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Renewable energy sources are needed globally to support the available non-renewable energy sources our day-to-day living. There is high demand for renewable energy sources in both the developed and developing economies. Solar power is a good example of renewable energy source and people are currently embracing it globally for both domestic and industrial uses. Generally, these energy sources are meant to support the hydro, thermal and other energy sources that are available in different countries of the world. With the popularity of solar energy for both domestic and industrial usage, it can be argued that the estimation of the production level of such energy source is necessary so as to achieve proper planning and management. Due to the fact that the availability of the solar energy power depends largely on a number of environmental and weather conditions, predicting its production or generation can be very important. This study surveyed different works in the area of using machine learning techniques for solar power production prediction. The articles sourced were from notable research repositories. This study focuses on articles that were published between 2013 and 2023 on the subject matter. Different types of machine learning (ML) algorithms that have been used to build models from solar energy datasets are reported in this study. It is believed that the work can provide better insights for the researchers working in the problem area. Thus, the insights in this study can lead to building of improved machine learning-based models for solar power forecasting

Can the building sector become productive and, in parallel, help create livable spaces? Agricultural and solar energy systems can contribute to the building sector’s transformation; however, research on these systems has mostly focused on technological development and achieved gains, while overlooking a key driver of success, which is social acceptance. Only recently has the discussion on social acceptance of the systems gained momentum revealing that their adoption, especially in residential sectors, is bound to end users. Therefore, using a quantitative, survey-based, case study approach, we investigated what influences the social acceptance of end users, i.e., the residents of residential buildings in Egyptian new cities. Based on UTAUT – a Technology Acceptance theory – seven underlying factors were tackled using a statistical contingency analysis (SPSS, n = 274) to test their association with (a) social acceptance of agricultural and solar energy systems and (b) the sociocultural background of the residents. Results revealed that social acceptance of the systems was associated with factors like the expected effort for implementation, concerns and anxieties about the systems, external supporting conditions, and social influences – while surprisingly, it was not associated with the expected performance of the systems, their perceived costs, and the need for financial support. Most studied factors showed associations with the sociocultural aspects, except for the expected effort and perceived cost of solar systems and financial facilitations of both systems, which proved to be completely independent of the sociocultural background of the residents. The conducted analysis and concluded insights about the underlying factors behind social acceptance have not been previously covered in detail for the two systems in comparison, especially for the case of new Egyptian residences. The study findings can support relevant stakeholders such as policymakers, suppliers, engineers, etc. in triggering the social acceptance of the systems in Egypt and contexts of similar settings.

The main objective of this study is to demonstrate the integration of weather forecasts which can lead to a significant reduction in energy costs and carbon emissions while ensuring the reliability of the microgrid operation. By serving a small area or a particular building, the incorporation of weather forecasts can considerably increase the efficiency of microgrid energy management. The planning and operation of microgrids can be greatly improved by using weather predictions, which give useful information about upcoming weather conditions. By forecasting future energy demand and supply based on meteorological conditions, Microgrid Energy Management (MEM) is utilized to optimize the energy management decisions in microgrid systems. Making better choices regarding energy generation, storage, and consumption may be aided by the incorporation of weather forecasts, which can offer a more precise and trustworthy estimate of the energy demand and supply. This strategy can result in increased energy efficiency, decreased energy prices, and decreased carbon emissions, all of which are important goals for contemporary power systems. A promising approach for raising energy effectiveness and lowering greenhouse gas emissions in contemporary power networks is MEM. The incorporation of weather forecasts into MEM can improve decision-making regarding energy management by giving a better insight of future energy demand and supply. This essay examines the advantages and disadvantages of using weather forecasts in MEM through the presentation of a case example. By providing valuable information about future weather conditions, weather forecasts this review explain the Optimized Renewable Energy Integration, Improved Energy Storage Utilization, Load Shifting and Demand Response, Efficient Grid Management for reducing reliance on fossil fuels and lowering energy cost and carbon emissions. In order to address the issues related with MEM employing weather forecasts, this study offers potential fixes for increasing the accuracy of weather forecasts and emphasizes the necessity for more research in this area. [ABSTRACT FROM AUTHOR]

