developing countries. Experience from research and practice has revealed that installed PV-systems significantly underperform. This has been one of the major barriers to PV-system adoption, yet it has received very little attention. The poor performance of installed PV-systems means they do not generate the required electric energy output they have been designed to produce. Performance assessment parameters such as performance yields and performance ratio (PR) help to provide mathematical accounts of the expected energy output of PV-systems. Many reasons have been advanced for the disparity in the performance of PV-systems. This study aims to analyze the factors that affect the performance of installed PV-systems, such as geographical location, solar irradiance, dust, and shading. Other factors such as multiplicity of PV-system components in the market and the complexity of the permutations of these components, their types, efficiencies, and their different performance indicators are poorly understood, thus making it difficult to optimize the efficiency of the system as a whole. Furthermore, mathematical computations are presented to prove that the different design methods often used for the design of PV-systems lead to results with significant differences due to different assumptions often made early on. The methods for the design of PV-systems are critically appraised. There is a paucity of literature about the different methods of designing PV-systems, their disparities, and the outcomes of each method. The rationale behind this review is to analyze the variations in designs and offer far-reaching recommendations for future studies so that researchers can come up with more standardized design approaches. [ABSTRACT FROM AUTHOR]