Different Climatic Zones Of India Research Articles

Reliability-Based Pavement Roughness Progression Modeling Using Bayesian Approach

This paper aims to develop reliability-based roughness progression models for bituminous pavements across different climatic zones in India. The methodology involves dividing the Indian geographical region into six climatic zones with specific climatic characteristics that influence pavement deterioration. This delineation is achieved using K-means clustering and considers parameters such as temperature, rainfall, and humidity. The roughness progression models and reliability interpretations are developed through a Bayesian regression framework. Initially, International Roughness Index (IRI) progression models are created for three climatic zones: hot and dry, warm and humid, and moderate. These models are based on time series data from 2015 to 2016, gathered from 130 pavement sections in each zone. To validate the models, additional time series data from 2016 to 2017 are utilized. The forecast results indicate that the hot and dry zone exhibits the highest IRI progression rate for specified causal variable values, followed by the warm and humid zone and the moderate zone. The adoption of the Bayesian regression framework provides probabilistic parameter distributions for model coefficients, enabling the assessment of model-level reliability. The study demonstrates that, by adjusting the quartile of causal variables, the overall reliability of the model can be improved, thereby reducing the deviation from actual IRI values. Moreover, it is possible to evaluate variable-level reliability by examining the influence of individual variables on roughness progression.

Seasonal dependence of characteristics of rain drop size distribution over two different climatic zones of India

Seasonal dependence of characteristics of rain drop size distribution over two different climatic zones of India

Techno-economic analysis of hybrid renewable power generation system under different climatic zones in India

Techno-economic analysis of hybrid renewable power generation system under different climatic zones in India

Effect of salinity stress on morphological characters of Bajra napier hybrid grass [Pennisetum glaucum (L.) R. Br.]x [Pennisetum purpureum (K.) Schm] varieties

Salinity is a significant constraint in expressing thecrop’s full potential. Bajra Napier Hybrid grass is a highyielding,good-quality fodder grass with palatability grown across different climatic zones of India. A field experiment wasconducted at the Indian Grassland and Fodder Research Institute, Dharwad, during 2020-21 to evaluate the effect of salinitystress tolerance in seven established BN Hybrid genotypes,viz.,DHN 6, DHN 15, COBN 5, CO 6, BNH 10, IGFRI 7andPHULE YASHWANT at 0 and 12 ECe. The morphological characters like plant height, the number of tillers, the numberof green leaves per tiller, regeneration index, and total fresh weight were recorded in these seven genotypes under salinityand control conditions. The maximum height was observed in Phule Yashwant, whereas the minimum height was recordedin genotype DHN 6. BNH 10 recorded the highest regeneration index. The highest total fresh biomass was recorded in thePhule Yashwant genotype, irrespective of salinity levels. This study revealedthat Phule Yashwant displayed the highestfresh biomass among all the genotypes, and the biomass of BNH 10 was on par.Whereas CO 6 and DHN 6 were the poorperformers. Higher tiller number, regeneration index, and low leaf-to-stem ratio were the strategies adopted by the tolerantvarieties under higher salt levels.

Excess Mortality Risk Due to Heat Stress in Different Climatic Zones of India.

India is at a high risk of heat stress-induced health impacts and economic losses owing to its tropical climate, high population density, and inadequate adaptive planning. The health impacts of heat stress across climate zones in India have not been adequately explored. Here, we examine and report the vulnerability to heat stress in India using 42 years (1979-2020) of meteorological data from ERA-5 and developed climate-zone-specific percentile-based human comfort class thresholds. We found that the heat stress is usually 1-4 °C higher on heatwave (HW) days than on nonheatwave (NHW) days. However, the stress on NHW days remains considerable and cannot be neglected. We then showed the association of a newly formulated India heat index (IHI) with daily all-cause mortality in three cities - Delhi (semiarid), Varanasi (humid subtropical), and Chennai (tropical wet and dry), using a semiparametric quasi-Poisson regression model, adjusted for nonlinear confounding effects of time and PM2.5. The all-cause mortality risk was enhanced by 8.1% (95% confidence interval, CI: 6.0-10.3), 5.9% (4.6-7.2), and 8.0% (1.7-14.2) during "sweltering" days in Varanasi, Delhi, and Chennai, respectively, relative to "comfortable" days. Across four age groups, the impact was more severe in Varanasi (ranging from a 3.2 to 7.5% increase in mortality risk for a unit rise in IHI) than in Delhi (2.6-4.2% higher risk) and Chennai (0.9-5.7% higher risk). We observed a 3-6 days lag effect of heat stress on mortality in these cities. Our results reveal heterogeneity in heat stress impact across diverse climate zones in India and call for developing an early warning system keeping in mind these regional variations.