To investigate the impact of solar energy on the carbon footprint, to find effective measures to reduce the carbon footprint and slow global warming as soon as possible, this paper takes 30 provinces in China as an example. First, the inter‐regional input–output model is used to calculate the carbon footprint of each province. Then, the panel quantile regression model is used to investigate the impact of solar energy generation on different quantiles of carbon footprint. The results show that from 2012 to 2020, China's carbon footprint is at a high level, which is not conducive to the achievement of the goal of peaking carbon dioxide emissions. The eastern region has the highest carbon footprint, followed by the western region, the central region has the lowest carbon footprint, and its carbon footprint proportion continues to decline. The increase of solar energy generation can significantly reduce the carbon footprint of high quantile location, but has no significant impact on the carbon footprint of the middle and low quantile locations. Except for the negative impact of afforestation area on carbon footprint, the others control variables are all positive. Therefore, to reduce the carbon footprint of China, it is necessary to upgrade solar technology, increase solar energy generation, reduce energy consumption per unit of GDP (Gross Domestic Product) and the share of coal consumption, strengthen forestry management and reduce reliance on private vehicles. [ABSTRACT FROM AUTHOR]

As solar energy generation has become more and more important for the economies of numerous countries in the last couple of decades, it is highly important to build accurate models for forecasting the amount of green energy that will be produced. Numerous recurrent deep learning approaches, mainly based on long short-term memory (LSTM), are proposed for dealing with such problems, but the most accurate models may differ from one test case to another with respect to architecture and hyperparameters. In the current study, the use of an LSTM and a bidirectional LSTM (BiLSTM) is proposed for dealing with a data collection that, besides the time series values denoting the solar energy generation, also comprises corresponding information about the weather. The proposed research additionally endows the models with hyperparameter tuning by means of an enhanced version of a recently proposed metaheuristic, the reptile search algorithm (RSA). The output of the proposed tuned recurrent neural network models is compared to the ones of several other state-of-the-art metaheuristic optimization approaches that are applied for the same task, using the same experimental setup, and the obtained results indicate the proposed approach as the better alternative. Moreover, the best recurrent model achieved the best results with R 2 of 0.604, and a normalized MSE value of 0.014, which yields an improvement of around 13% over traditional machine learning models. [ABSTRACT FROM AUTHOR]

Prediction of Total Cloud Cover (TCDC) from numerical weather simulation models, such as Global Forecast System (GFS), can aid renewable energy engineers in monitoring and forecasting solar photovoltaic power generation. A major challenge is the systematic bias in TCDC simulations induced by the errors in the numerical model parameterization stages. Correction of GFS-derived cloud forecasts at multiple time steps can improve energy forecasts in electricity grids to bring better grid stability or certainty in the supply of solar energy. We propose a new kernel ridge regression (KRR) model to reduce bias in TCDC simulations for medium-term prediction at the inter-daily, e.g., 2–8 day-ahead predicted TCDC values. The proposed KRR model is evaluated against multivariate recursive nesting bias correction (MRNBC), a conventional approach and eight machine learning (ML) methods. In terms of the mean absolute error (MAE), the proposed KRR model outperforms MRNBC and ML models at 2–8 day ahead forecasts, with MAE ≈ 20–27%. A notable reduction in the simulated cloud cover mean bias error of 20–50% is achieved against the MRNBC and reference accuracy values generated using proxy-observed and non-corrected GFS-predicted TCDC in the model's testing phase. The study ascertains that the proposed KRR model can be explored further to operationalize its capabilities, reduce uncertainties in weather simulation models, and its possible consideration for practical use in improving solar monitoring and forecasting systems that utilize cloud cover simulations from numerical weather predictions. [Display omitted] • KRR model for bias correction of Total Cloud Cover forecasts is proposed. • KRR reduces bias in two-to-eight-day ahead forecasts at 0, 3, 6 UTC. • Cloud cover forecast error is reduced between 20-50% against conventional error reduction methods. • KRR is validated with multivariate recursive nested bias correction and other models. • KRR reduces bias in Total Cloud Cover forecasts for solar energy forecast systems. [ABSTRACT FROM AUTHOR]

