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All Journal Journal of Computer Science and Research Indonesian Journal of Computer Science and Engineering Journal Majelis Paspama
Obiora Jeremiah Obiafudo
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Mechanical Engineering

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Demand Forecasting of Domestic Gas Consumption: A Comparative Study of Trend Analysis, Moving Average, Single and Double Exponential Smoothing Methods Uchendu Onwusoronye Onwurah; Chukwuebuka Martinjoe U-Dominic; Christopher Chukwutoo Ihueze; Onyekachukwu Godspower Ekwueme; Obiora Jeremiah Obiafudo; Emmanuel Okechukwu Chukwumuanya
Indonesian Journal of Computer Science and Engineering Vol. 2 No. 01 (2025): IJCSE Volume 02 Nomor 01, Mei 2025
Publisher : CV. Cendekiawan Muda Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70656/ijcse.v2i01.429

Abstract

The increase in population and global economy has led to an increase in energy demand and consumption. Domestic gas consumption has continued to increase on a daily basis. Forecasting is essential to support decisions such as inventory management, production planning, and procurements in natural gas production and distribution. This study is aimed at forecasting natural gas demand in a selected area using trend analysis, moving average, single exponential smoothing, and double exponential smoothing techniques. 16 years (2009–2024) historical data were collected from a domestic gas distribution plant. The data were analyzed, and forecasts were made using trend analysis, moving average, single exponential smoothing, and double exponential methods. A comparative study revealed that trend analysis outperformed the other forecasting techniques, based on the lowest mean absolute percentage error (MAPE), mean absolute deviation (MAD), and mean squared deviation (MSD) as the decision criteria. The performance of double exponential smoothing is very close to that of the trend analysis. This study concludes that both trend analysis and double exponential smoothing, based on their lower MAPE and MAD, can be adopted by the gas plant in forecasting the domestic gas demand in the selected area.
Life-Cycle Cost Analysis of a 220Ah Tubular Battery in a Solar-Powered Academic Setting Obiora Jeremiah Obiafudo; Godspower Onyekachukwu Ekwueme; Ugochukwu Richards Orji; Clement Nworji Obiora
Journal Majelis Paspama Vol. 3 No. 02 (2025): Journal Majelis Paspama, 2025
Publisher : Journal Majelis Paspama

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Abstract

Reliable energy storage remains a critical challenge in sustaining solar-powered systems within academic environments, particularly in Nigeria where erratic grid supply hinders teaching and research activities. Batteries constitute the most cost-intensive component of solar installations, and their economic performance determines long-term viability. The study integrated MATLAB/Simulink simulations, field observations, and expert input. The analysis followed three stages: system modeling, performance evaluation, and economic benchmarking. Monocrystalline PV modules (220–330 W, 18–20% efficiency) were configured with 7° tilt and passive cooling to optimize performance in Nigeria’s tropical climate. A 60A MPPT controller and 1 kW inverter enhanced efficiency, while protections improved system reliability. Life-cycle cost analysis (LCCA) over 15 years at 10% discount rate compared tubular lead-acid and LiFePO₄ batteries, revealing LiFePO₄’s long-term cost advantage. Sensitivity analysis and benchmarking confirmed its superior cycle life, reduced maintenance, and lower levelized storage costs. The life-cycle cost analysis showed that tubular lead-acid batteries were cheaper upfront (₦92,000/kWh vs. ₦230,000/kWh) but incurred higher O\&M (₦46,000/kWh every 5 years) and required replacements at years 5 and 10, raising their 15-year cost to ₦400,200/kWh. LiFePO₄, though costlier (₦481,100/kWh total), offered longer lifespan, lower O\&M (₦18,400/kWh), and higher salvage value (₦34,500). Net Present Cost was lower for tubular (₦248,500/kWh vs. ₦289,200/kWh), yet LiFePO₄ delivered a better Levelized Cost of Storage (₦98/kWh vs. ₦127/kWh) and achieved payback in 8.2 years. Thus, tubular favored affordability, while LiFePO₄ provided superior long-term value and reliability for Nigerian universities. The study recommends a shift toward durable storage technologies to enhance reliability, reduce operating costs, and strengthen energy security in Nigerian universities.
Multivariate Analysis and Neural Network-Based Prediction of Compression Molding Behavior in Plantain–Bamboo Fiber Reinforced HDPE Composites Obiora Jeremiah Obiafudo; Joseph Achebo; Kessington Obahiagbon; Frank. O. Uwoghiren; Callistus Nkemjika Chukwu
Journal of Computer Science and Research (JoCoSiR) Vol. 3 No. 1 (2025): Jan: Computer Science
Publisher : Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) Provinsi Sumatera Utara

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Abstract

The compression molding behavior of plantain–bamboo fiber reinforced high-density polyethylene (HDPE) composites was studied through an integrated multivariate analysis and neural network modelling framework. The study utilized materials for fiber extraction and composite production, including water, alkali (NaOH), acetic acid, acetic anhydride, maleic anhydride grafted PE, hydrogen peroxide, hypochlorite, and caustic soda. The composite matrix was high-density polyethylene with density (0.96 g/cm³), reinforced with activated plantain and bamboo fibers. Methods involved mechanical extraction, chemical treatment using alkali solutions, neutralization, bleaching, and stabilization. Fibers were oven-dried, milled, and sieved to (75 μm) before composite formation. Process variables such as fiber fraction (10–50%) and temperature (150–190°C) informed the experimental design. A feed-forward neural network (5-5-5) was used for modelling system performance. The multivariate analysis used predictive neural network models to study combined process-variable effects during compression molding. Interaction plots were generated by varying fiber volume fraction (VF) against other variables. Results showed that high yield stress near (90 MPa) occurred at low VF (10–20%) when bamboo fiber ratio (BFR) was maintained at (40–60%). Pure plantain fiber outperformed pure bamboo at (0) and (1.0 BFR). Optimal molding temperature ranged (166–174°C), producing high yield stress even at VF (10%). At low temperatures (150°C) and VF (30%), yield stress exceeded (80 MPa). Maximum strength required holding times (>17 min) and low clamping force (<1900 N). Neural network predictions aligned closely with experimental data, demonstrating strong predictive reliability. This integrated statistical–computational approach provides valuable insights for optimizing natural fiber composite manufacturing and reducing experimental cost.
Co-Authors Callistus Nkemjika Chukwu Christopher Chukwutoo Ihueze Chukwuebuka Martinjoe U-Dominic Clement Nworji Obiora Emmanuel Okechukwu Chukwumuanya Frank. O. Uwoghiren Godspower Onyekachukwu Ekwueme Joseph Achebo Kessington Obahiagbon Onyekachukwu Godspower Ekwueme Uchendu Onwusoronye Onwurah Ugochukwu Richards Orji