What is it about?

This paper examines and analyses weather data in Oman to re-encode the appropriate climate dimension to generate solar energy. It also suggested prediction models that could accurately predict future weather information. The present study aims to help decision-makers take the necessary measures to address the demand for renewable energy generation and solutions to environmental problems by taking advantage of long daylight hours in Oman to increase the production of alternative and clean electricity. There is no doubt that different environmental factors such as temperature, humidity, wind intensity and rain have a significant impact on the amount of solar cells produced. However, accurate forecasting of temperature and humidity helps to select the best weather conditions that can help raise the generation of solar energy and reduce the cost of production, leading to an increase in the economic income of countries. This paper presents various mathematical prediction models based on a multi-boundary score (2, 3, 4), which has the value of the R2 determination factor equal to (0.9335, 0.9603, 0.9977), respectively. The column test results (Prob> F) proved that the null hypothesis was accepted and rejected the alternative hypothesis. Thus, all the results are less than the significant value (0.5), and each variable has an average value or less than the mean value of the test (26). Therefore, there are no significant differences or unusual cases in historical temperature data in Oman from 1991 to 2015. Also, the prediction values corresponding to the actual temperature in the future, which helps to predict and analyze the temperature data at any time.

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Why is it important?

Highlights • Weather data were tested and analyzed in Oman. •The climate dimension for solar power generation has been re-encoded. •Different mathematical prediction models have been developed using multidisciplinary score. •The results proved that the null hypothesis is acceptable while alternative hypothesis rejected.

Perspectives

The most significant contributions in this paper include the following: 1. Propose linear and nonlinear models for forecasting the weather in Oman and analysing the data to obtain the best climatic conditions for any region in Oman for generating solar energy efficiently. 2. The proposed models help decision-makers to calculate the average time of renewable energy production and compare it with the consumption rate required over a given period to take the necessary measures to estimate the cost of manufacturing and the size of the solar cells needed and calculate the economic benefit from them. 3. The proposed models help to accurately predict the temperature and humidity in increasing the production of solar energy and reduce the cost of production, which leads to an increase in the economic income of countries. Also, produce alternative clean energy that protects the environment in Oman of any residues harmful if using traditional methods like oil and gas. 4. The study helps to propose models to evaluate the actual temperatures in the future for any period, which helps to preserve the safety of citizens from the risk of rising temperatures. Models can also be modified to predict temperatures in any region of the world similar to the Oman climate. Different mathematical prediction models based on the degree of Polynomial (2, 3, and 4), which achieved a value of the coefficient of determination R2 equal to (0.9335, 0.9603, and 0.9977) respectively, are presented. The results of testing the (Prob>F) column proven that the null hypothesis is accepted and rejected the Alternative Hypothesis. The mathematical and statistical methods showed that all results are less than the significant value (0.5). Therefore, significantly all variables have a mean value less or equal the test mean value (26).

Assoc. Prof Dr. Jabar H. Yousif
Sohar University

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This page is a summary of: Analysis and forecasting of weather conditions in Oman for renewable energy applications, Case Studies in Thermal Engineering, March 2019, Elsevier,
DOI: 10.1016/j.csite.2018.11.006.
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