The building sector is one of the most resource-intensive industries. In Egypt, buildings consume 60% of electricity, produce 8% of CO2 emissions, and anthropize agricultural land, peri-urban and urban landscapes. To compensate for these consumption patterns, building envelopes can become productive in terms of greening and energy production. This encompasses the implementation of productive building systems that include (a) greening systems such as building-integrated vegetation and agriculture systems and (b) solar energy systems such as building-applied and integrated photovoltaics. For Egypt, the transformation toward more productive buildings still lacks a holistic understanding of their status and implementation requirements. This paper undergoes a comprehensive analysis of the two systems’ classifications, benefits, challenges, and implementation aspects based on a thorough assessment of 121 studies and 20 reports addressing them. This is coupled with a contextual analysis using questionnaires (n = 35) and semi-structured interviews (n = 13) with Egyptian experts and suppliers. Results showed that a large variety of systems is studied in literature and exists in the local market. Among the most purchased productive building systems in the Egyptian market, according to experts, are hydroponics (selected by 75% of respondents), planter boxes/pots (50%), roof-mounted photovoltaic panels (95%), and solar water heaters (55%). The main benefits of greening and solar energy systems are identified as enjoying the greenery view (95%) and reducing energy expenses (100%), respectively. The high initial cost was considered the main barrier for both systems. Multiple commonalities between the two systems in terms of spatial and environmental applicability aspects (e.g., accessibility and safety, net useable area, sun exposure, wind exposure) and environmental performance aspects (e.g., energy demand and emissions reduction, heat flow reduction) were identified. Lastly, we highlight the importance of analyzing integrated solutions that make use of the identified synergies between the systems and maximize the production potentials.

In this paper, to balance power supplement from the solar energy's intermittent and unpredictable generation, we design a solar energy generation and trading platform (EggBlock) using Internet of Things (IoT) systems and blockchain technique. Without a centralized broker, the proposed EggBlock platform can promote energy trading between users equipped with solar panels, and balance demand and generation. By applying the second price sealed-bid auction, which is one of the suitable pricing mechanisms in the blockchain technique, it is possible to derive truthful bidding of market participants according to their utility function and induce the proceed transaction. Furthermore, for efficient generation of solar energy, EggBlock proposes a Q-learning-based dynamic panel control mechanism. Specifically, we set the instantaneous direction of the solar panel and the amount of power generation as the state and reward, respectively. The angle of the panel to be moved becomes an action at the next time step. Then, we continuously update the Q-table using transfer learning, which can cope with recent changes in the surrounding environment or weather. We implement the proposed EggBlock platform using Ethereum's smart contract for reliable transactions. At the end of the paper, measurement-based experiments show that the proposed EggBlock achieves reliable and transparent energy trading on the blockchain and converges to the optimal direction with short iterations. Finally, the results of the study show that an average energy generation gain of 35% is obtained. [ABSTRACT FROM AUTHOR]

North African countries generally have strategic demands for energy transformation and sustainable development. Renewable energy development is important to achieve this goal. Considering three typical types of renewable energies— wind, photovoltaic (PV), and concentrating solar power (CSP)—an optimal planning model is established to minimize construction costs and power curtailment losses. The levelized cost of electricity is used as an index for assessing economic feasibility. In this study, wind and PV, wind/PV/CSP, and transnational interconnection modes are designed for Morocco, Egypt, and Tunisia. The installed capacities of renewable energy power generation are planned through the time sequence production simulation method for each country. The results show that renewable energy combined with power generation, including the CSP mode, can improve reliability of the power supply and reduce the power curtailment rate. The transnational interconnection mode can help realize mutual benefits of renewable energy power, while the apportionment of electricity prices and trading mechanisms are very important and are related to economic feasibility; thus, this mode is important for the future development of renewable energy in North Africa.

Can the building sector become productive and, in parallel, help create livable spaces? Agricultural and solar energy systems can contribute to the building sector's transformation; however, research on these systems has mostly focused on technological development and achieved gains, while overlooking a key driver of success, which is social acceptance. Only recently has the discussion on social acceptance of the systems gained momentum revealing that their adoption, especially in residential sectors, is bound to end users. Therefore, using a quantitative, survey-based, case study approach, we investigated what influences the social acceptance of end users, i.e., the residents of residential buildings in Egyptian new cities. Based on UTAUT – a Technology Acceptance theory – seven underlying factors were tackled using a statistical contingency analysis (SPSS, n = 274) to test their association with (a) social acceptance of agricultural and solar energy systems and (b) the sociocultural background of the residents. Results revealed that social acceptance of the systems was associated with factors like the expected effort for implementation, concerns and anxieties about the systems, external supporting conditions, and social influences – while surprisingly, it was not associated with the expected performance of the systems, their perceived costs, and the need for financial support. Most studied factors showed associations with the sociocultural aspects, except for the expected effort and perceived cost of solar systems and financial facilitations of both systems, which proved to be completely independent of the sociocultural background of the residents. The conducted analysis and concluded insights about the underlying factors behind social acceptance have not been previously covered in detail for the two systems in comparison, especially for the case of new Egyptian residences. The study findings can support relevant stakeholders such as policymakers, suppliers, engineers, etc. in triggering the social acceptance of the systems in Egypt and contexts of similar settings. [ABSTRACT FROM AUTHOR]

