Browsing by Author "Naibi,A.U."

Now showing 1 - 5 of 5

Citation Count: 0
Building Occupancy Detection for Energy-Saving: Exploring the Current Technologies and Methods with their Underlying Issues
(Collegium Basilea, 2024) Girei,Z.J.B.; Chukwumauchegbu,M.I.; Adewolu,A.O.; Naibi,A.U.; Uwa,J.N.
Non-intrusive indoor environment sensing for occupancy detection and estimation has attracted extensive research interest in the building domain over the past decade due to the increasing number of applications for improving building infrastructure. Occupancy detection and estimation can be integrated into building appliances to manage lighting applications, intrusion detection in secured building areas, and occupancy-driven ventilation which has the potential to improve the performance of the Heating Ventilation and air-conditioning (HVAC) system through the finegrained occupant count to enhance the trade-off between thermal comfort and energy consumption. The research strategies for occupancy detection and estimation have utilized different technologies (including camera, wearable, and indoor environmental variables sensing through direct sensing and machine learning), which experience challenges in terms of acquiring essential sensory data related to occupancy information and correctly modeling the occupancy data due to hardware deployment limitations and underlying cost. This study explores existing technologies and methods for occupancy detection and estimation with their underlying issues. It provides a comprehensive procedure for occupancy modeling methodology using different machine learning methods and analyzing their comparative results to assist in decision making for choosing an optimal technique for solving occupancy detection and estimation problem. The results recommend Random Forest as a candidate model with high performance achieving 73.6% to 99.7% for occupancy detection and overall, 99.3% for occupancy estimation. © 2024, Collegium Basilea. All rights reserved.
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.
Citation Count: 0
REVIEW OF HEATING VENTILATION, AND AIR-CONDITIONING (HVAC) SYSTEM MODELING TECHNIQUE
(National Society of Environmental Science and Engineering (SNSIM), 2023) Chukwuma-Uchegbu,M.I.; Girei,Z.J.B.; Mounir,S.; Abdul-Azeez,A.I.; Maaloufa,Y.M.; Naibi,A.U.; Uwa,J.N.
This study provides a review of the System Modeling Technique, Heating, Ventilation, and Air-Conditioning (HVAC) system. HVAC is the major consumer of building energy consumption. Some of the factors that contribute to high energy usage include poor management of the temperature set-point, air infiltration, window type, window-wall ratio, and internal heat loss or gain. According to research, the proper management of HVAC systems operation can result in 25% energy savings while maintaining a comfortable indoor atmosphere. A review paper addressing these problems could offer a thorough understanding of the modeling approaches now utilized in HVAC systems. However, ineffective HVAC system design and operation can lead to excessive energy usage. Through a comprehensive literature review. This study identified that; the control system and optimization parameters of an HVAC system play a significant role in how effectively it operates. Instead of upgrading HVAC equipment with newer, more energy-efficient technology, it is much more affordable and sustainable to enhance the control algorithms. As a result, an extensive understanding of the role, application, benefits, drawbacks, and consequences of various modeling methodologies used in HVAC systems is required for the selection and design of an acceptable model. Furthermore, this study examines the benefits and a disadvantage of various modeling methodologies, as well as the flaws of several existing studies/developed models, culminating in recommendations based on a comprehensive literature assessment. The study discovered that each modeling technique has both good and negative elements to consider when selecting a technique for model development by comparing three basic modeling strategies based on performance criteria. © (2023), (Procedia Environmental Science). All rights reserved.
Citation Count: 0
SMART INDOOR THERMAL COMFORT CONTROL
(National Society of Environmental Science and Engineering (SNSIM), 2023) Uwa,J.N.; Mounir,S.; Girei,Z.J.B.; Aliyu,J.; Naibi,A.U.; Chukwuma-Uchegbu,M.I.
Current research in building performance is giving more attention to strategies to improve HVAC system performance to ensure healthy indoor ventilation and comfort conditioning. An efficient control is required to minimize the energy usage input based on defined occupants’ thermal comfort constraint to achieve this goal. Over the years Smart Home Energy Management System (SHEMs) solutions have been used to control the performance of HVAC systems through advanced control strategies whereby ambient conditions and building energy profiles become an integral part of the system. However, recent investigation reveals most of the SHEMs are based on ambient temperature and humidity constraints which cannot fully reflect precise thermal comfort sensation and result in higher discomfort situations and energy usage. To improve the current approaches the study considered more input parameter constraints including occupancy number, reference comfort level, and electricity price modeled using discrete-time models for the control system. Quadratic cost function design for linear optimal control systems is employed to optimize desired temperature setpoint outputs to maximize thermal comfort and lower the energy consumption cost. To test and evaluate the proposed approach’s performance, a real-time price electricity scheme was used. The result analysis shows the proposed method achieved comfort conditioning with lower energy input and discomfort situations compared to previous approaches. © (2023), (Procedia Environmental Science). All rights reserved.