Access to Solar Energy for Livelihood Security in Odisha, India

Giri, Nimay Chandra; Das, Sima; Pant, Divya; Bhadoria, Vikas Singh; Mishra, Debasis; Mahalik, Gyanranjan; Mrabet, Rachid

doi:10.1007/978-981-99-0969-8_23

Cited by 15 publications

(4 citation statements)

References 7 publications

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“…The lattice consists of nodes or neurons, each representing a weight vector of the same dimension as the input data. During the training process, the SOM adjusts the weights of the neurons to represent the input data in a topological manner [32]. Self-organizing maps (SOMs) are a type of artificial neural network that can be used for unsupervised learning and data visualization.…”

Section: Self-organizing Maps (Som)mentioning

confidence: 99%

“…In order to determine the most suitable power plant, a dataset based on various issues related to power generation is prepared. To make informed decisions, a multiple-criteria decision analysis (MCDA) is proposed as a methodology [32]. The factors considered in the MCDA include low gas emissions, installation cost, low maintenance cost, high power generation, and high resource availability.…”

Section: Sustainable Energy Generation and Societal Benefitsmentioning

confidence: 99%

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Performance Assessment of Different Sustainable Energy Systems Using Multiple-Criteria Decision-Making Model and Self-Organizing Maps

Dash,

Chakravarty,

Giri

et al. 2024

Technologies

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The surging demand for electricity, propelled by the widespread adoption of intelligent grids and heightened consumer interaction with electricity demand and pricing, underscores the imperative for precise prognostication of optimal power plant utilization. To confront this challenge, a dataset centered on issue-centric power plans is meticulously crafted. This dataset encapsulates pivotal facets indispensable for attaining sustainable power generation, including meager gas emissions, installation cost, low maintenance cost, elevated power generation, and copious resource availability. The selection of an optimal power plant entails a multifaceted decision-making process, demanding a systematic approach. Our research advocates the amalgamation of multiple-criteria decision-making (MCDM) models with self-organizing maps to gauge the efficacy of diverse sustainable energy systems. The examination discerns solar energy as the preeminent MCDM criterion, securing the apex position with a score of 83.4%, attributable to its ample resource availability, considerable energy generation, nil greenhouse gas emissions, and commendable efficiency. Wind and hydroelectric power closely trail, registering scores of 75.3% and 74.5%, respectively, along with other energy sources. The analysis underscores the supremacy of the renewable energy sources, particularly solar and wind, in fulfilling sustainability objectives and scrutinizing factors such as cost, resource availability, and the environmental impact. The proposed methodology empowers stakeholders to make judicious decisions, accentuating facets that are required for more sustainable and resilient power infrastructure.

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Section: Self-organizing Maps (Som)mentioning

confidence: 99%

Section: Sustainable Energy Generation and Societal Benefitsmentioning

confidence: 99%

Performance Assessment of Different Sustainable Energy Systems Using Multiple-Criteria Decision-Making Model and Self-Organizing Maps

Dash,

Chakravarty,

Giri

et al. 2024

Technologies

Self Cite

View full text Add to dashboard Cite

show abstract

“…One of the features that the lighting control system must have is data processing, as the controller is more interested in summary statistics than in the raw readings from each sensor's terminal. An adaptive weighted fusion algorithm is used in the system's processing link for data-level fusion, allowing for the fusion of data from several sensors [18]. Te system will next employ the polynomial discrete gray model G (1,1,K) for lighting prediction control in the decision-level fusion phase.…”

Section: Data Processing Module Designmentioning

confidence: 99%

Design of the Urban Lighting Control System Based on Optical Multisensor Technology and the GM Model

Wu,

Tang

2023

Journal of Electrical and Computer Engineering

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Lighting has emerged as a central concern in the domain of city planning and design in recent decades. Better lighting does more than just make cities safer and more secure; it also makes them more aesthetically pleasing and easier to live in. A single type of optical sensor is no longer sufficient to meet the needs of intelligent lighting for urban roads, and as such, there is a growing demand for cutting-edge control systems that can adapt to the dynamic lighting needs in urban environments. This paper’s goal is to create an intelligent urban lighting control system by integrating optical multisensor technology and the gray model (GM model). Programmable logic controller (PLC) serves as the system’s central processing unit, with light intensity sensors and color sensor-detecting devices placed strategically throughout each city and linked directly to the controller. Each road streetlight is equipped with a motion sensor detection device that is tasked with identifying the presence of vehicles and pedestrians within its field of view. Data fusion technology is utilized to process the environmental data gathered by optical multisensors, the collected data are then used to control and predict outcomes using the robust prediction capability of the GM model, and the result is a lighting control strategy that is both efficient and intelligent. In the end, the strategy presented in this paper is applied to improving the management of an industrial park lighting system’s energy consumption. The results of the evaluations show that the fresh method is successful in dimming, prediction, and control. This conclusively demonstrates the efficacy of the paper’s proposed design solution, which integrates optical multisensor technology with sophisticated control algorithms and data analysis to improve the quality of life in urban areas by boosting the efficiency and sustainability of the urban lighting system.

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“…Soiling of solar power systems, such as CSP mirrors and PV modules, signifi cantly affects energy yield and increases the Levelized Cost of Electricity (LCOE), which poses signifi cant socio-economic, technological, and environmental threats [1][2][3][4][5]. Although the consequences of soiling are higher for concentrating systems than for non-concentrating systems, the gradual reduction of nominal power output is widely recognized as a defect mainly of PV systems.…”

Section: Introductionmentioning

confidence: 99%

Contemporary considerations of the utility of self-cleaning coatings for solar power installations-The SolarSkin system

Nikolaos D,

Paraskevi,

et al. 2023

Appl Chem Mater Sci

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Soiling of conventional Photovoltaic (PV) modules and Concentrated Solar Power (CSP) mirrors significantly affects the efficiency of the system. Although there are several soiling mitigation strategies, none of them has been widely adopted, since effectiveness in real field conditions is often disputed. Anti-soiling coatings are perhaps the most promising solution for solar power projects since they combine low cost and effectiveness. These are broadly categorized into hydrophilic and hydrophobic. Hydrophilic coatings perform better in arid climates, whereas hydrophobic coatings have a wider spectrum of functionality. Most of them however suffer from various drawbacks, such as poor durability and transmittance which restrict the extent of benefits. In addition, there are only a few reliable technologies that can be retrofitted to existing installations. Predicting the efficiency of a given anti-soiling coating in a specific plant is difficult, therefore, the objective of this work was to identify those parameters that are critical to their performance in real outdoor conditions. Focus was given to the drawbacks of current hydrophobic coating systems and the requirements that a modern anti-soiling coating should satisfy in order to be implemented as an effective after-market solution to degraded solar systems. A prominent example of cutting-edge, high-performing self-cleaning technology, i.e. the SolarSkin system developed by BFP Advanced Technologies, is additionally presented. The latter can renovate deteriorated solar collectors, increase the energy yield of the system, and protect it from weathering effects over its expected operational lifecycle. Potential increment of the system’s efficacy as well as maintenance savings are also briefly discussed.

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Access to Solar Energy for Livelihood Security in Odisha, India

Cited by 15 publications

References 7 publications

Performance Assessment of Different Sustainable Energy Systems Using Multiple-Criteria Decision-Making Model and Self-Organizing Maps

Performance Assessment of Different Sustainable Energy Systems Using Multiple-Criteria Decision-Making Model and Self-Organizing Maps

Design of the Urban Lighting Control System Based on Optical Multisensor Technology and the GM Model

Contemporary considerations of the utility of self-cleaning coatings for solar power installations-The SolarSkin system

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