Social acceptance of end users is indispensable for the implementation of agricultural and solar energy systems to create a more sustainable and productive residential building sector. Thus, the main aim of this study is to investigate the social acceptance level of the two systems and the implementation preferences of Egyptian end users, i.e., residents, in relation to their different sociocultural backgrounds. Given that most of the construction in Egypt is taking place in new cities, the acceptance of such systems strongly relates to societal implications for urban sustainability. An online survey was therefore disseminated to the residents of new cities in the Greater Cairo Region in Egypt (n = 274). A contingency analysis was conducted using the SPSS tool, calculating the Chi-squared and Fisher tests to identify significant associations between the variables. Results indicated a high level of social acceptance of both the agricultural (71 %) and solar energy (64 %) systems. The attitude of residents towards the systems and their experience using them were the variables exhibiting the highest association with social acceptance of agricultural systems (p < 0.001 for attitude and experience) and solar energy systems (p = 0.04 for attitude and p = 0.002 for experience). The most preferred system types were the horizontal planters on hand railings and roof-mounted photovoltaics. Responses showed that production was the main aim for agricultural systems, while economic returns were the main aim for solar systems. However, both systems faced the same barriers, especially in relation to economic barriers. Out of the 12 sociocultural variables tested, respondents' age had the most significant impact on the implementation preferences of both systems followed by gender, residence type, and access to shared facilities. Our study addressed a knowledge gap by comparing the two systems to identify the common or different reasons behind the disparity between their high theoretical potential versus low on-ground implementation. Future research could investigate other underlying factors behind social acceptance beyond the analyzed sociocultural aspects and tackle the types of each system in detail. • Gap between the potential and actual implementation of productive systems on building envelopes. • Agricultural and solar systems have high social acceptance in residences in Egypt. • Resident's attitude and experience are key aspects associated with acceptance. • Age, gender, residence type, and area restrictions affected the systems' acceptance. • Production and economic aims were most preferred for agricultural and solar systems respectively. [ABSTRACT FROM AUTHOR]

The quest to improve power supply in Nigeria has motivated the country to look for alternative energy resources to augment existing ones. Being in the tropics, Nigeria is well positioned to harness solar energy to improve its energy poverty status. Although, a number of solar energy projects have been initiated in the arid parts of the country, very little has been done in the humid Eastern part of the country. Hence, this study aims to evaluate the solar energy generation potential in Eastern Nigeria, using geospatial technologies. A spatially-explicit multi-criteria decision analysis (MCDA) was implemented with GIS, using a number of geospatial data products including Landsat satellite images, NASA global direct normal irradiance (DNI), ASTER Digital Elevation Model (DEM) and National Forest Reserve map covering the study area. Various derivatives, including land use and land cover (LU/LC) map, slope, and factor maps were generated in other to produce a solar energy potential map of the region. This research found that over 59,000ha (0.67% of the region) were found to be suitable for the production of solar energy.

In this study, the optimal capacity of a battery and power conditioning system (PCS) of energy storage system were calculated. In addition, economic analysis was conducted to determine the optimal equipment standard, taking the government support plan into account. In addition, the changes in the power generation pattern were examined when the energy storage system and photovoltaic (PV) were connected to verify the power peak management efficiency of the energy storage system. Moreover, the effect of the energy storage system support policy was assessed by comparing the economic efficiency of single-PV equipment and energy storage system-connected equipment by the internal rate of return. Internal rate of return was analyzed by the change in cost of energy storage system equipment and the price of system marginal price/renewable energy certificate, which was a sales factor, and used for economic forecasting of the energy storage system. To accomplish this, the 2015 power generation output data (daily average 3.69 h power generation) of LG Hausys Ulsan station were converted to small-scale (3 MW) and large-scale (10 MW) solar power and a model that calculated the factor capacity of battery and the PCS capacity of the energy storage system was then constructed. Furthermore, the selected battery capacity and PCS capacity were analyzed separately by economic analysis to propose an energy storage system equipment standard, which could guarantee the optimal economic efficiency. Finally, based on the "Guideline for Management and Operation of Mandatory Supply for New and Renewable Energy" established by the Ministry of Commerce Industry and Energy, the profit model applied to the economic analysis was limited to an energy storage system charged from 10:00 to 16:00. [ABSTRACT FROM AUTHOR]

