Improved Medium Term Approach for Load Forecasting of Nigerian Electricity Network Using Artificial Neuro-Fuzzy Inference System: A Case Study of University of Nigeria, Nsukka

Eya, Candidus U.; Salau, Ayodeji Olalekan; Braide, S. L.; Chigozirim, Onwe Divine

doi:10.1016/j.procs.2023.01.232

Cited by 5 publications

(4 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“… 46 The network that establishes the model is composed of three layers of basic processing units connected by cyclic linkages. The following equation is used to denote the link between output

and inputs

, 47 where The model parameters are denoted as

and

, where n denotes the length of input nodes, while z denotes the length of hidden nodes in the equation above.…”

Section: Methodsmentioning

confidence: 99%

A hybrid forecasting technique for infection and death from the mpox virus

Iftikhar,

Daniyal,

Qureshi

et al. 2023

DIGITAL HEALTH

View full text Add to dashboard Cite

Objectives The rising of new cases and death counts from the mpox virus (MPV) is alarming. In order to mitigate the impact of the MPV it is essential to have information of the virus's future position using more precise time series and stochastic models. In this present study, a hybrid forecasting system has been developed for new cases and death counts for MPV infection using the world daily cumulative confirmed and death series. Methods The original cumulative series was decomposed into new two subseries, such as a trend component and a stochastic series using the Hodrick–Prescott filter. To assess the efficacy of the proposed models, a comparative analysis with several widely recognized benchmark models, including auto-regressive (AR) model, auto-regressive moving average (ARMA) model, non-parametric auto-regressive (NPAR) model and artificial neural network (ANN), was performed. Results The introduction of two novel hybrid models, [Formula: see text] and [Formula: see text], which demonstrated superior performance compared to all other models, as evidenced by their remarkable results in key performance indicators such as root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE), is a significant advancement in disease prediction. Conclusion The new models developed can be implemented in forecasting other diseases in the future. To address the current situation effectively, governments and stakeholders must implement significant changes to ensure strict adherence to standard operating procedures (SOPs) by the public. Given the anticipated continuation of increasing trends in the coming days, these measures are essential for mitigating the impact of the outbreak.

show abstract

“… 46 The network that establishes the model is composed of three layers of basic processing units connected by cyclic linkages. The following equation is used to denote the link between output

and inputs

, 47 where The model parameters are denoted as

and

, where n denotes the length of input nodes, while z denotes the length of hidden nodes in the equation above.…”

Section: Methodsmentioning

confidence: 99%

A hybrid forecasting technique for infection and death from the mpox virus

Iftikhar,

Daniyal,

Qureshi

et al. 2023

DIGITAL HEALTH

View full text Add to dashboard Cite

show abstract

“…Three institutional hourly energy consumption and weather variables for three years were used to feed the DL algorithms, and they showed that LSTM performed well compared to CNN and MLP. Eya et al (2023) also forecasted medium-term energy demand for the University of Nsukka using AI techniques. The ANN and artificial neuro-fuzzy inference system (ANFIS) were used for analysis based on monthly energy used data between 2014 and 2019.…”

Section: Empirical Reviewmentioning

confidence: 99%

“…Also, in terms of comparing the suitability of the artificial intelligence models, the evidence in the literature is mixed as to the most optimum machine learning algorithm to model electricity demand. For instance, Abdusalam et al (2016), Melodi et al (2017), Saglam et al (2022), andHasanah et al (2020) provided evidence that supports the optimal performance of neural network ML algorithm; however, Chapagain et al (2020), Yotto et al (2023 and Eya et al (2023) refuted their optimal performance. However, studies about electricity demand modelling using ML techniques for Nigeria, such as Abdusalam et al (2016), Melodi et al (2017) and Adewuyi et al (2020) adopted the neural network algorithms, but Eya et al (2023) that also used the neural network algorithms for Nigeria reported that the algorithm underperformed compared to artificial neuro-fuzzy inference system (ANFIS) algorithm.…”

Section: Table 23 Herementioning

confidence: 99%

Investigating the Drivers of Grid Electricity Demand in Nigeria: Harnessing the Power of Machine Learning and Artificial Intelligence Algorithms

