Background of the Study
University data centers are vital components of modern academic institutions, serving as hubs for data storage, management, and processing. However, these data centers can be energy-intensive, consuming substantial amounts of power, which poses both environmental and financial challenges. At Federal University, Kashere, located in Kashere LGA, Gombe State, managing the energy consumption of its data center is crucial for reducing operational costs and promoting sustainability.
Artificial intelligence (AI) offers an innovative approach to optimizing power consumption in data centers by analyzing operational data in real-time and adjusting the system’s performance to ensure energy efficiency. AI algorithms can predict energy demands, optimize cooling systems, and adjust the use of servers and storage to reduce unnecessary power usage. This study seeks to implement AI-powered solutions to optimize power consumption at the Federal University, Kashere’s data center.
Statement of the Problem
The Federal University, Kashere, is experiencing significant power consumption within its data center, leading to high operational costs and environmental concerns. Traditional methods of managing energy use do not provide the level of control and efficiency required to meet sustainability goals. The lack of AI-based optimization strategies means the university is missing opportunities to reduce energy consumption and minimize costs. Thus, there is a need for an AI-driven approach to optimize power usage in the university's data center.
Objectives of the Study
1. To design and implement AI-powered systems to optimize power consumption in the Federal University, Kashere data center.
2. To evaluate the effectiveness of AI algorithms in reducing energy consumption and operational costs in the data center.
3. To assess the impact of AI-based power optimization on the university’s overall sustainability efforts.
Research Questions
1. How can AI-powered systems be used to optimize power consumption in university data centers?
2. What is the impact of AI-based optimization on the energy efficiency and cost reduction of the Federal University, Kashere data center?
3. How does AI optimization contribute to the university’s sustainability efforts and overall operational efficiency?
Research Hypotheses
1. AI-powered optimization systems will significantly reduce the power consumption of the Federal University, Kashere data center.
2. The implementation of AI-based optimization will lead to a decrease in operational costs associated with the data center’s energy usage.
3. AI-powered power optimization will enhance the university’s sustainability efforts and improve its environmental impact.
Significance of the Study
This study will contribute to the use of AI in optimizing energy consumption in data centers, offering a solution for reducing both operational costs and the environmental footprint of university data centers. The findings will help Federal University, Kashere, enhance its sustainability practices and can serve as a model for other universities seeking to reduce energy consumption in their data centers.
Scope and Limitations of the Study
The study will focus on the implementation of AI-powered power consumption optimization for the data center at Federal University, Kashere, located in Kashere LGA, Gombe State. The study will be limited to power consumption and will not cover other aspects of data center management or sustainability.
Definitions of Terms
• AI-Powered Power Optimization: The use of artificial intelligence techniques to adjust and manage energy consumption in a system, such as a data center, to improve efficiency and reduce costs.
• Data Center: A facility used by universities or businesses to store, process, and manage large amounts of data.
• Energy Efficiency: The process of using less energy to perform the same tasks, thus reducing consumption and environmental impact.
• Sustainability: The ability to meet current needs without compromising the ability of future generations to meet their own needs, particularly in terms of environmental impact.
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