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This paper reviews and assesses India's energy policy in the context of its commitment to achieve the target of net-zero carbon emissions by 2070. It emphasizes the central role of green electrification, particularly through the expansion of solar power capacity. It discusses policies outside the energy sector that need to be part of a strategy of achieving this target, including the use of information technology, infrastructure development, and transportation. Furthermore, it examines possible policy options for accelerating the target date to 2050, especially with the use of carbon capture to manage the transition from the current heavy use of coal. The paper also discusses the possible financial and growth implications of various strategy options, arguing that feasible external financing commitments can allow India to achieve net-zero goals without sacrificing economic growth. [ABSTRACT FROM AUTHOR]

This paper explores business models for community energy storage (CES) and examines their potential and feasibility at the local level. By leveraging Multi Criteria Decision Making (MCDM) approaches and real-world case studies in Europe and India, it presents insights into CES deployment opportunities, challenges, and best practices. Different business models, including community energy cooperatives, utility–community partnerships, demand response, energy services, and market mechanisms, are analyzed. The proposed method combines the MCDM method PROMETHEE II with the fuzzy set theory to obtain a complete CES business model ranking, addressing project uncertainties. The analysis emphasizes CES's role in balancing local renewable energy supply and demand, facilitating energy sharing, and achieving energy independence. Findings prioritize models like Community Cooperative, Energy Arbitrage, and Energy Arbitrage Peak Shaving for CES with renewables. Environmental benefits include reduced diesel use and greenhouse gas emissions. Efficient cooperatives are advocated to recover costs and enable competitive energy prices. The paper highlights the need for novel value propositions to boost the energy transition in local communities. This research contributes to the discourse on CES business models, fostering knowledge exchange and promoting effective strategies for sustainable energy systems. [ABSTRACT FROM AUTHOR]

This paper provides a summary of financial instruments to address two biggest risks to renewable projects in India. These risks include the following: first, off-taker (or counterparty) risk, which relates to payment delays by public-sector distribution companies to independent power producers, which then impact project level cash flows in the domestic currency; second, currency (or foreign exchange) risk related to currency fluctuations, which impact foreign investor level cash flows in foreign currencies. This paper then describes multiple solutions for each of these risks, using public funding mechanisms. For payment delays, the category of solutions is termed Payment Security Mechanisms; whereas, for currency fluctuations, the category of solutions is termed Foreign Exchange Hedging Facilities. The coverage in this paper shows the evolution of the solutions from theory to practice over time. These solutions are likely to be applicable to other developing countries. [ABSTRACT FROM AUTHOR]

Electric vehicles reduce pollution only if a high percentage of the electricity mix comes from renewable sources and if the battery manufacturing takes place at a site far from the vehicle use region. Industries developed due to increased electric vehicle adoption may also cause additional air pollution. The Indian government has committed to solving New Delhi's air pollution issues through an ambitious policy of switching 100% of the light duty consumer vehicles to electric vehicles by 2030. This policy is based on vehicle grid interaction and relies on shared mobility through the electric vehicle fleet. There are several human behavioral changes necessary to achieve 100% adoption of electric vehicles. This paper reviews different steps in the lifecycle of an electric vehicle (EV), their impact on environmental emissions, and recommends policies suitable for different socio-economic group that are relevant to the Indian market. To reduce air pollution through adoption of electric vehicles, the Indian government needs to adopt policies that increase sale of electric vehicles, increase percentage of renewable energy in the electricity mix, and prevent air pollution caused from battery manufacturing. The recommended policies can be customized for any market globally for reducing air pollution through increased adoption of electric vehicles. [ABSTRACT FROM AUTHOR]