Microgrids (MGs) utilising both renewables and energy storage to optimise onsite energy consumption rather than importing power form the utility grid, require a tertiary-level energy management system (EMS). The EMS must monitor and control the energy exchange within the nodes of MG to maximise any solar energy generation and benefit from installed storage. This paper considers a single-family house as the MG that has DC distribution circuit model. The look-ahead EMS is formulated as a linear programming problem, which has been tested in both offline simulation and hardware-in-the-loop (HIL) simulation environment. The simulation results indicate that the proposed look-ahead EMS can effectively reduce the DC MG operation cost without any operational constraint violation. In addition, the proposed look ahead energy optimisation approach has the potential to be used in a large-scale system such as a community MG with multiple buildings.

Microgrids (MGs) utilising both renewables and energy storage to optimise onsite energy consumption rather than importing power form the utility grid, require a tertiary-level energy management system (EMS). The EMS must monitor and control the energy exchange within the nodes of MG to maximise any solar energy generation and benefit from installed storage. This paper considers a single-family house as the MG that has DC distribution circuit model. The look-ahead EMS is formulated as a linear programming problem, which has been tested in both offline simulation and hardware-in-the-loop (HIL) simulation environment. The simulation results indicate that the proposed look-ahead EMS can effectively reduce the DC MG operation cost without any operational constraint violation. In addition, the proposed look ahead energy optimisation approach has the potential to be used in a large-scale system such as a community MG with multiple buildings. [ABSTRACT FROM AUTHOR]

An electrical power system for low-Earth-orbit satellites, which can be reused for a variety of mission requirements with minimal redesign, is proposed. The goals of the proposed power system are achieved by modularizing the subsystems of a small spacecraft and designing them to allow any number of each module to be connected simultaneously. The modularized subsystems include solar energy generation and energy storage. The overall objectives will be accomplished without compromising efficiency or power system stability. The purpose of this paper is to describe the proposed power system and detail the experimental results. The generalized system can be used as a one-size-fits-all solution that only requires basic power systems knowledge to configure and operate. [ABSTRACT FROM AUTHOR]

To overcome the inherent demerit of low power factor existing in transverse‐flux permanent‐magnet (TFPM) machines, a tubular staggered‐teeth TFPM linear machine is presented here. Linear alternator integrated with free‐piston Stirling engines could offer a great potential in a wide variety of applications ranging from solar energy generation to space power supply. The thermal behaviour of the machine is studied using a three‐terminal lumped‐parameter thermal network (LPTN) to solve the problem of temperature overestimation of the traditional LPTN. The determination of thermal resistances and thermal parameters are introduced in detail. The temperatures of various components of the machine under different load conditions are calculated by both the three‐terminal LPTN model and the numerical thermal model. Sensitivity analysis is carried out to study the influence of critical thermal parameters on the temperature rise of the machine. On this basis, the effectiveness of the forced air cooling is investigated. A prototype is fabricated and temperature experiment indicates that there is a good agreement between measurement and calculation. [ABSTRACT FROM AUTHOR]

Solar panel photovoltaic (PV) systems are widely used in Korea to generate solar energy, which is one of the most promising renewable energy sources. With regard to solar electricity providers and a grid operator, it is critical to accurately predict solar power generation for supply–demand planning in an electrical grid, which directly affects their profit. This prediction is, however, a challenging task because solar power generation is weather dependent and uncontrollable. In this study, a daily prediction model based on the weather forecast information for solar power generation is proposed. In the case of the proposed model, the cloud and temperature data available from the weather forecast information is used to predict the amount of solar radiation as well as a loss adjustment factor to reflect the possible loss of power generation due to the degradation or failure of the PV module. Using the proposed model, solar power generation for the following day can be predicted. The proposed model is embedded into a solar PV monitoring system that is commercially used in Korea, and it is shown to perform better than the existing prediction models. [ABSTRACT FROM AUTHOR]