Olubusoye,

Emmanuel,

Nwobi

et al. 2023

Preprint

View full text Add to dashboard Cite

The electricity situation in Nigeria is epileptic, and several households rely on alternative sources of electricity to power homes and businesses. In an urban area of the country, four (4) out of every six (6) households operate an alternative electricity supply. On the other hand, individuals in rural areas can hardly afford alternative electricity sources. More so, urban individuals use mostly alternative sources that are non-renewable (such as generators), thereby causing environmental degradation and promoting Climate-related issues. The direct impact of these alternative electricity sources has led to the death of entire families due to carbon monoxide breathing by household occupants/family and caused detrimental health issues to nearby households on several occasions. To address electricity instability issues and promote a sustainable and reliable green energy future, it is worthwhile to identify the factors influencing the grid electricity demand and develop efficient models for identifying these factors and their influence on grid electricity demand. The study finds that classical algorithms are ultimately inefficient due to the multicollinearity component of the relevant features. Consequently, the study harnesses the power of Blackbox and Glassbox algorithms - Deep Neural Network (DNN) and Multivariate Adaptive Regression Spline (MARS), respectively, to investigate these factors. The two learning algorithms agreed that Nigeria's electricity demand is majorly driven by Macroeconomic and Climatology variables – with Rural population, Temperature and GDP per capita being the most relevant drivers of electricity demand in Nigeria. Given the relevance of the GDP per capita, the result implies that the discrepancies in the socioeconomic characteristics of households or individuals in Urban to Rural played a major role in the electricity demand in Nigeria. Hence, the study concludes that by addressing only Electricity problems, Nigeria will achieve fifty-three (53) percent of the global SDG agenda and greater economic development.

show abstract

“…Ghenai et al [49] developed an ANFIS model to predict energy consumption in the very short term (0.5, 1, and 4 h) at the University of Sharjah campus, Sharjah, United Arab Emirates, where their results were very accurate at the 30-min horizon, reducing its estimation approach as the estimation took a longer amount of time to predict, concluding that the model requires a large amount of data collected to be able to carry out training that allows the predictive horizon to be extended without a greater margin of inaccuracies. Eya et al [50] presented an improved ANFIS predictive model for the estimation of the weekly and monthly load consumption for six months of the electrical system of the University of Nigeria, Nsukka. For its design and training, they carried out a collection of historical environmental and consumption data during the period 2014 to 2019.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Electricity Generation of a 60-kW Photovoltaic System with Intelligent Models ANFIS and Optimized ANFIS-PSO

Lara-Cerecedo,

Hinojosa,

Pitalúa-Díaz

et al. 2023

Energies

View full text Add to dashboard Cite

The development and constant improvement of accurate predictive models of electricity generation from photovoltaic systems provide valuable planning tools for designers, producers, and self-consumers. In this research, an adaptive neuro-fuzzy inference model (ANFIS) was developed, which is an intelligent hybrid model that integrates the ability to learn by itself provided by neural networks and the function of language expression, how fuzzy logic infers, and an ANFIS model optimized by the particle swarm algorithm, both with a predictive capacity of about eight months. The models were developed using the Matlab® software and trained with four input variables (solar radiation, module temperature, ambient temperature, and wind speed) and the electrical power generated from a photovoltaic (PV) system as the output variable. The models’ predictions were compared with the experimental data of the system and evaluated with rigorous statistical metrics, obtaining results of RMSE = 1.79 kW, RMSPE = 3.075, MAE = 0.864 kW, and MAPE = 1.47% for ANFIS, and RMSE = 0.754 kW, RMSPE = 1.29, MAE = 0.325 kW, and MAPE = 0.556% for ANFIS-PSO, respectively. The evaluations indicate that both models have good predictive capacity. However, the PSO integration into the hybrid model allows for improving the predictive capability of the behavior of the photovoltaic system, which provides a better planning tool.

show abstract

Improved Medium Term Approach for Load Forecasting of Nigerian Electricity Network Using Artificial Neuro-Fuzzy Inference System: A Case Study of University of Nigeria, Nsukka

Cited by 5 publications

References 6 publications

A hybrid forecasting technique for infection and death from the mpox virus

A hybrid forecasting technique for infection and death from the mpox virus

Investigating the Drivers of Grid Electricity Demand in Nigeria: Harnessing the Power of Machine Learning and Artificial Intelligence Algorithms

Prediction of the Electricity Generation of a 60-kW Photovoltaic System with Intelligent Models ANFIS and Optimized ANFIS-PSO

Contact Info

Product

Resources

About