Forecasting of COVID-19 cases using deep learning models: Is it reliable and practically significant?

The ongoing outbreak of the COVID-19 pandemic prevails as an ultimatum to the global economic growth and henceforth, all of society since neither a curing drug nor a preventing vaccine is discovered. The spread of COVID-19 is increasing day by day, imposing human lives and economy at risk. Due to the increased enormity of the number of COVID-19 cases, the role of Artificial Intelligence (AI) is imperative in the current scenario. AI would be a powerful tool to fight against this pandemic outbreak by predicting the number of cases in advance. Deep learning-based time series techniques are considered to predict world-wide COVID-19 cases in advance for short-term and medium-term dependencies with adaptive learning. Initially, the data pre-processing and feature extraction is made with the real world COVID-19 dataset. Subsequently, the prediction of cumulative confirmed, death and recovered global cases are modelled with Auto-Regressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), Stacked Long Short-Term Memory (SLSTM) and Prophet approaches. For long-term forecasting of COVID-19 cases, multivariate LSTM models is employed. The performance metrics are computed for all the models and the prediction results are subjected to comparative analysis to identify the most reliable model. From the results, it is evident that the Stacked LSTM algorithm yields higher accuracy with an error of less than 2% as compared to the other considered algorithms for the studied performance metrics. Country-specific analysis and city-specific analysis of COVID-19 cases for India and Chennai, respectively, are predicted and analyzed in detail. Also, statistical hypothesis analysis and correlation analysis are done on the COVID-19 datasets by including the features like temperature, rainfall, population, total infected cases, area and population density during the months of May, June, July and August to find out the best suitable model. Further, practical significance of predicting COVID-19 cases is elucidated in terms of assessing pandemic characteristics, scenario planning, optimization of models and supporting Sustainable Development Goals (SDGs)

A holistic review on energy forecasting using big data and deep learning models

With the growth of forecasting models, energy forecasting is used for better planning, operation, and management in the electric grid. It is important to improve the accuracy of forecasting for a faster decision‐making process. Big data can handle large scale of datasets and extract the patterns fed to the deep learning models that improve the accuracy than the traditional models and hence, recently started its application in energy forecasting. In this study, an in‐depth insight is initially derived by investigating artificial intelligence (AI) and machine learning (ML) techniques with their strengths and weaknesses, enhancing the consistency of renewable energy integration and modernizing the overall grid. However, Deep learning (DL) algorithms have the capability to handle big data by capturing the inherent non‐linear features through automatic feature extraction methods. Hence, an extensive and exhaustive review of generative, hybrid, and discriminative DL models is being examined for short‐term, medium‐term, and long‐term forecasting of renewable energy, energy consumption, demand, and supply etc. This study also explores the different data decomposition strategies used to build forecasting models. The recent success of DL is being investigated, and the insights of paradoxes in parameter optimization during the training of the model are identified. The impact of weather prediction in the wind and solar energy forecasting is examined in detail. From the existing literatures, it has seen that the average mean absolute percentage error (MAPE) value of solar and wind energy forecasting is 10.29% and 6.7% respectively. Current technology barriers involved in implementing these models for energy forecasting and the recommendations to overcome the existing system barriers are identified. An in‐depth analysis, discussions of the results, and the scope for improvement are provided in this study including the potential directions for future research in the energy forecasting

An Assessment of Onshore and Offshore Wind Energy Potential in India Using Moth Flame Optimization