Offshore wind turbine (OWT), a sustainable energy source, has recently gained wide attention. The energy demand for India is soaring high as it is a fast-developing nation in terms of industrialization; however, the interest shown by India toward renewable energy is low, especially for OWTs. This study aims to identify, categorize, and evaluate the criteria needed to be considered in the installation of OWTs and selection of potential locations in India. Based on literature analysis and exploratory interviews with experts, six aspects, namely, climatic conditions, regional features, investments and benefits, environmental impact, economic impact, and social and technical impact, with a total of twenty-six criteria, were identified and evaluated. An integrated approach of data envelopment analysis (DEA) with grey analytical hierarchy process (GAHP) and grey Complex proportional assessment (GCOPRAS) is used to evaluate the criteria and also to identify the locations for OWTs. Soil condition, extreme wind speed, seismic movement, tidal flow, and closeness to the power transmission grid have been identified as the top five criteria to be considered in the installation of OWTs. Gujarat, Tamil Nadu, Odisha, the Lakshadweep Islands, and the Andaman and Nicobar Islands have been identified as potential locations for installing OWTs in India. The outcomes of this study will deliver better insights for the practitioners about the criteria that need to be considered in OWTs. Further, this study sheds light on the importance of OWTs in an Indian context, which can possibly attract more investments. [ABSTRACT FROM AUTHOR]

This paper investigates the nexus between CO2 emissions (CO2E), GDP, energy use (ENU), and population growth (PG) in India from 1980–2018 by comparing the "vector error correction" model (VECM) and "auto regressive distributed lag" (ARDL). We applied the unit root test, Johansen multi-variate cointegration, and performed a Variance decomposition analysis using the Cholesky approach. The VECM and ARDL-bound testing approaches to cointegration suggest a long-term equilibrium nexus between GDP, energy use, population growth and CO2E. The empirical outcomes show the existence of a long-term equilibrium nexus between the variables. The Granger causality results show that short-term bi-directional causality exists between GDP and ENU, while a uni-directional causality between CO2E and GDP, CO2E and ENU, CO2E and PG, and PG and ENU. Evidence from variance decomposition indicates that 58.4% of the future fluctuations in CO2E are due to changes in ENU, 2.8% of the future fluctuations are due to changes in GDP, and 0.43% of the future fluctuations are due to changes in PG. Finally, the ARDL test results indicate that a 1% increase in PG will lead to a 1.4% increase in CO2E. Our paper addresses some important policy implications. [ABSTRACT FROM AUTHOR]

In the present work, India's primary energy use is analysed in terms of four socio-economic variables, including Gross Domestic Product, population, and the amounts of exports and imports. Historical data were obtained from the World Bank database for 44 years as annual values (1971–2014). Energy use is analysed as an optimisation problem, where a unique ensemble of two metaheuristic algorithms, Grammatical Evolution (GE), and Differential Evolution (DE), is applied. The energy optimisation problem has been investigated in two ways: estimation and a year-ahead prediction. Models are compared using RMSE (objective function) and further ranked using the Global Performance Index (GPI). For the estimation problem, RMSE values are found to be as low as 0.0078 and 0.0103 on training and test datasets, respectively. The average estimated energy use is found in good agreement with the data (RMSE = 6.3749 kgoe/capita), and the best model (E10) has an RMSE of 5.8183 kgoe/capita, with a GPI of 1.7249. For the prediction problem, RMSE is found to be 0.0096 and 0.0122 on training and test datasets, respectively. The average predicted energy use has RMSE of 7.8857 (kgoe/capita), while Model P20 has the best value of RMSE (7.9201 kgoe/capita) and a GPI of 1.8836. [ABSTRACT FROM AUTHOR]

With an increasing expected energy demand and current dominance of coal electrification, India plays a major role in global carbon policies and the future low-carbon transformation. This paper explores three energy pathways for India until 2050 by applying the linear, cost-minimizing, global energy system model (GENeSYS-MOD). The benchmark scenario "limited emissions only" (LEO) is based on ambitious targets set out by the Paris Agreement. A more conservative "business as usual" (BAU) scenario is sketched out along the lines of the New Policies scenario from the International Energy Agency (IEA). On the more ambitious side, we explore the potential implications of supplying the Indian economy entirely with renewable energies with the "100% renewable energy sources" (100% RES) scenario. Overall, our results suggest that a transformation process towards a low-carbon energy system in the power, heat, and transportation sectors until 2050 is technically feasible. Solar power is likely to establish itself as the key energy source by 2050 in all scenarios, given the model's underlying emission limits and technical parameters. The paper concludes with an analysis of potential social, economic and political barriers to be overcome for the needed Indian low-carbon transformation. [ABSTRACT FROM AUTHOR]