Putting into practice a decision-making framework for a thorough performance and location evaluation of solar photovoltaic plants in India from distinctive climate zones

Solar energy utilization has evolved tremendously in many countries to compete with conventional energy sources due to abundant availability, ongoing module price declines, and efficiency gains. Besides these advantages, most solar power plants continue to underperform due to various technical and environmental factors. Some solar projects in India even failed to meet the generation targets. Hence, to achieve sustainable development goals and be future-ready, solar power plants must be sustainably developed for power generation. One should focus on every sustainable aspect before constructing a powerplant, i.e., proper location selection. This study focuses on this problem and considers seven solar photovoltaic plants from different climatic zones in India to compare their performance and location criteria using fuzzy multi-criteria decision-making methods. Fuzzy Stepwise Weight Assessment Ratio Analysis is used to determine the criteria significance, while Fuzzy COmplex PRoportional ASsessment and Fuzzy ELimination Et Choix Traduisant la REalité (ELimination Et Choice Translating REality) are used to rank the seven solar photovoltaic plants. Given solar photovoltaic plants' high initial investment cost, economic criteria precede all other evaluation criteria in assessment, followed by technical, environmental, and connectivity criteria. The social criteria are neglected here because of the minor societal concerns about deploying solar photovoltaic plants. According to the results, the Unchahar solar photovoltaic plant (from the composite climatic zone) outperforms other selected photovoltaic plants and is determined to be the most appropriate plant. This is due to the lowest levelized cost of energy and lowest total cost for the Unchahar solar photovoltaic plant, as these criteria are the most significant per the study. This study's findings will be useful to energy decision-makers and may serve as a reference for constructing and developing future solar plants. Further, this study will make recommendations for future solar photovoltaic plant development. The study also includes sensitivity analysis to determine the data's robustness. This study is very robust, as the normalized alternative is constant for each criterion, and the ranking remains the same.

Solar irradiance forecasting models using machine learning techniques and digital twin: A case study with comparison

The ever-increasing demand for energy and power consumption due to population growth, economic expansion, and evolving consumer choices has led to the need for renewable energy sources. Traditional energy sources such as coal, oil, and gas have contributed to global pollution and have adverse effects on human health. As a result, the use of renewable energy for power generation has increased tremendously. One such area of research is solar irradiation prediction, which utilizes Artificial Intelligence and Machine Learning techniques. With the use of real-time predicted data, the digital twins are intended to add value to the organization by identifying and preventing problems, predicting performance, and improving operations. This paper provides an overview of various learning methods used for predicting irradiance and presents a new ensemble solar irradiance forecasting model that combines eight machine learning models to ensure model diversity. The model's most critical factors for predicting irradiance include temperature, cloudiness index, relative humidity, and day of the week. To conduct a comprehensive analysis, the proposed 8-Stacking Regression Cross Validation (8 STR-CV) model was tested using data from three different climatic zones in India. The model's high accuracy scores of 98.8% for Visakhapatnam, 98% for Nagpur, and 97.8% for the mountainous region make it a valuable tool for future prediction in various sectors, including power generation and utilization planning.

