Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Review Citation - WoS: 26Citation - Scopus: 28Exploring Geothermal Energy Based Systems: Review From Basics To Smart Systems(Pergamon-elsevier Science Ltd, 2025) Anya, Belka; Mohammadpourfard, Mousa; Akkurt, Gulden Gokcen; Mohammadi-Ivatloo, BehnamMost of the energy demand is currently supplied from fossil fuels, which leads to the accumulation of greenhouse gases and air pollution. A sustainable future can be created globally through the efficient use of renewable energy sources. These sources include wind, solar, geothermal, biomass, and more. Geothermal energy can meet the energy needs of the future as a clean and reliable source and stands out due to certain distinctive features among renewable energy sources. Unlike other renewable energy sources, geothermal energy is not dependent on time or weather, making it a reliable and continuous energy supply. Additionally, it has a lower environmental impact. This review examines the development of geothermal energy systems and their integration into smart technologies, highlighting the potential of geothermal energy for smart energy systems. The focus is on integrating smart systems into geothermal-based setups to enhance efficiency and analyze the state-of-the-art technologies of such systems. Geothermal-based systems can be classified as single generation, co-generation, multigeneration, smart energy systems, and energy hubs. Consequent to examining systems, it has been concluded that geothermal systems have a huge potential, but unfortunately, not all of them are used due to some difficulties. Its development will occur faster, and its share in the renewable energy sector will grow with smart system integration.Article Citation - WoS: 4Citation - Scopus: 5A Smart Building Energy Management Incorporating Clustering-Based Tariffs in the Presence of Domestic Solar Energy, Battery, and Electric Vehicle(Pergamon-elsevier Science Ltd, 2024) Alilou, Masoud; Mohammadi-ivatloo, Behnam; Mohammadpourfard, MousaSmart buildings play a crucial role in optimizing energy management within the power network. As end-users of the power network, they have the ability to not only reduce economic costs for householders but also modify the technical indices of the power network. To promote efficient device management in smart homes (SH), demand response programs are recommended for consumers. This research investigates the application of clusteringbased electricity pricing strategy aimed at effectively managing the energy devices of a residential smart home. The utilized method categorizes the electricity tariff into five rates according to the clustering of the realtime pricing program. Ward's clustering method is utilized to cluster and determine new electricity tariffs. The primary goal of the energy management program is to minimize the building's energy cost, which is accomplished through the utilization of the multi-verse optimizer. The smart home consists of essential and manageable appliances, a photovoltaic panel (PV), a sodium-sulfur (NaS) battery, and an electric vehicle (EV). The initial parameters of the PV and EV are modeled stochastically by their probability distribution functions and calculated using the Latin hypercube sampling algorithm. The smart building's performance is assessed by taking into account various demand response programs. The numerical results present that the application of the clusteringbased management method has resulted in a significant reduction of 23-43 % in the electricity cost of smart homes. Additionally, the smart home exhibits a more linear consumption pattern when considering the electricity tariffs based on the clustering approach.