India's Nationally Determined Contributions (NDC) aim to increase the share of non-fossil fuel, especially renewables, in power generation. But at the same time, it mentions that coal is likely to dominate the power generation in the short and medium term to meet the increase in demand and support the intermittency of renewable energy-based power generation. Thus, additional efforts to transform the thermal power generation to a more efficient and less emitting one in the near term by increasing the use of natural gas (a fossil fuel with a lower emission factor than coal) may be planned towards achieving India's additional mitigation commitments. The paper presents the implications of a proposed increase in the share of natural gas in thermal power generation of India by looking into the trade-off between emission mitigation and energy security. Along with a Reference Scenario, three alternative emission scenarios are proposed to understand the likely impacts of increased penetration of natural gas in power generation on India's projected emission profile up to 2050. Results suggest that higher mitigation potential can be achieved through fuel-switch in thermal generation and technological up-gradation to enhance energy use efficiency. The energy security due to the increased share of natural gas imports can be dealt with by diversifying import sources. Shifts can achieve this in the emphasis on long-term contracts. [ABSTRACT FROM AUTHOR]

Technologies influencing alternativemeans of transportation have been expanding in recent years due to increasing urbanization and motorization. In this paper, a solar powered electric auto-rickshaw (SPEA) is designed and developed for Indian conditions. The vehicle developed is comprehensively analyzed techno-economically for its viability in the Indian market. The performance analysis of SPEA results in an optimal charging rate of 2 kWh per day with an average solar irradiance of 325 W/m2 on a typical sunny day. The discharging characteristics are studied based on different loading conditions. The vehicle achieved a maximum speed of 21.69 km/h with battery discharge rate of 296Wat 90 kg load and also reached a maximum discharge rate of 540Wat 390 kg loading with a maximum speed of 12.11 km/h. Environmental analysis of SPEA indicated that the yearly CO2 emissions of 1777 kg, 1987 kg and 1938 kg from using Compressed Natural Gas, Liquefied Petroleum Gas and gasoline engines respectively can be mitigated using SPEA. The financial analysis of SPEA concluded that the investor's payback duration is 24.44% less compared to a gasoline-run vehicle. Socio-Economic analysis of SPEA discussed its significant advantages and showed 15.74% and 0.85% increase in yearly income over gasoline driven and battery driven vehicles. [ABSTRACT FROM AUTHOR]

Policymakers and environmental scientists have proposed numerous measures toward achieving a sustainable environment. Some of these measures include the efficient use of energy and a clean energy transition. This study empirically investigates the role of non-renewable energy efficiency and renewable energy utilization in driving environmental sustainability in India over the period from 1965 to 2018. Using the approach of the Dynamic Autoregressive Distributed Lag (DyARDL) simulations, the empirical evidence shows that non-renewable energy efficiency and renewable energy utilization promote environmental sustainability through an increase in the load capacity factor. The effects of financial development and trade impede environmental sustainability through a decrease in the load capacity factor. The results further show that the relationship between income and load capacity factor is characterized by an inverted U-shape. This suggests that the load capability curve (LCC) hypothesis is not valid for India. Given the overall findings of this study, it is suggested that policymakers should promote energy efficiency and renewable energy technologies as the ultimate policy measure to mitigate the accumulation of CO2 emissions and other significant climatic changes in India. [ABSTRACT FROM AUTHOR]