Solar energy is an important and reliable source of energy. Better understanding the concepts and relationships of the factors that affect solar energy generation capacity can enhance the usage of solar energy. This understanding can lead investors and governors in their solar power investments. However, solar power generation process is complicated, and the relations among the factors are vague and hesitant. In this paper, a hesitant fuzzy cognitive map for solar energy generation is developed and used for modeling and analyzing the ambiguous relations. The concepts and the relationships among them are defined by using experts’ opinions. Different scenarios are formed and evaluated with the proposed model.

Solar photovoltaic (PV) deployments are growing rapidly to provide a sustainable source of electricity, but their output is strongly impacted by environmental phenomena such as soiling and low irradiance conditions induced by haze from urban sources, dust, and bushfire smoke. This review examines the effects of haze on PV performance, highlights significant results, and identifies apparent research gaps in the current literature. In addition to the severe health issues caused by industrial exhausted aerosol, dust storms particles, and bushfire smoke, reduction in irradiance (in some cases up to 80%) is the most dominant impact of these sources of haze. Haze also causes changes in the received solar spectrum, and higher bandgap PV materials are more affected by the presence of haze and aerosols in the atmosphere by 20-40% than low bandgap semiconductors. In many cities throughout the world, pollution-related haze causes substantial annual revenue loss to PV operators. In addition, haze imposes severe effects on direct irradiance; therefore, tracking systems and concentrated PV systems are most affected. These technical impacts of haze all indicate the need for careful customization of PV systems for specific locations. In addition, to increase global PV output, it is clear that air pollution control regulations such as China's national policies against air pollution and eco-friendly international actions such as COP26 should be employed and executed. Further studies are needed including indoor experiments, forecasting future implications of aerosols on PV energy conversion, and performing energy policy analysis to identify associated challenges and propose practical strategies. • Solar photovoltaic (PV) strongly impacted by environmental phenomena induced by haze. • Industrial exhausted aerosol, dust storms particles, bushfire smoke cut irradiance. • Haze changes in received solar spectrum, and higher bandgap PV 20-40% cut. • Pollution-related haze causes substantial annual revenue loss to PV operators. • Haze imposes most severe effects on direct irradiance; tracking + concentrated PV systems. [ABSTRACT FROM AUTHOR]

To achieve the 2025 Non-Nuclear Homeland goal and carbon emission mitigation target, the Taiwan government has been developing actively green and renewable energy with low carbon emissions. This study assessed the FSPS development project in the Cigu region of Tainan City to provide a thorough analysis toward making tradeoffs among ecosystem conservation, aquaculture, fisheries, and green power generation development. This study employs the choice experiment method and designs different attributes and levels to discuss the preferences of stakeholders in the policy development choices for ecosystem conservation and FSPS. The hope is that it can balance economic development and ecological conservation. The findings demonstrate that the tourists' marginal willingness to pay is low. The respondents then give importance to improving biodiversity. Finally, they prefer minimal changes to the status quo with the FSPS policy implementation. These findings can serve as a reference for decision making for regional sustainable development, aquaculture and fishery upgradation, and green power generation and exploitation. [ABSTRACT FROM AUTHOR]

• Geographical distribution of solar energy capacity in India. • The Jawaharlal Nehru National Solar Mission is the initiative by the government of India for installing 100 GW of solar energy by 2022. • Solar Electricity for Industrial Applications in India. • Future Plan of solar power projects in India for Next Decade. Energy has a viable role-play in the socio-economicequilibrium of a country and improving overall quality of existence. Diminishing fossil fuel resources and high risks of environmental contamination associated with them has made it necessary to look for alternative and renewable resources. Due to development and necessity of new alternative renewable energy sources, bio-fuels, solar or wind energy, etc., are gaining constant popularity. Both the state and central government of India and have supported research over solar photovoltaic power during last decade. The present study discuss renewable energy and to be specific solar photovoltaic energy inclination in India along with the comparative studies of current and future developments. This paper also reviews policies and promotional steps taken by the Indian government. Finally, this review paper will assist to realize the present status, challenges and barrier for better planning and administration in setting up and utilizing alternative energy resources. [ABSTRACT FROM AUTHOR]