Wind energy is one of the supremely renewable energy sources and has been widely established worldwide. Due to strong seasonal variations in the wind resource, accurate predictions of wind resource assessment and appropriate wind speed distribution models (for any location) are the significant facets for planning and commissioning wind farms. In this work, the wind characteristics and wind potential assessment of onshore, offshore, and nearshore locations of India—particularly Kayathar in Tamilnadu, the Gulf of Khambhat, and Jafrabad in Gujarat—are statistically analyzed with wind distribution methods. Further, the resource assessments are carried out using Weibull, Rayleigh, gamma, Nakagami, generalized extreme value (GEV), lognormal, inverse Gaussian, Rician, Birnbaum–Sandras, and Bimodal–Weibull distribution methods. Additionally, the advent of artificial intelligence and soft computing techniques with the moth flame optimization (MFO) method leads to superior results in solving complex problems and parameter estimations. The data analytics are carried out in the MATLAB platform, with in-house coding developed for MFO parameters estimated through optimization and other wind distribution parameters using the maximum likelihood method. The observed outcomes show that the MFO method performed well on parameter estimation. Correspondingly, wind power generation was shown to peak at the South West Monsoon periods from June to September, with mean wind speeds ranging from 9 to 12 m/s. Furthermore, the wind speed distribution method of mixed Weibull, Nakagami, and Rician methods performed well in calculating potential assessments for the targeted locations. Likewise, the Gulf of Khambhat (offshore) area has steady wind speeds ranging from 7 to 10 m/s with less turbulence intensity and the highest wind power density of 431 watts/m2. The proposed optimization method proves its potential for accurate assessment of Indian wind conditions in selected locations

Pathways toward high-efficiency solar photovoltaic thermal management for electrical, thermal and combined generation applications: A critical review

Photovoltaic (PV) panels convert a portion of the incident solar radiation into electrical energy and the remaining energy (>70 %) is mostly converted into thermal energy. This thermal energy is trapped within the panel which, in turn, increases the panel temperature and deteriorates the power output as well as electrical efficiency. To obtain high-efficiency solar photovoltaics, effective thermal management systems is of utmost. This article presents a comprehensive review that explores recent research related to thermal management solutions as applied to photovoltaic technology. The study aims at presenting a wide range of proposed solutions and alternatives in terms of design approaches and concepts, operational methods and other techniques for performance enhancement, with commentary on their associated challenges and opportunities. Both active and passive thermal management solutions are presented, which are classified and discussed in detail, along with results from a breadth of experimental efforts into photovoltaic panel performance improvements. Approaches relying on radiative, as well as convective heat transfer principles using air, water, heat pipes, phase change materials and/or nanoparticle suspensions (nanofluids) as heat-exchange media, are discussed while including summaries of their unique features, advantages, disadvantages and possible applications. In particular, hybrid photovoltaic-thermal (PV-T) collectors that use a coolant to capture waste heat from the photovoltaic panels in order to deliver an additional useful thermal output are also reviewed, and it is noted that this technology has a promising potential in terms of delivering high-efficiency solar energy conversion. The article can act as a guide to the research community, developers, manufacturers, industrialists and policymakers in the design, manufacture, application and possible promotion of high-performance photovoltaic-based technologies and systems

Envisioning the UN Sustainable Development Goals (SDGs) through the lens of energy sustainability (SDG 7) in the post-COVID-19 world

The United Nations (UN) have formulated seventeen Sustainable Development Goals (SDGs) and thus, humans were trying to traverse the sustainable path. Meanwhile, the COVID-19 pandemic has emerged and forced out the ephemeral conventional approaches. Thus, the post-COVID world indicates the need for sustainable development and strategies in par with the ecosystem. The authors propose this study as a guide to direct the post-pandemic scenario into the sustainable pathway by prioritizing energy sustainability to engage the actions for achieving the SDGs. The analysis in this study commences with the investigation of pronounced impacts in the energy sector with its influence on the progress towards sustainability. To pursue the path of energy sustainability, a qualitative analysis is performed in a parallel approach from the key viewpoint of the renewable and sustainable energy transition, digital transformation of the energy sector and energy affordability in the post-COVID world. A SWOT-AHP hybrid methodology is employed to identify the significance of each strategy or issues to be focused on immediately in the post-COVID world. The study also discusses energy sustainability from political bodies and policy makers’ perspective, and the actual scenario where we are headed is revealed with the aid of process-tracing method. Furthermore, a novel quantitative analysis is established to represent the SDG’s interaction and the result shows that the SDG 7 is the underpinning goal in relative to other SDGs. In context with it, the mapping of energy sustainability to the sustainable world is accomplished. The ultimate inference from envisioning the SDGs through energy sustainability shows that a sustainable world would result after the pandemic. However, the changes in the energy market, investment preferences and more importantly, the decisions influenced by the political bodies in the post-COVID-world is decisive in achieving the same in a stipulated time frame