An Indian electricity system with very high shares of solar photovoltaics seems to be a plausible future given the ever-falling solar photovoltaic (PV) costs, recent Indian auction prices, and governmental support schemes. However, the variability of solar PV electricity, i.e., the seasonal, daily, and other weather-induced variations, could create an economic barrier. In this paper, we analyzed a strategy to overcome this barrier with demand-side management (DSM) by lending flexibility to the rapidly increasing electricity demand for air conditioning through either precooling or chilled water storage. With an open-source power sector model, we estimated the endogenous investments into and the hourly dispatching of these demand-side options for a broad range of potential PV shares in the Indian power system in 2040. We found that both options reduce the challenges of variability by shifting electricity demand from the evening peak to midday, thereby reducing the temporal mismatch of demand and solar PV supply profiles. This increases the economic value of solar PV, especially at shares above 40%, the level at which the economic value roughly doubles through demand flexibility. Consequently, DSM increases the competitive and cost-optimal solar PV generation share from 33–45% (without DSM) to ~45–60% (with DSM). These insights are transferable to most countries with high solar irradiation in warm climate zones, which amounts to a major share of future electricity demand. This suggests that technologies, which give flexibility to air conditioning demand, can be an important contribution toward enabling a solar-centered global electricity supply. [ABSTRACT FROM AUTHOR]

The goal of limiting global temperature rise to "well below" 2 °C has been reaffirmed in the Paris Agreement on climate change at the 21st Conference of the Parties (COP21). Almost all countries submitted their decarbonization targets in their Intended Nationally Determined Contributions (INDC) to the United Nations Framework Convention on Climate Change (UNFCCC) and India did as well. India's nationally determined contribution (NDC) aims to reduce greenhouse gas (GHG) emissions intensity of national GDP in 2030 by 33–35% compared to 2005. This paper analyzes how India's NDC commitments compare with emission trajectories consistent with well below 2 °C and 1.5 °C global temperature stabilization goals. A top-down computable general equilibrium model is used for the analysis. Our analysis shows that there are significant emission gaps between NDC and global climate stabilization targets in 2030. The energy system requires significant changes, mostly relying on renewable energy and carbon capture and storage (CCS) technology. The mitigation costs would increase if India delays its abatement efforts and is locked into NDC pathways till 2030. India's GHG emissions would peak 10 years earlier under 1.5 °C global temperature stabilization compared to the 2 °C goal. The results imply that India would need financial and technological support from developed countries to achieve emissions reductions aligned with the global long-term goal. [ABSTRACT FROM AUTHOR]

Modern economies run on the backbone of electricity as one of major factors behind industrial development. India is endowed with plenty of natural resources and the majority of electricity within the country is generated from thermal and hydro-electric plants. A few nuclear plants assist in meeting the national requirements for electricity but still many rural areas remain uncovered. As India is primarily a rural agrarian economy, providing electricity to the remote, undeveloped regions of the country remains a top priority of the government. A vital, untapped source is livestock generated biomass which to some extent has been utilized to generate electricity in small scale biogas based plants under the government's thrust on rural development. This study is a preliminary attempt to correlate developments in this arena in the Asian region, as well as the developed world, to explore the possibilities of harnessing this resource in a better manner. The current potential of 2600 million tons of livestock dung generated per year, capable of yielding 263,702 million m³ of biogas is exploited. Our estimates suggest that if this resource is utilized judiciously, it possesses the potential of generating 477 TWh (Terawatt hour) of electrical energy per annum. [ABSTRACT FROM AUTHOR]

Concerns regarding environmental sustainability have generally been an important element in achieving long-term development objectives. However, developing countries struggle to deal with these concerns, which all require specific treatment. As a result, this study explores the interaction between financial development, renewable energy consumption, technological innovations, and CO2 emissions in India from 1980 to 2019, taking into account the critical role of economic progress and urbanization. The Autoregressive Distributed Lag (ARDL) model is used to quantify long-run dynamics, while the Vector Error Correction Model is used to identify causal direction (VECM). According to the study's conclusions, financial development has a considerable positive impact on CO2 emissions. The coefficient of renewable energy consumption and technical innovations, on the other hand, is strongly negative in both the short and long run, indicating that increasing these measures will reduce CO2 emissions. Furthermore, economic expansion and urbanization have a negative impact on environmental quality since they emit a significant amount of CO2 into the atmosphere. The results of the robustness checks were obtained using the Fully Modified Ordinary Least Squares (FMOLS), the Dynamic Ordinary Least Squares (DOLS), and the Canonical Cointegration Regression (CCR) approaches to verify the findings. The VECM results reveal that there is long-run causality in CO2 emissions, financial development, renewable energy utilization, and urbanization. A range of diagnostic tests were also used to confirm the validity and reliability. This study delivers new findings that contribute to the existing literature and may be of particular interest to the country's policymakers in light of the financial system and its role in environmental issues. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