Investigation of annual performance of a building shaded by rooftop PV panels in different climate zones of India

Photovoltaics panels are generally used on rooftop for electricity generation. However, installation of PV on the rooftop also has potential impact on the heating and cooling load of the building. This work studied these indirect benefits of rooftop PV panels by conducting experiments in Raipur, India, and compared the results with the exposed roof. Further, mathematical model is presented to analyze the annual effect of PV shading in terms of thermal load saving and power generation. Annual variation of cooling/heating load, PV power generation and overall energy-saving efficiency index is presented for different climatic zones of India. Average annual reduction in the roof and ceiling temperatures for different cities are in the range 6.05–10.96 °C and 3.94–7.15 °C, respectively. Annual cooling load reduced by 70.33–94.37%. Although PV panels shading shows negative impact during the winter season by increasing the heating load of building in all the climatic zones, the overall thermal load of building may reduce up to 22.76–74.07%. Furthermore, the overall energy-saving efficiency index varies from 21.27 to 25.12%, with maximum efficiency observed for Jodhpur city followed by Raipur. Overall, this study highlights the potential of rooftop PV application in buildings under different climatic zones of India.

On the thermal performance of naturally ventilated room with straw insulation retrofitted envelope for different climatic zones of India

The heat insulating potential of straw, an agricultural residue, used as part of the envelope of unconditioned buildings is the focus of the current work. Using a CFD software with experimentally validated boundary conditions, transient thermal analysis is performed on a 3-D model room of dimensions 3 m × 3 m × 2.5 m with vents and heat source representing a naturally ventilated indoor space. The thermal performances of a reference uninsulated concrete envelope and a straw insulation-based envelope are compared in terms of quantitative parameters such as indoor air temperature, decrement factor, and time lag as well as a qualitative discussion on the need for occupant adaptation, namely closing or opening of inlet vent and requirement of additional heating. The assessment and subsequent recommendation of straw insulation utilization is done for representative summer and winter days of the five different climatic zones of India. A maximum reduction of up to 3.5 °C in the peak indoor air temperature is achievable with inlet temporarily closed when straw insulation is included in the envelope in summer conditions. In winter conditions of cold climatic zone, with inlet always closed, use of straw insulation leads to the lessening of additional active heat source requirement by as much as 600 W for the considered model room. Further, the effects of increased straw insulation thickness, removal of heat source, varying inlet air velocity, and changing the position of the straw insulation to the outside are also analyzed for the extreme warm and cold conditions.

Development of machine learning models based on air temperature for estimation of global solar radiation in India

AbstractThe designing of solar thermal systems need accurate information on global solar radiation (GSR). In the present study, six machine learning models, for example, random forest, k‐nearest neighbors, Gaussian process regression, support vector machine, multilayer perception, and XGBoost, are developed for GSR prediction with only air temperature as input for different climatic zones of India. The performance of machine learning models is also compared with some well‐known empirical models. The results show that generally, the performance of the machine learning models is better than empirical models, though, for a few climatic zones, empirical models give a better prediction. The top‐performing models are k‐nearest neighbors and XGBoost. Thus, we highly recommend temperature‐based models to predict GSR in the regions of India where only air temperature data are available. The accurate information of future GSR can be easily obtained by combining future air temperature forecasts with KNN/XGBoost models. These models can be extremely helpful in designing solar thermal systems in those regions where solar radiation facility is not available.

Development of an analytical model for cost-based optimization of KVIC biogas plant dimensions

This study has been carried out in response to minimizing the capital cost of construction at optimum efficiency from the Khadi and Village Industries Commission (KVIC) biogas plant. The main aim was to find out the optimized dimensions of KVIC in the different climatic zones of India with varying hydraulic retention times (HRT). For this study, a 2 m3 capacity biogas plant was selected and the analytical model has been developed to generate optimized dimensions; the result were used to generate the equations to obtain the directly optimized dimensions for KVIC biogas plant. As per the cost of floating drum and cost of construction of digester, the various dimensions have been calculated from the developed equations and concluded that lowering the cost of the floating drum alone translates to typical plants narrow and deep digesters. However, developed model shall be useful for quick prediction of dimensions of KVIC biogas plant.

