Unveiling the Future: Feasibility Model Of Solar Energy Plants By Ann And Mcdm Techniques

With the ever-increasing energy demands and the urgent need to transition towards sustainable alternatives, solar energy has emerged as a beacon of hope. Solar energy plants harness the power of the sun to generate electricity, offering an environmentally friendly and renewable source of power. As the world looks to reduce its carbon footprint and mitigate the effects of climate change, the feasibility model of solar energy plants becomes a crucial aspect to consider for investors, policymakers, and researchers.

The Role of ANN and MCDM in Solar Energy Plant Feasibility

The Feasibility Model of Solar Energy Plants involves intricate analyses and assessments to determine the practicality and profitability of setting up such plants. In recent years, Artificial Neural Networks (ANN) and Multi-Criteria Decision Making (MCDM) techniques have gained popularity for evaluating the feasibility of renewable energy projects, including solar energy plants.

Feasibility Model of Solar Energy Plants by ANN and MCDM Techniques (SpringerBriefs in Energy)

by Mrinmoy Majumder (1st ed. 2016 Edition, Kindle Edition)

5 out of 5

Language	:	English
File size	:	4335 KB
Text-to-Speech	:	Enabled
Screen Reader	:	Supported
Enhanced typesetting	:	Enabled
Word Wise	:	Enabled
Print length	:	59 pages

FREE

ANN allows us to simulate the complex behavior of renewable energy systems by modeling relationships between various parameters. It aids in forecasting solar energy generation, estimating costs, and optimizing energy production. Through machine learning algorithms, ANN can adapt and improve its accuracy over time, helping developers make informed decisions when assessing the feasibility of solar energy plants.

MCDM techniques provide a framework for analyzing multiple criteria and evaluating solar energy projects from different perspectives. By considering factors such as financial viability, environmental impact, social acceptance, and technological feasibility, MCDM methods assist in quantifying the overall feasibility of solar energy projects. These techniques help stakeholders prioritize aspects and make well-informed decisions for sustainable development.

Benefits of Using ANN and MCDM for Feasibility Assessment

The combination of ANN and MCDM techniques provides numerous advantages when evaluating the feasibility of solar energy plants:

• Accurate Predictions: ANN enables accurate forecasting of solar energy generation, taking into account various parameters such as weather conditions, location, and system efficiency.
• Cost Optimization: ANN assists in determining the optimal system size, minimizing installation and operating costs, and maximizing the return on investment.
• Mitigating Risks: MCDM techniques identify and quantify risks associated with solar energy projects, allowing developers to take precautionary measures.
• Enhanced Decision-making: MCDM provides a structured approach for evaluating multiple aspects, aiding stakeholders in making well-informed decisions aligned with sustainability goals.
• Social and Environmental Considerations: MCDM techniques help in evaluating the social and environmental impacts of solar energy plants, promoting sustainable and responsible development.

Implementation Challenges and Future Outlook

While ANN and MCDM techniques offer promising solutions for assessing the feasibility of solar energy plants, there are also challenges to overcome. One of the key challenges is obtaining accurate and reliable data for training the ANN models. Additionally, identifying appropriate criteria and assigning weights in MCDM techniques require careful consideration and expert knowledge.

Nonetheless, as technology advancements continue and data availability improves, the feasibility assessment of solar energy plants using ANN and MCDM techniques will become more robust and accurate. These methods will aid in optimizing the efficiency of solar energy production and enable stakeholders to make informed decisions based on comprehensive evaluations.

The feasibility model of solar energy plants plays a crucial role in the transition towards sustainable and renewable energy sources. By leveraging ANN and MCDM techniques, stakeholders can assess the practicality, profitability, and environmental impact of solar energy projects more accurately. These methods enable better decision-making, cost optimization, and risk mitigation, ensuring the successful implementation of solar energy plants.

As the world embraces a greener future, the feasibility model of solar energy plants combined with ANN and MCDM techniques presents a pathway for sustainable development, reduced carbon emissions, and energy independence.

Feasibility Model of Solar Energy Plants by ANN and MCDM Techniques (SpringerBriefs in Energy)

by Mrinmoy Majumder (1st ed. 2016 Edition, Kindle Edition)

5 out of 5

Language	:	English
File size	:	4335 KB
Text-to-Speech	:	Enabled
Screen Reader	:	Supported
Enhanced typesetting	:	Enabled
Word Wise	:	Enabled
Print length	:	59 pages

FREE

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.