This paper studies power management in a grid-tied hybrid energy system consisting of photovoltaic array, wind turbine, fuel cell, electrolyzer, hydrogen storage tank and a combinational heating system to supply the thermal and electrical demand of a building. Moreover, the hybrid system is capable of exchanging power with local grid. Thus, variable daily buying and selling tariffs are also taken into account so as to cover a wide range of operational conditions. The thermal demand is supplied by both electric heating system and the fuel cell exhaust heat. The paper formulates the matter in the form of a nonlinear constrained optimization problem and then evaluates several well-known heuristic optimization techniques. The performance of each of these algorithms is discussed in detail and the elite algorithm is introduced. Furthermore, the effect of the initial charge of the hydrogen storage tank on total operation cost and also charge remained are fully discussed. In order to fulfill this intention, a novel criterion is presented to determine the optimal initial charge. The validation of the results will be implemented based on simulations.

Background and Theoretical Foundations: Climatic processes have widespread effects on the lives of humans and living organisms, particularly sea surface temperature (SST) changes, which play a key role in the distribution of heat from tropical to polar regions. These changes can influence weather patterns and climatic phenomena such as precipitation, wind, and air temperature. SST is affected by factors like ocean currents, wind speed, and temperature, and can impact regions far from the coastline. Various studies have shown that SST variations influence rainfall in different parts of the world, including Iran. These changes are particularly linked to events like El Niño oscillations and climate change in tropical areas. Investigating SST is crucial for predicting natural phenomena, managing crises, and improving weather forecasting models, as it helps to better understand the relationships between air and sea. Methodology: In this study, sea surface temperature (SST) data were used to analyze temperature change trends. The data included SST retrievals from the ECMWF ERA-Interim database for the years 1980 to 2015, satellite remote sensing SST data from the NOAA AVHRR sensor for the years 1981 to 2006, and MWOI satellite data for 1998 to 2015. SST data with a spatial resolution of 0.125×0.125 degree were extracted from the ECMWF database for the period 1979 to 2015 on a daily basis. Monthly SST data were obtained using the AVHRR sensor, and MWOI microwave satellite datasets were used for marine biology monitoring and SST changes. To evaluate the SST trends, the non-parametric Mann-Kendall test was applied, which is capable of identifying non-linear trends and is resistant to outliers. This method is particularly effective for studying short-term changes and climatic patterns such as El Niño and La Niña. Findings: This research analyzed the spatiotemporal changes of sea surface temperature (SST) in the southern waters of Iran from 1980 to 2015. Initially, SST data from the ECMWF ERA-Interim database were used to analyze SST trends on a monthly scale. The results showed significant seasonal variations in SST, especially in the Persian Gulf, which experiences higher temperature fluctuations due to its shallow depth and high salinity compared to the Sea of Oman and the Indian Ocean. During the warmer seasons, the Persian Gulf has higher temperatures due to the influx of colder river waters and its shallow depth, while the Sea of Oman and the Indian Ocean exhibit less temperature fluctuation. Mann-Kendall trend analysis revealed an increasing SST trend in the Persian Gulf in most months, with some areas showing decreasing trends. These temperature changes have varying effects on coastal ecosystems and marine life depending on the season. Additional analysis using AVHRR and MWOI satellite data indicated that the temperature differences between satellite data sets were due to differences in imaging conditions and measurement instruments. Overall, the study demonstrates that the Persian Gulf, due to its unique geographical features, experiences greater temperature fluctuations than other regions, and these changes can have significant impacts on local coastal and marine ecosystems. Conclusion: This study examined the spatiotemporal changes of sea surface temperature (SST) in the southern waters of Iran. SST variations were analyzed using remote sensing data from the AVHRR and MWOI sensors, as well as ECMWF data. The results indicated that the Persian Gulf exhibits the greatest SST fluctuations, while the Indian Ocean shows the least variability. Factors such as depth, area, and salinity significantly influence SST fluctuations. Additionally, the analysis of temperature trends showed an increasing trend in most months of the year, with some months exhibiting a decreasing trend. The Persian Gulf, due to its high temperature variability, has a more fragile ecosystem compared to the Sea of Oman and the Indian Ocean. The satellite data analysis also showed that the Persian Gulf experiences greater temperature variations than the Indian Ocean, and these changes are less pronounced during the warmer seasons.

