Applied computing Journal

Volume 1 , Issue 3 , July 2021

Implementation of Big Data Analytics for Simulating, Predicting and Optimizing the Solar Energy Production

  • jabar yousif
jabar yousif
ACAA Publisher

Abstract

The notable developments in renewable energy facilities and resources help reduce the cost of production and increase production capacity. Therefore, developers in renewable energy evaluate the overall performance of the various equipment, methods, and structure and then determine the optimal variables for the design of energy production systems. Variables include equipment characteristics and quality, geographical location, and climatic variables such as solar irradiance, temperature, humidity, dust, etc. This paper investigated and reviewed the current big data methods and tools in solar energy production. It discusses the comprehensive two-stage design and evaluation for examining the optimal structure for renewable energy systems. In the design stage, technical and economic aspects are discussed based on a robust analysis of all input/output variables for determining the highest performance. Next, assess and evaluate the effectiveness of each method under different circumstances conditions. Then convert each qualitative indicator into a quantitative measure using extensive data analysis methods to determine the overall performance of the various qualitative variables. The paper also provides an in-depth analysis of the mathematical techniques used in measuring the efficiency of the renewable energy production system and discussing future axes of work in the field of specific energy.

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References

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DOI: https://doi.org/10.52098/acj.202140
Published August 8, 2021
Keywords
  • Big Data,
  • Machine Learning,
  • Solar Energy,
  • ANN,
  • Model Optimization
How to Cite
yousif, jabar. (2021). Implementation of Big Data Analytics for Simulating, Predicting and Optimizing the Solar Energy Production. Applied Computing Journal, 3(Issue 3), 133-140. https://doi.org/10.52098/acj.202140

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