Building Technology

Permanent URI for this collectionhttp://197.211.34.35:4000/handle/123456789/135

Building Technology

Browse

Author: search.filters.author.Musa Titus DadaHas files: Yes

Search Results

Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    Assessment of Electrical Energy Consumption in Some Selected Tertiary Institutions Administrative Buildings in Niger State
    (School of Environmental Technology, Federal University of Technology Minna. PMB 65, Minna, Niger State Nigeria., 2024-11-29) Musa Titus Dada; Akanmu Williams Paul; Jimoh Richard A; Ejiga Anthony Ejiga
    High electrical energy consumption in public buildings and institutions poses a significant challenge, particularly in developing countries like Nigeria. Inadequate empirical studies on building energy use have resulted in a lack of electrical energy data, especially in tertiary institutions where bulk metering is common. This study aimed to assess the electrical energy consumption in selected administrative buildings of tertiary institutions in Niger State, with the goal of reducing electrical consumption and improving energy efficiency. Experimental data collection involved direct field measurements using a real-time Efergy wireless energy (EW4500) monitoring device. Current transformer sensors were attached to the main distribution panels of the administrative buildings at the Federal University of Technology Minna, Niger State Polytechnic Zungeru, and Niger State College of Education Minna. The objectives included evaluating electrical energy consumption, comparing total consumption across buildings, and analyzing energy consumption patterns. Results indicated that the Senate building at FUT Minna had the highest consumption rate at 2604.7 KWh/m², followed by Niger State Polytechnic Zungeru at 2579.1 KWh/m², both exceeding the global benchmarks of 128 to 130 kWh/m² set by the Chartered Institute of Building Services Engineers (CIBSE) and the Building Energy Efficiency Guideline for Nigeria (BEEGN). In contrast, COE Minna’s administrative building, with consumption levels averaging 1579.1 KWh/m², generally fell within these benchmarks. The elevated energy consumption at FUT Minna and Niger State Polytechnic Zungeru was primarily attributed to operational inefficiencies, such as the continuous operation of HVAC systems and equipment during non-essential hours, even when buildings were not fully occupied. Hourly consumption patterns revealed peak usage during early working hours, with significant seasonal variations; however, both FUT Minna and Niger State Polytechnic Zungeru exhibited high energy use during off-peak periods, reflecting poor energy management practices. To address these inefficiencies, the study recommends conducting comprehensive energy audits and installing energy-efficient appliances at FUT Minna and Niger State Polytechnic Zungeru. Additionally, implementing smart metering, occupancy sensors, and optimized HVAC controls would significantly enhance monitoring and reduce energy consumption. These measures are critical for improving energy efficiency and ensuring sustainable operations in both institutions.
  • No Thumbnail Available
    Item
    Optimizing HVAC Systems for Sustainable Lecture Rooms: Harnessing Environmental and Occupancy Data for Comfort and Energy Efficiency through Data-Driven Insights
    (School of Environmental Technology, Federal University of Technology Minna. PMB 65, Minna, Niger State Nigeria., 2024-11-29) Abdulwaheed Badmus; Musa Titus Dada
    The escalating energy consumption in campus infrastructure, especially in lecture halls with heating, ventilation, and air conditioning (HVAC) systems, necessitates data-driven optimization strategies. This research demonstrates the integration of Internet of Things (IoT) sensors with cloud-based predictive analytics to develop intelligent lecture room policies aimed at enhancing efficiency and sustainability. A Raspberry Pi-based IoT device, equipped with a BME680 sensor for monitoring temperature, humidity, and air quality, and a passive infrared sensor for occupancy detection, was installed in a university lecture room for real-time data acquisition. Data collected was routed through MySQL for storage and Node-RED for preprocessing. Time series forecasting models, including ARIMA and Prophet, along with machine learning models like XGBoost, achieved over 90% forecast accuracy for temperature and occupancy levels, enabling proactive control of environmental conditions. The optimized HVAC scheduling, based on forecasted occupancy patterns, resulted in a 20% reduction in energy consumption over an 8-week deployment, ensuring thermal comfort by maintaining temperatures within the recommended range of 21-23°C during occupancy. Enhanced occupant comfort was also achieved by maintaining humidity levels between 40-60%, improving indoor air quality through proactive ventilation control. Key recommendations include dynamic HVAC scheduling based on occupancy forecasts, thermostat setpoint adjustments to prevent temperature peaks, and expanding IoT sensor deployments across campus facilities to generate deeper insights. This integrated IoT and predictive analytics approach enabled a sustainable and responsive built environment, providing a scalable framework for optimizing other infrastructure types such as laboratories and offices.