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<title>Channels - Modelling and forecasting periodic electric load for a metropolitan city in Nigeria :: FRELIP Discovery
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Modeling and forecasting of short-term half-hourly electric load at the University of Ibadan, Nigeria
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BiKAN-LoadNet: An Electric Load Forecasting Model Based on Bidirectional LSTM and Kolmogorov–Arnold Networks
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Utility of information in package inserts by pharmacists and pharmacy clients in a metropolitan city in Southwest Nigeria
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Assessing Air Quality Variation Focusing on Criteria Pollutants in Metropolitan City, Addis Ababa, Ethiopia
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Hybrid Multi-Scale Deep Learning Enhanced Electricity Load Forecasting Using Attention-Based Convolutional Neural Network and LSTM Model
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Prevalence and clusters of modifiable cardiovascular disease risk factors among intra-city commercial motor vehicle drivers in a Nigerian metropolitan city
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Explainable Load Forecasting with Covariate-Informed Time Series Foundation Models
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Hisbah and Sharia Law Enforcement in Metropolitan Kano
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An econometric analysis in identifying behavioral and demographic factors associated with road crash severity in Bangladesh: Evidence from the Dhaka metropolitan city
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Multi-step hourly electrical load forecasting for a single household: A hybrid deep learning approach based on CNN, BiGRU, and Attention Mechanism
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A load forecasting method based on edge graph attention network
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Analysis of Libraries’ Onboarding Documentation at a Metropolitan University Consortium
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Survival of Translocated Coyotes in the Chicago Metropolitan Area, USA
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In this work, three models are used to analyze the electric load capacity of a fast growing urban city and to estimate its future consumption. Ikorodu, the case-study location is a highly populated city whose energy demand is continuously increasing. The ultimate focus of this study is to establish a basis for the comparison of different electric load consumption for the existing populace and to provide estimates for the future planning of the city. In this work, three different models have been used to present more accurate load predictions and to enhance proper comparison of results. Among numerous mathematical and scientific models that are applicable to this kind of task, the compound-growth method, the linear model approach and the cubic model have been chosen to enhance diversity in load analysis. The futuristic scheme to be harnessed will fall within the ranges of values obtained from the three different models used in forecasting. This paper concludes with issues pertaining to economics of load utilization as it affects substantive planning.
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MP-Transformer: A Hybrid Model Integrating Multi-Period ARIMA and Dynamically Gated Attention for Time-Series Forecasting
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Enhancing Information Sensing of Load Forecasting in Cyber-Physical-Social Systems: An Approach with Large Language Model
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Quantum-Inspired Deep Learning Model for Short-Term Load Forecasting in Smart Grid Energy Management
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Attentive Dual‐Domain Modelling for Error‐Resilient Net Load Forecasting in Intermittent Renewable Power Systems
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Some operations of electric power supply system in Benin City area of Nigeria
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Assessing the status of physical and health education in Ibadan metropolitan schools
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LEED and the Galisteo Basin Preserve: Sustainable Solution to Metropolitan Sprawl?
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Surface Mesovortices Formation and Maintenance in the St. Louis Metropolitan Area
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Schedulable capacity forecasting for electric vehicles based on big data analysis
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Feature selection for probabilistic load forecasting via sparse penalized quantile regression
