An Examination of the Role of Artificial Intelligence in Managing Energy Consumption in Residential Buildings: A Case Study of Abuja Housing Estates
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1.1 Background of the Study
The management of energy consumption in residential buildings is critical for reducing energy waste, lowering utility costs, and promoting sustainability. Artificial Intelligence (AI) technologies have emerged as transformative tools for managing energy use by providing real-time data monitoring, usage forecasting, and automation of energy-intensive systems. In Abuja, the rapid expansion of housing estates and increasing energy demand have intensified the need for efficient energy management solutions.
AI-driven systems such as smart thermostats, energy monitoring sensors, and automated lighting systems can optimize energy use, reduce wastage, and enhance residents' comfort (Okeke & Aliyu, 2025). These technologies leverage machine learning algorithms to predict energy needs, recommend energy-saving measures, and enable dynamic adjustments. This study explores the role of AI in managing energy consumption in Abuja housing estates, focusing on its benefits and challenges in a Nigerian context.
1.2 Statement of the Problem
Abuja’s housing estates experience high energy consumption due to inefficient appliances, poor energy management practices, and rising urbanization. Traditional energy management methods fail to address these challenges effectively. AI offers advanced solutions to optimize energy consumption, but its adoption in residential settings in Nigeria remains low. This study investigates the role of AI in managing energy use and addressing inefficiencies in Abuja housing estates.
1.3 Objectives of the Study
- To examine the role of AI in optimizing energy consumption in residential buildings.
- To evaluate the effectiveness of AI-driven energy management systems in Abuja housing estates.
- To identify challenges in adopting AI technologies in residential energy management.
1.4 Research Questions
- How does AI optimize energy consumption in residential buildings?
- What is the effectiveness of AI-driven energy management systems in Abuja housing estates?
- What challenges affect the adoption of AI in residential energy management?
1.5 Research Hypothesis
- AI significantly enhances energy management in residential buildings.
- The adoption of AI-driven systems reduces energy waste in Abuja housing estates.
- Financial and technical barriers hinder the implementation of AI technologies in residential settings.
1.6 Significance of the Study
This study underscores the potential of AI in addressing energy inefficiencies in residential buildings. Its findings are relevant to property developers, policymakers, and technology providers aiming to promote sustainable living practices in Nigeria.
1.7 Scope and Limitations of the Study
The study focuses on the application of AI technologies for energy management in Abuja housing estates. It does not cover commercial or industrial energy management systems. Limitations include the availability of data on AI adoption and the diversity of energy consumption patterns in residential settings.
1.8 Operational Definition of Terms
- Energy Consumption: The amount of energy used by appliances and systems within a building.
- Artificial Intelligence (AI): Systems that use algorithms to simulate human intelligence for decision-making.
- Smart Thermostats: Devices that regulate heating and cooling systems based on real-time data.
- Energy Monitoring Sensors: Devices that track and report energy usage patterns.
- Sustainability: The ability to maintain energy practices without depleting resources.
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