Quantitative Analysis of Bioactive Carbazole Alkaloids in Murraya koenigii (L.) from Six Different Climatic Zones of India Using UPLC/MS/MS and Their Principal Component Analysis.

Murraya koenigii (L.) Spreng (Curry leaf) is a commercially important medicinal plant in South Asia, containing therapeutically valuable carbazole alkaloids (CAs). Thus, the quantitative evaluation of these compounds from different climatic zones of India are an important aspect for quality assessment and economic isolation of targeted compounds from the plant. In this study, quantitative estimation of CAs among 34 Indian natural populations of M. koenigii was assessed using UPLC/MS/MS. The collected populations represent the humid subtropical, tropical wet & dry, tropical wet, semi-arid, arid, and montane climatic zones of India. A total of 11 CAs viz. koenine-I, murrayamine A, koenigine, koenimbidine, koenimbine, O-methylmurrayamine A, girinimbine, mahanine, 8,8''-biskoenigine, isomahanimbine, and mahanimbine were quantified using multiple reaction monitoring (MRM) experiments within 5.0 min. The respective range for natural abundance of CAs were observed as 0.097-1.222, 0.092-5.014, 0.034-0.661, 0.010-1.673, 0.013-7.336, 0.010-0.310, 0.010-0.114, 0.049-5.288, 0.031-1.731, 0.491-3.791, and 0.492-5.399 mg/g in leaves of M. koenigii. The developed method shown linearity regression coefficient (r2 >0.9995), LOD (0.003-0.248 ng/mL), LOQ (0.009-0.754 ng/mL), and the recovery was between 88.803-103.729 %. The bulk of these CAs were recorded in their highest concentrations in the humid subtropical zone, followed by the tropical wet & dry zones of India. Further, principal component analysis (PCA) was performed which differentiated the climatic zones according to the dominant and significant CAs contents within the populations. The study concludes that the method established is simple, rapid, with high sample throughput, and can be used as a tool for commercial purposes and quality control of M. koenigii.

Climate Responsive Strategy Matrix for Designing Buildings in India

The starting point for a good design of any building project is the analysis of the macroclimate and microclimate of the building site that encompasses an understanding of temperature, radiation, wind, precipitation, topography, vegetation, ground cover, etc., which together describe the site climate. Although urban context somewhat unifies, climate and topography prevalent in India are varied and diverse. Most part of the country is hot, while some regions are dry throughout the year, some are humid and some others are composite. The building design features, therefore, need to vary with the diversity to provide comfortable environments naturally. The context and the requirements for thermal comfort provide the basis for building siting, selection of building form and envelope, fenestration design, choice of materials, and other aspects. The paper proposes and discusses the various climate-responsive design strategies that are best adapted for the different climatic zones of India and presents such design interventions and features as a comparative matrix. Such a comparative presentation is novel, convenient, easy to comprehend, and provides a useful toolkit for building designers. The climate-responsive interventions in building design proposed in this study have the potential to enhance built environments naturally, thereby mitigating the adverse environmental impact. The proposed strategies are also validated through a sample field survey responded by building professionals from various climatic zones of the country.

Thermal modelling of single and double slope passive solar stills for different climatic zones in India

Thermal modelling of Single Slope Passive Solar Still (SSSS) and Double Slope Passive Solar Still (DSSS) has been carried out for different climatic zones in India and a MATLAB code has been developed to analyse solar radiation and yield which were validated for yield output with benchmark results. Temperature variations on hourly basis at glass cover and basin water, heat transfer coefficients and yield were evaluated. For maximum yield output based on latitude angle, the yield variation of SSSS and DSSS for six cities of India from different climatic zones was compared. It is observed that the corresponding percentage increase in yields of DSSS over SSSS for cities, such as Chennai, Goa, Jaipur, Mumbai, New Delhi and Raipur, are 91.3%, 89.3%, 70.92%, 93.25%, 56.88% and 75.87% for a summer day and 63.37%, 57.91%, 55.59%, 44.30%, 6.15% and 34.29% for a winter day, respectively.