Pathways toward high-efficiency solar photovoltaic thermal management for electrical, thermal and combined generation applications: A critical review

Photovoltaic (PV) panels convert a portion of the incident solar radiation into electrical energy and the remaining energy (>70 %) is mostly converted into thermal energy. This thermal energy is trapped within the panel which, in turn, increases the panel temperature and deteriorates the power output as well as electrical efficiency. To obtain high-efficiency solar photovoltaics, effective thermal management systems is of utmost. This article presents a comprehensive review that explores recent research related to thermal management solutions as applied to photovoltaic technology. The study aims at presenting a wide range of proposed solutions and alternatives in terms of design approaches and concepts, operational methods and other techniques for performance enhancement, with commentary on their associated challenges and opportunities. Both active and passive thermal management solutions are presented, which are classified and discussed in detail, along with results from a breadth of experimental efforts into photovoltaic panel performance improvements. Approaches relying on radiative, as well as convective heat transfer principles using air, water, heat pipes, phase change materials and/or nanoparticle suspensions (nanofluids) as heat-exchange media, are discussed while including summaries of their unique features, advantages, disadvantages and possible applications. In particular, hybrid photovoltaic-thermal (PV-T) collectors that use a coolant to capture waste heat from the photovoltaic panels in order to deliver an additional useful thermal output are also reviewed, and it is noted that this technology has a promising potential in terms of delivering high-efficiency solar energy conversion. The article can act as a guide to the research community, developers, manufacturers, industrialists and policymakers in the design, manufacture, application and possible promotion of high-performance photovoltaic-based technologies and systems

Comparative evaluation of AI-based intelligent GEP and ANFIS models in prediction of thermophysical properties of Fe3O4-coated MWCNT hybrid nanofluids for potential application in energy systems

Hybrid nanofluids are gaining popularity owing to the synergistic effects of nanoparticles, which provide them with better heat transfer capabilities than base fluids and normal nanofluids. The thermophysical characteristics of hybrid nanofluids are critical in shaping heat transmission properties. As a result, before using thermophysical qualities in industrial applications, an in-depth investigation of thermophysical properties is required. In this paper, a metamodel framework is constructed to forecast the effect of nanofluid temperature and concentration on numerous thermophysical parameters of Fe3O4-coated MWCNT hybrid nanofluids. Evolutionary gene expression programming (GEP) and an adaptive neural fuzzy inference system (ANFIS) were employed to develop the prediction models. The model was trained using 70% of the datasets, with the remaining 15% used for testing and validation. A variety of statistical measurements and Taylor's diagrams were used to assess the proposed models. The Pearson's correlation coefficient (R), coefficient of determination (R2) was used for the regression index, the error in the model was evaluated with root mean squared error (RMSE). The model's comprehensive assessment additionally includes modern model efficiency indices such as Kling-Gupta efficiency (KGE) and Nash-Sutcliffe efficiency (NSCE). The proposed models demonstrated impressive prediction capabilities. However, the GEP model (R > 0.9825, R2 > 0.9654, RMSE = 0.7929, KGE > 0.9188, and NSCE > 0.9566) outperformed the ANFIS model (R > 0.9601, R2 > 0.9218, RMSE = 1.495, KGE > 0.8015, and NSCE > 0.8745) for the majority of the findings. The generated metamodel was robust enough to replace the repetitive expensive lab procedures required to measure thermophysical properties