India accounts for six per cent of the world's primary energy consumption. Rapid urbanization and rapid urban population growth have had a serious impact on energy consumption and subsequent carbon emissions. In particular, cities face a complex and interrelated set of challenges across different sectors (building environment, mobility, water and waste management and public services). Re-examining these challenges by integrating smart energy management (SEM) principles is critical for sustainable and low-carbon urban development. In addition, managing energy footprint is one of the most challenging goals for cities, and as existing cities evolve and transform into smart cities, SEM becomes an integral part of the urban transformation. This article comprehensively reviews the different SEM technologies for different sectors (construction, transportation, public services, water and waste), the policies, and the current challenges and opportunities for SEM policy governance in India. Making urban energy smart can manage a city's energy footprint and have a positive impact on future carbon emissions. [ABSTRACT FROM AUTHOR]

With serious energy poverty, especially concerning power shortages, the economic development of India has been severely restricted. To some extent, power exploitation can effectively alleviate the shortage of energy in India. Thus, it is significant to balance the relationship between power supply and demand, and further stabilize the two in a reasonable scope. To achieve balance, a prediction of electricity generation in India is required. Thus, in this study, five methods, the metabolism grey model, autoregressive integrated moving average, metabolic grey model-auto regressive integrated moving average model, non-linear metabolic grey model and non-linear metabolic grey model-auto regressive integrated moving average model, are applied. We combine the characteristics of linear and nonlinear models, making a prediction and comparison of Indian power generation. In this way, we enrich methods for prediction research on electrical energy, which avoids large errors in trends of electricity generation due to those accidental factors when a single predictive model is used. In terms of prediction outcomes, the average relative errors from five models above are 1.67%, 1.62%, 0.84%, 1.84%, and 1.37%, respectively, which indicates high accuracy and reference value of these methods. In conclusion, India's power generation will continue to grow with an average annual growth rate of 5.17% in the next five years (2018–2022). [ABSTRACT FROM AUTHOR]

India is one of the fastest developing countries in the world. To sustain this growth, energy and electricity demands will increase. In 2015, of the 1337 TWh produced, 916 TWh were from fossil fuels. We prepared several models of electricity demand from 2015 to 2030, based on publicly available datasets and trends. Models were tested on data from previous years and adjusted accordingly. From several scenarios, we decided to introduce two possibilities, i.e., a scenario using high energy savings in all sectors, and a scenario counting on a high industrial growth not supported by an equal increase of electricity savings. For both cases we prepared models for extreme situations: (1) where coal- and lignite-based power plants are preferred after slow-down of a renewable energy boom, and (2) with high utilization of renewable energy supported by natural gas and nuclear energy. With GDP and population increasing at the same rate as in previous years, the unambiguous result in all scenarios is a 2 to 3-fold increase of the electricity demand by 2030. On the electricity production side, all scenarios stress the role of coal, renewables and nuclear sources. Both energy and climate policies should be prepared for such a development in advance. [ABSTRACT FROM AUTHOR]

The economic size of the Indian economy and its status as one of the major global emitters of carbon emissions makes the country a good place to study the determinants of environmental degradation in India. The study aims at analyzing the impact of energy, export variety, and foreign direct investment (FDI) on environmental degradation in India in the context of environmental Kuznets curve (EKC) hypothesis. The long run relationship was found between variables of the study through a cointegration test, whereas long run estimates were obtained through cointegration and dynamic ordinary least squares (DOLS). Results of the study reveal that energy consumption, export variety, FDI, and income positively contributed to environmental degradation in India. Results also unveil that the EKC hypothesis does not exist in India. Causality analyses document unidirectional causality from income and FDI to environmental degradation, and bidirectional causality was witnessed between energy consumption and environmental degradation and between export variety and environmental degradation in the long run. The long run and the short run causality highlight that India has to forego the short run economic growth in order to improve its environmental quality and reduce global carbon emissions; however, it will not affect its long term economic development process. [ABSTRACT FROM AUTHOR]