Comprehensive exergetic, sustainability and enviro-economic evaluation of single-stage and hybrid evaporative coolers in India

A comprehensive exergetic evaluation of six single-stage and multi-stage evaporative cooling modes along with a standalone direct expansion cooling system (DX) is conducted based on an eight-story building simulation carried out using EnergyPlus. The exergy analysis is carried out for 21 different locations across India covering five different climatic zones, namely arid, semi-arid, humid subtropical, tropical wet and dry, and tropical wet zones, based on building simulation for one day. As the three-stage evaporative cooler is known for the highest cooling range, the exergy analysis is further carried out based on a one-year simulation with a three-stage system integrated into the eight-story building in five different locations, namely Jodhpur, Hyderabad, Nagpur, Chennai and Panaji, representative of five different climatic zones in India. A fixed temperature of 23 °C, pressure after process completion and saturated humidity ratio at the outlet of the cooling system are considered as dead state properties throughout the analysis. The results revealed that indirect evaporative cooler (IEC) exhibits the highest exergy efficiency approaching 80% among seven cooling modes in all 21 locations in India. However, due to lower cooling capacity, the IEC is suitable for a limited set of climatic conditions. The three-stage cooling system exhibits superior exergy efficiency reaching a magnitude beyond 30% among DX-based multi-stage coolers in arid, semi-arid, and humid subtropical zones. It is also found that the three-stage cooling results in superior exergy efficiency in Jodhpur, Hyderabad, and Nagpur, reaching a maximum of 37.5% during one year.

Accelerated stress testing of potential induced degradation susceptibility of PV modules under different climatic conditions

Potential Induced Degradation (PID) is one of the severe failure modes observed in the field with high system voltage, high humidity and high operating temperature. PV module needs to be tested for PID susceptibility as per site conditions before installation in the field. In this paper the effect of temperature, humidity, and voltage on leakage current, as well as PID, has been analyzed for multi-crystalline silicon and HIT module technologies. Also, reported the effect of PID stress on PV modules in terms of electrical degradation and visual defects. For the studied multi-crystalline module, deviation of degradation between one cycle and three cycles of PID stress for the Isc is 98%, Voc is 18% and Pmax degradation is 87%. This paper also reported a case study about the designing of accelerated stress testing of the PID of the PV module for different climatic zones of India. The stress testing condition was designed at harsh conditions of temperature and humidity for representative sites of climatic zones. It was found that the required testing time for PV module in the case of Hot & humid climatic zone is more as compared to other climatic zones.

Proposal of a unique index for selection of optimum phase change material for effective thermal performance of a building envelope

Phase change material (PCM) shows excellent potential to prevent heat gain and reduce the cooling load of buildings. However, the selection of suitable PCM for a given climatic condition becomes important for the effective thermal performance of the building envelope. In the present work, based on the key thermal responses i.e. total heat gain, time lag, and decrement factor, a new Measure of Key Response index (MKR index) is introduced that is used as an evaluation criterion for selecting a suitable PCM for building envelope. For the same, a numerical analysis is carried out for seven cities representing different climatic zones of India during the six months of the summer season from March to August. Seven commercially available PCMs are considered for the analysis. The most suitable PCM for each city is determined based on the MKR index. The results indicated that the selection of suitable PCM can lead to a more than 50% reduction of total heat gain for all seven cities.

