Wind power generation forecasting is a crucial aspect in renewable energy industry since accurate predictions of wind power generation can support the power grid operators and power plant owners to optimize their energy resources management. This will potentially reduce the purchase of extra electricity from other stakeholders or neighboring countries and will consequence significant cost savings and further reliability. With the increasing importance of wind power utilization in modern power systems, employing accurate forecasting model cannot be understated. In this study, a new probabilistic forecasting model based on the utilization of quantile functions is discussed. The proposed model integrates an adaptive optimal weighted continuous ranked probability score (CRPS) is presented to improve the computational efficiency of the prediction process and enabling more accurate forecast output. Also, an adaptive Adam optimization algorithm is presented for the CRPS loss minimization. A convolutional neural network based long short-term memory (CNN-LSTM) is employed to evaluate the quantile function parameters. To validate the effectiveness of our approach, the specific forecasting models have been compared across a wide range of scenarios. The obtained results unequivocally reveal the superiority and improved accuracy of the proposed forecasting model.

The use of renewable energy in different parts of the world is increasing in recent years, therefore, understanding the way and how to use these energies is very important. Today, the use of climate and productive energies has attracted more researchers. The purpose of this research is to determine the potential of the wind turbine power plant and mapping in guilan province based on fuzzy logic and AHP. In this research, wind direction and speed data of 4 synoptic stations of guilan province were provided to assess the potential, mapping and determine the establishment of a wind turbine plant in guilan province for sustainable development using fuzzy logic in a statistical period for each synoptic station since its establishment until 2014. In this systemic model, the monthly and seasonal wind speed were scored using the fuzzy method and WindRose Plot and SabaWind software were used to determine the wind direction. Wind speed mapping map was drawn to establish a wind turbine plant with scores obtained for each of the stations using the GIS. The findings of the research show that fuzzy logic method showed a large variation to establish wind turbine power plants using wind speed and wind direction. In total, the Manjil station with the highest windy with the score of 29.29 were ranked first in the construction of wind turbines after obtaining the final rating each of the 4 stations in guilan province to build a high-confidence wind power plant and other stations are the next priorities in the province of Gilan for the establishment of the turbine plant with the scores (Bandar Anzali: 22.79, Astara: 20.37 and Rasht: 17.91). guilan province is suitable for establishing a turbine plant, because of the potential and windy favorable conditions to use it to meet the needs of various energies, including the electricity of the area. Therefore, it is necessary to take steady steps in order to develop this important industry. Keywords: Wind direction and Speed, Potentiometric, Fuzzy logic, Wind power plant, Statistical analysis

Relative humidity is considered to be one of the most important climatic parameters and atmospheric phenomena. The purpose of the present study is to evaluate the regional algorithms for estimating relative humidity using remote sensing data in Hormozgan province. To this end, MOD05 and MOD07 products were employed to estimate ...

The evapotranspiration of the reference Crop is of particular importance due to the changes in climate parameters of temperature, sunlight, humidity and wind speed in combination. the purpose of this study is to investigate the effects of climate change on evapotranspiration of apple during the growing season.For this purpose, ...