Analysis and forecasting of weather conditions in Oman for renewable energy applications. (March 2019)

• Record Type: Journal Article
• Authors: Yousif, Jabar H.; Al-Balushi, Haitham A.; Kazem, Hussein A.; Chaichan, Miqdam T.
• Is Part Of: Case studies in thermal engineering. Volume 13(2019)
• Publication Date: 2019-03
• Subjects: Climate data -- Forecasting models -- Renewable energy -- Data analysis and visualization -- Environment monitoring; Heat engineering -- Case studies -- Periodicals; 621.40205
• DOI: 10.1016/j.csite.2018.11.006
• Languages: English
• ISSNs: 2214-157X

Abstract

Abstract: 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 R 2 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. 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.