A holistic review of the present and future drivers of the renewable energy mix in Maharashtra, State of India

A strong energy mix of Renewable Energy Sources (RESs) is needed for sustainable development in the electricity sector. India stands as one of the fastest developing countries in terms of RES production. In this framework, the main objective of this review is to critically scrutinize the Maharashtra state energy landscape to discover the gaps, barriers, and challenges therein and to provide recommendations and suggestions for attaining the RES target by 2022. This work begins with a discussion about the RES trends in various developing countries. Subsequently, it scrutinizes the installed capacity of India, reporting that Maharashtra state holds a considerable stake in the Indian energy mix. A further examination of the state energy mix is carried out by comparing the current and future targets of the state action plan. It is found that the installed capacity of RESs accounts for about 22% of the state energy mix. Moreover, the current installed capacity trend is markedly different from the goals set out in the action plan of the state. Notably, the installed capacity of solar energy is four times less than the target for 2020. Importantly, meeting the targeted RES capacity for 2022 presents a great challenge to the state. Considering this, an analysis of the state’s strengths, barriers, and challenges is presented. Moreover, strong suggestions and recommendations are provided to clear the track to reach the desired destination. This can be useful for the government agencies, research community, private investors, policymakers, and stakeholders involved in building a sustainable energy system for the future

A holistic review of the present and future drivers of the renewable energy mix in Maharashtra, State of India

Das, NK ORCiD: 0000-0002-3396-4194A strong energy mix of Renewable Energy Sources (RESs) is needed for sustainable development in the electricity sector. India stands as one of the fastest developing countries in terms of RES production. In this framework, the main objective of this review is to critically scrutinize the Maharashtra state energy landscape to discover the gaps, barriers, and challenges therein and to provide recommendations and suggestions for attaining the RES target by 2022. This work begins with a discussion about the RES trends in various developing countries. Subsequently, it scrutinizes the installed capacity of India, reporting that Maharashtra state holds a considerable stake in the Indian energy mix. A further examination of the state energy mix is carried out by comparing the current and future targets of the state action plan. It is found that the installed capacity of RESs accounts for about 22% of the state energy mix. Moreover, the current installed capacity trend is markedly different from the goals set out in the action plan of the state. Notably, the installed capacity of solar energy is four times less than the target for 2020. Importantly, meeting the targeted RES capacity for 2022 presents a great challenge to the state. Considering this, an analysis of the state’s strengths, barriers, and challenges is presented. Moreover, strong suggestions and recommendations are provided to clear the track to reach the desired destination. This can be useful for the government agencies, research community, private investors, policymakers, and stakeholders involved in building a sustainable energy system for the future.</jats:p

Smart Battery Management Technology in Electric Vehicle Applications: Analytical and Technical Assessment toward Emerging Future Directions

Electric vehicles (EVs) have received widespread attention in the automotive industry as the most promising solution for lowering CO2 emissions and mitigating worldwide environmental concerns. However, the effectiveness of EVs can be affected due to battery health degradation and performance deterioration with lifespan. Therefore, an advanced and smart battery management technology is essential for accurate state estimation, charge balancing, thermal management, and fault diagnosis in enhancing safety and reliability as well as optimizing an EV’s performance effectively. This paper presents an analytical and technical evaluation of the smart battery management system (BMS) in EVs. The analytical study is based on 110 highly influential articles using the Scopus database from the year 2010 to 2020. The analytical analysis evaluates vital indicators, including current research trends, keyword assessment, publishers, research categorization, country analysis, authorship, and collaboration. The technical assessment examines the key components and functions of BMS technology as well as state-of-the-art methods, algorithms, optimization, and control surgeries used in EVs. Furthermore, various key issues and challenges along with several essential guidelines and suggestions are delivered for future improvement. The analytical analysis can guide future researchers in enhancing the technologies of battery energy storage and management for EV applications toward achieving sustainable development goals