Browsing by Author "Dodo,Y.A."

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Citation Count: 2
Energy-carbon emission nexus in a residential building using BIM under different climate conditions: an application of multi-objective optimization
(Frontiers Media SA, 2023) Alhamami,A.H.; Dodo,Y.A.; Naibi,A.U.; Alviz-Meza,A.; Mokhtarname,A.
This study was carried out to investigate the impact of building insulation, a method of reducing energy consumption, on the amount of energy consumed in a building, as well as its impact on cooling and heating loads and carbon emission. A residential structure was designed in Revit, and DesignBuilder determined the cooling and heating loads, as well as the energy consumption. Under three distinct climate conditions, the impact of the environment on the energy-carbon emission nexus of residential buildings was assessed. The cold mountain climate of Koick, Slovakia; the arid desert climate of Ha’il, Saudi Arabia; and the tropical monsoon climate of Borneo, Indonesia were chosen. During the design stage, the Particle Swarm Optimization (PSO) method was used to minimize the energy consumption cost (ECC) and CO2 emissions. Over the course of 24 h, the cooling and heating loads decreased by 2.51 kW and 1.9 kW, respectively. When the two modes in Ha’il were combined, the heating load was reduced to 850 kWh and the cooling load was reduced to 650 kWh, according to the results. In Borneo, the heating load was reduced by 200 kWh, while in Koick, it was reduced by 2,000 kWh. The cooling load was reduced by 550 and 50 kWh in Borneo and Koick, respectively. This system appears to perform better in arid and hot climates in terms of both heating and cooling loads. However, energy losses in the arid and hot climate (Ha’il) are greater than in other climates. This could be due to temperature and humidity differences between the inside and outside. According to the findings of the PSO evolutionary algorithm optimization, the building can be constructed to reduce ECC by 19% by taking into account input characteristics such as Wind-to-Wall Ratio (WWR), wall, glazes, and weather conditions. This research provides useful insights into the practical application of optimization methods for reducing CO2 emissions, paving the way for more sustainable and eco-conscious architectural practices. Copyright © 2023 Alhamami, Dodo, Naibi, Alviz-Meza and Mokhtarname.
Citation Count: 0
An innovative method for building electricity energy management in smart homes based on electric vehicle energy capacity
(Frontiers Media SA, 2024) Dodo,Y.A.; Ibrahim,A.O.; Abuhussain,M.A.; Baba Girei,Z.J.; Maghrabi,A.; Naibi,A.U.
The surging demand for electricity, fueled by environmental concerns, economic considerations, and the integration of distributed energy resources, underscores the need for innovative approaches to smart home energy management. This research introduces a novel optimization algorithm that leverages electric vehicles (EVs) as integral components, addressing the intricate dynamics of household load management. The study’s significance lies in optimizing energy consumption, reducing costs, and enhancing power grid reliability. Three distinct modes of smart home load management are investigated, ranging from no household load management to load outages, with a focus on the time-of-use (ToU) tariff impact, inclining block rate (IBR) pricing, and the combined effect of ToU and IBR on load management outcomes. The algorithm, a multi-objective approach, minimizes the peak demand and optimizes cost factors, resulting in a 7.9% reduction in integrated payment costs. Notably, EVs play a pivotal role in load planning, showcasing a 16.4% reduction in peak loads and a 7.9% decrease in payment expenses. Numerical results affirm the algorithm’s adaptability, even under load interruptions, preventing excessive increases in paid costs. Incorporating dynamic pricing structures like inclining block rates alongside the time of use reveals a 7.9% reduction in payment costs and a 16.4% decrease in peak loads. In conclusion, this research provides a robust optimization framework for smart home energy management, demonstrating economic benefits, peak load reduction potential, and enhanced reliability through strategic EV integration and dynamic pricing. Copyright © 2024 Dodo, Ibrahim, Abuhussain, Baba Girei, Maghrabi and Naibi.