Proportional Trends of Continuous Rainfall in Indian Summer Monsoon

A comprehensive study on the Indian summer monsoonal rainfall (ISMR) is performed in the light of decadal changes in the continuous rainfall events and the number of rainy days using 68 years (1951–2018) of gridded rain gauge data. Non-parametric Mann–Kendall’s test is applied on total rainfall amount, the number of rainy days, number of continuous rainfall events, and rainfall magnitude to find trends over different climatic zones of India for the two periods, 1951–1984 and 1985–2018. Our results found a decreasing trend for more than 4-days of continuous rainfall events during the recent 34 years (1985–2018) compared to 1951–1984. The rate of increase/decrease in extreme/continuous rainfall events does not follow a similar trend in number of continuous rainfall events and magnitude. Moreover, the rainfall is shifted towards a lesser number of continuous rainfall days with higher magnitudes during 1985–2018. During the crop’s sow season (i.e., the first 45 days from the onset date of Indian monsoon), the total number of rainy days decreased by a half day during the last 34 years. Over the Central and North East regions of India, the number of rainfall days decreased by ~0.1 days/yr and ~0.3 days/yr, respectively, during 1985–2018. Overall, the decreasing trends in continuous rainfall days may escalate water scarcity and lead to lower soil moisture over rain-fed irrigated land. Additionally, an upsurge in heavy rainfall episodes will lead to an unexpected floods. On a daily scale, rainfall correlates with soil moisture and evaporation up to 0.87 over various land cover and land use regions of India. Continuous light-moderate rainfall seems to be a controlling factor for replenishing soil moisture in upper levels. A change in rainfall characteristics may force the monsoon-fed rice cultivation period to adopt changing rainfall patterns.

Case Study of Grid-Connected Photovoltaic Power System Installed at Monthly Optimum Tilt Angles for Different Climatic Zones in India

Elevated price of renewable energy (RE) systems slowed its adoption in many countries. Hence, it is important to select an optimal size of the system in order to decrease cost, excess energy produced by RE system. The RE system is used to minimize air pollution and energy security. The aim of this study is to evaluate and compare the techno-economic performance of grid-connected photovoltaic (PV) power systems for a rooftop solar PV building containing 14 families in six regions with different climate zones in India. For this purpose, grid connected PV (Grid-PV) is installed at optimum tilt angles (OTA). Then, techno-economic performance of these systems is performed in the six climatic zones in India, which is the novelty of this study. RE resources and ambient temperature for different seasons are considered during analysis. The load is fixed for all the sites for a better comparison in the study. The results show that using OTA in Grid-PV system reduces greenhouse gas emissions, e.g. COx, SOx and NOx, decreases payback time while increasing overall PV production and other project productivity parameters. These include specific yield, PV penetration, return on investment, Internal Rate of Interest, Net Present Value, Annualized Saving, Energy sold to the grid for all climatic zones in India proving useful for industry. Using these metrics and results in this paper, researchers and project developers, policy makers can promote better use of renewable energy. Therefore, it is necessary to use Grid-PV with OTA for different climatic zones.

Novel Approach to Investigate the Influence of Optimum Tilt Angle on Minimum Cost of Energy-Based Maximum Power Generation and Sizing of PV Systems: A Case Study of Diverse Climatic Zones in India

Optimum Photovoltaic (PV) system integration in power grid depend upon the total of power accessible from the PV. To figure the PV systems highest power yield, PV panels must be positioned at an optimal tilt angle (OPTA) to absorb maximum solar radiation (SR). This OPTA is a function of the latitude, clearness index, diffuse SR, global SR, direct SR and optimum PV size. Therefore OPTA has an impact on maximum power generation and optimal PV system sizing. The PV is not installed at OPTA for most of the sites in India which is important for maximum power generation and optimum sizing of standalone PV systems. This results in variation of OPTA from site to site and its effect on PV sizing needs to be investigated. The innovative aspect of this work is the calculation of OPTA, which are employed as sensitive factors in Hybrid Optimization of Multiple Energy Resources (HOMER), to determine their impact on maximum optimum sizing and power generation for 26 cities in India's various climate zones. This methodology can be applied all over the world to determine the impact of OPTA on maximum power generation and size. It is found that OPTA varies from 63° to 0° throughout year in India and it is maximum for December in India. The results indicates that Net Present Cost varies from $\$ $ 1105 to $\$ $ 1280 and Cost of Energy (COE) variation is 0.041 to 0.048 $\$ $ /kWh throughout India cities and low temperature sites are good for photovoltaic (PV) power generation. Two axis tracking system produces more power in comparison to other tracking systems. This research is beneficial for researcher and industry to install PV system in different climatic zones of India to generate maximum power at minimum cost of energy.