Introduction Land use/cover information is vital to the dynamic monitoring, planning and management, and the reasonable development of land. Recently, due to human activity, land cover information has changed dramatically. Furthermore, construction, land has become increasingly scarce, and the non-agriculturalization of arable land has been highlighted. Therefore, it has become increasingly significant to timely, and accurately monitor land use and land cover for the reasonable development and utilization of urban land resources in city regions. It is significant to timely, accurate, and effectively monitor land cover for conservation, reasonable development and land resources. The remotely sensed dynamic monitoring of covered land in rapidly developing regions has increasingly depended on remote-sensing data on temporal and spatial resolutions. In many cases, it is difficult to acquire enough time-series images with high quality at both high temporal and spatial resolution from the same sensor.In this research, Landsat images and an object-oriented method were used to eliminate errors using the visual interpretation method to prepare a land cover map and achieve acceptable accuracy and classification results. Landsat 8 data was used to prepare the land cover map using spatio-temporal integration model. In addition, through an object-oriented classification method, land cover was extracted, which was used to provide a more accurate and efficient technical method for effectively extracting land cover information in Bushehr province. Materials and Methods In this paper, we proposed a method for mapping land use and land cover in a Bushehr province area with high spatial-temporal resolution using fusing Landsat 8 time series. The method has three steps, 1) Enhance the spatial-temporal adaptive reflectance fusion model (ESTARFM), 2) Determination the optimal data combined for the extraction of cover type, 3) Image segmentation and Land cover extraction and the accuracy assessment based on the field sample method was used. In many cases, it is difficult to acquire enough time-series images with high quality at both high temporal and spatial resolution from the same sensor. This study used the temporal-spatial fusion model ESTARFM (Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model) to blend Landsat 8 and MODIS data and obtain Landsat 8 images. Then, the cover lands information of Bushehr province is extracted using an object-based classification method. Results and Discussion In this paper, an object-oriented LULC mapping method using time series Landsat 8 images was proposed. Based on the time-series Landsat 8 data (red band, NIR band and NDVI), the land cover types. In this study, the proposed method was used in a case study of Bushehr province. The results of the object-based method show that the overall accuracy and Kappa coefficients were 93.34% and 0.86, respectively, and the user/producer accuracies of cover lands in the pixel-based method were all over 80%. The approach presented an accurate and efficient technical method for effectively extracting land use information in heterogeneous regions. In this paper, we have achieved an acceptable classification result using Landsat 8 image. There are still some potential factors affecting the accuracy. The first factor is the uncertainties of the fused images by the ESTARFM fusion model. The second is the mixed pixel problem, the Landsat image pixels are always informed with several land cover types, which will affect the classification accuracy. Remote sensing images with high spatial resolution may be a feasible way to achieve higher accuracy. Conclusion In the present study, based on the combined data of Landsat 8 and object-oriented classification, the land cover map of Bushehr province was extracted. Types of pasture and desert land cover were clearly presented. In this research, an object-oriented method was presented for the preparation of pasture cover type map using the images generated from ESTARFM time-spatial integration model. Based on Landsat 8 data (red band, near-infrared band and normalized vegetation difference index), the types of pasture cover in Bushehr province were extracted using visual and object-oriented methods.The use of Landsat 8 images and the object-oriented method improved the classification. But there are some factors that affect the accuracy of preparing land use and land cover maps. The first uncertainty factor is the fusion images by the enhanced spatio-temporal adaptive reflectance model (ESTARFM). The second problem is the existence of heterogeneous pixels. In heterogeneous areas, the presence of small polygons in which the pixels of the Landsat image of vegetation cannot be separated in these polygons, which affects the classification accuracy. Remote sensing images with high spatial resolution may be a practical way to achieve higher accuracy. Therefore, the object-oriented method combined with spectral integration analysis can be improved to achieve land cover information extraction. The results of this research showed that the overall accuracy and Kappa coefficients in the object-oriented method were 93.34% and 0.86, respectively, and the accuracy of the land cover user/producer in the pixel-oriented method was more than 80%. The object-oriented algorithm analyzes images as objects by merging neighborhood information, which enhances the analysis and increases the classification accuracy. The object-oriented algorithm has shown its potential in identifying land cover and preparing land cover in heterogeneous areas.