We propose three algorithms to the problem of solar energy prediction and Percentile Root Estimation (PRE) of three- parameters distributions. The first algorithm named Algorithm of Change Rate Matrix (ACRM), Our approach is based on creating a matrix of solar energy change rates for each month separately during successive years. ACRM is characterized by not relying on the transition matrix or Markov model. The second algorithm named Algorithm of Converting Dataset to Markov model (ACDM) depends on the transition states of the solar energy and Markov model for a month during successive years. The results were compared with the actual values to validate the algorithms ACRM and ACDM. We demonstrate the ability of the mentioned algorithms to perform on the other dataset in various applications. The third algorithm PRE applied on the distributions Lognormal, Fatigue lifetime, Erlang, Fre ́chet and Pert which it was validated using Goodness-fit-tests, Anderson-Darling test. We analyzed the influence of PRE algorithm, as a result it is more accurate and easier in coding than the maximum likelihood estimation method.
Digital Object Identifier (DOI)
El Genidy, M.; Megahed, W.; and Mahfouz, K.
"Algorithms of Solar Energy Prediction Combined with Percentile Root Estimation of Three-Parameters Distributions,"
Applied Mathematics & Information Sciences: Vol. 16:
4, Article 6.
Available at: https://digitalcommons.aaru.edu.jo/amis/vol16/iss4/6