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Über dieses Buch

This Brief highlights a novel model to find out the feasibility of any location to produce solar energy. The model utilizes the latest multi-criteria decision making techniques and artificial neural networks to predict the suitability of a location to maximize allocation of available energy for producing optimal amount of electricity which will satisfy the demand from the market. According to the results of the case studies further applications are encouraged.

Inhaltsverzeichnis

Frontmatter

Chapter 1. Introduction

Abstract
The scarcity in the supply of energy and rapid urbanization all over the world has enforced to look for alternative sources of energy. Solar energy is one of the most popular and reliable source of energy which is infinitely available and can become a possible substitute to fossil fuels. As most of the factors on which the usability of solar energy depends varies with location, selection of suitable sites for solar power plant is the key for optimal utilization of the potential. Present study uses AHP and GMDH to identify feasible sites for installation of solar power plants.
Mrinmoy Majumder, Apu K. Saha

Chapter 2. Solar Energy

Abstract
Solar energy mainly depends on insolation intensity and duration of sunlight hours. Both of this varies with location. That is why if suitable location can be identified, optimal utilization of the resources is possible. Solar energy is a popular alternative and renewable form of energy source which can easily be converted to utilizable forms of energy. Although conversion efficiency and cost is not conducive enough to designate this form of energy to substitute the conventional energy sources. But depending on location cost can be reduced and amount of energy can be increased.
Mrinmoy Majumder, Apu K. Saha

Chapter 3. Multi Criteria Decision Making

Abstract
Multi Criteria Decision Making (MCDM) is one of the technique which is used to select most optimal alternative with respect to multiple criteria for a specific goal. The method provides objectivity and compares the alternative relatively to estimate the priority value of the alternatives. Based on the priority value the optimal alternative is identified and selected as the option which can achieves the decision objective.
Mrinmoy Majumder, Apu K. Saha

Chapter 4. Artificial Neural Network

Abstract
Artificial Neural Network (ANN) is a technique which can map a relationship within a non-linearly related variables. The development of the model involves selection of network topology,estimation of network weights and validation of the model output by comparing with the desired. Group Method of Data Handling (GMDH) is a new variant of ANN which uses multiple algorithms to find the optimal value of the network weights. The present investigation uses GMDH as a predictive model to estimate the indicator value with the help of input parameters.
Mrinmoy Majumder, Apu K. Saha

Chapter 5. Methodology

Abstract
The present investigation uses Analytical Hierarchy Process and Analytical Network Process to estimate the priority value of the parameters. The ratio of priority value with the value of the beneficiary and non-beneficiary parameters constituted the indicator value which is made directly proportional to the suitability of site to be used for solar power production. The Group Method of Data Handling was used to map the input parameters with the indicator value. Sensitivity analysis was performed and the indicator was used to find the suitability of sites in twelve different locations.
Mrinmoy Majumder, Apu K. Saha

Chapter 6. Results and Discussions

Abstract
The results from the MCDM delineated the importance of insolation parameter in site selection methodologies. The GMDH model trained with GMDH algorithm, output data transformed by Arc tangenet function and priority value retrieved from Analytical Network Process method was found to have the highest performance among the twenty four models developed for the study. The city of New York was found to have the most and Paris have the least suitability for installation of solar power plants among the twelve cities considered for the study.
Mrinmoy Majumder, Apu K. Saha

Chapter 7. Conclusion

Abstract
In the conclusion of the study it was found that the methodology depicted in the present investigation for site selection for solar power plants is both objective, cognitive and relative in nature. The indicator is a stand alone model which can be mebedded anywhere to predct site suitability real time. But dependence on type of method used is a major drawback which need to be addressed in the later research works.
Mrinmoy Majumder, Apu K. Saha
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