Techno-economic, social, and environmental factors influence a large part of society, predominantly in developing countries. Due to energy poverty and bloating populations, developing countries like India are striving to meet the energy balance. One initiative of India to achieve the country's Renewable Energy Target (RET) is the setting up of the National Solar Mission (NSM) to meet a target of 175 GW (non-hydro) by the year 2022. Prioritizing Renewable Energy (RE) utilization to achieve techno-economic balance is India's primary objective and creating a positive environmental impact is a bonus. In this study, various scenarios are explored by investigating the techno-economic and environmental impact on RE adoption for a small community in India by optimally sizing the Hybrid Renewable Energy System (HRES). This study is an exemplar in understanding and exploring RE utilization, whilst examining the recent RE market in depth and exploring the advantages and disadvantages of the current RE situation by initiating it in a smaller community. Improved Hybrid Optimization using Genetic Algorithm (iHOGA) PRO+ software, (Version 2.4 -Pro+, Created by Dr Rodolfo Dufo López, University Zaragoza (Spain)) is used to size the RE systems. The results are categorized using triple bottom line analysis (TBL analysis) and for different scenarios, the techno-economic, environmental, and social merits are weighed upon. The probable hurdles that India has to surpass to achieve easy RE adoption are also discussed in this work. The influential merits for analyzing the TBL for a real-time scenario are Net Present Cost (NPC), Carbon-di-oxide (CO2) emissions, and job criteria. Compared to Hybrid Optimization of Multiple Energy Resources (HOMER) software, iHOGA remains less explored in the literature, specifically for the grid-connected systems. The current study provides a feasibility analysis of grid-connected RE systems for the desired location. iHOGA software simulated 15 sets of results for different values of loads considered and various acquisition costs of HRES. At least 70% of RE can be penetrated for the Aralvaimozhi community with the lowest value of NPC of the HRES. From the TBL analysis conducted, integrating HRES into a micro-grid for the community would result in mitigating CO2 emissions and provide job opportunities to the local community; although, the economic impact should be minimized if the acquisition costs of the HRES are reduced, as has been established through this study. [ABSTRACT FROM AUTHOR]

Continual energy availability is one of the prime inputs requisite for the persistent growth of any country. This becomes even more important for a country like India, which is one of the rapidly developing economies. Therefore electrical energy's short-term demand forecasting is an essential step in the process of energy planning. The intent of this article is to predict the Total Electricity Consumption (TEC) in industry, agriculture, domestic, commercial, traction railways and other sectors of India for 2030. The methodology includes the familiar black-box approaches for forecasting namely multiple linear regression (MLR), simple regression model (SRM) along with correlation, exponential smoothing, Holt's, Brown's and expert model with the input variables population, GDP and GDP per capita using the software used are IBM SPSS Statistics 20 and Microsoft Excel 1997–2003 Worksheet. The input factors namely GDP, population and GDP per capita were taken into consideration. Analyses were also carried out to find the important variables influencing the energy consumption pattern. Several models such as Brown's model, Holt's model, Expert model and damped trend model were analysed. The TEC for the years 2019, 2024 and 2030 were forecasted to be 1,162,453 MW, 1,442,410 MW and 1,778,358 MW respectively. When compared with Population, GDP per capita, it is concluded that GDP foresees TEC better. The forecasting of total electricity consumption for the year 2030–2031 for India is found to be 1834349 MW. Therefore energy planning of a country relies heavily upon precise proper demand forecasting. Precise forecasting is one of the major challenges to manage in the energy sector of any nation. Moreover forecasts are important for the effective formulation of energy laws and policies in order to conserve the natural resources, protect the ecosystem, promote the nation's economy and protect the health and safety of the society. [ABSTRACT FROM AUTHOR]