Reactive Power Compensation Improvement Technique for Injection Substations in Nigeria

Idoniboyeobu, D. C.; Wokoma, Biobele A.; Evoh, S.

doi:10.14299/ijser.2018.04.03

Cited by 5 publications

(5 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Advanced techniques like ANNs and ensemble methods, as well as statistical methods like ARIMA and Bayesian time series analysis, are effective in achieving high-accuracy predictions. In the work of [10], exponential regression was used to analyze and forecast the load needed to be generated by generating stations, considering the epileptic condition of the power supply and possibly planning for future increments. [25] the forecast shows that the peak load for Nigeria is between 7 pm -10 pm which then encourages people to respond by changing their consumption behavior by charging consumer that consume energy during the peak time more.…”

Section: Literature Reviewmentioning

confidence: 99%

High Precision Modeling of Power Demand for Electricity Distribution Companies with frequent load shedding

Oladeji

Fasogbon

2023

Preprint

View full text Add to dashboard Cite

Globally, electricity forecast plays a critical role in ensuring the stability of the national grid. However, developing counties are challenged with a lack of data infrastructure for continuous data capture, storage, and process that ensure quality data are available for short-term power forecasting, a critical requirement for maintaining grid stability but at the same time utilizing loading scheduling for unbalanced power distribution and grid stability. This led to a huge gap and significant paucity in existing historical power demand. This research provides a mythological framework for modeling and missing value imputation of mean, median, and interpolation while combining meteorological data set from NASA and Power demand datasets from Distribution companies, an approach for improved short-term forecasting accuracy. This research considered Linear machine learning algorithms which include Linear regression, Lasso Linear Regression, Elastic Linear Regression, and nonlinear machine learning algorithms which are Support Vector Machine, Random Forest, Decision Tree, and K- Nearest Neighbor and deep learning algorithms like Artificial Neural Networks and Long Short Term Memory. Among these algorithms, the Deep learning method outperformed all of them with a performance accuracy of above 82.2% for ANN and with 91.2% performance which is prone to overfitting. Hyperparameter tuning with a batch size of 50 and an epoch of 5 gives the best performance for the deep learning methods.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

High Precision Modeling of Power Demand for Electricity Distribution Companies with frequent load shedding

Oladeji

Fasogbon

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The range of the actual data is between 2000 and 2012, and the OLS approach showed that industrial energy consumption will reach about 1660.13 MW in 2030, 11494.83 MW for residential, 6421.09 MW for commercial and 19576.05 MW for total energy use by 2030, respectively. Similarly, Idoniboyeobu et al (2018) modelled energy demand for Nigeria by breaking down energy demand following Ezennaya et al (2014). However, the study applied exponential regression to predict energy demand, and the result showed that by 2032, residential energy demand will be 11815.66MW, 6655.06MW for commercial, 1665.48MW for industrial and 201364.41MW for total energy demand by 2032, respectively.…”

Section: Empirical Reviewmentioning

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

“…The Harvey Logistic model is based on the Logistic model. From equation (10), the Harvey Logistic model is:…”

Section: Logistic Modelmentioning

confidence: 99%

“…Results showed that ANN-model performed better than the regression model for load forecasting. In [10], a study on long term electric load forecasting on the Nigerian power system using the modified form of the exponential regression model was carried out. The model was used to predict residential, commercial, and industrial load demand.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Stochastic Models for Forecasting Electricity Generation and Consumption in Nigeria

Nkemnole

Akinola

2020

PoS

View full text Add to dashboard Cite

With energy serious shortage of the Nigerian Power Sector owing to industry deregulation, abrupt variations in electricity demand, and increasing population density, Nigeria's economic development has been restricted. Thus, it is significant to balance the relationship between power generation and consumption, and further stabilize the two in a reasonable scope. To achieve balance, an accurate model to fit and predict electricity generation and consumption in Nigeria is required. This study, therefore, proposes a comparative study on stochastic modeling; (Harvey model, Autoregressive model, and Markov chain model) for forecasting electricity generation and consumption in Nigeria. The data gathered were analyzed and the model parameters were estimated using the maximum likelihood estimation technique. The comparative performance revealed that the Markov chain model best-predicted electricity generation than the Harvey and Autoregressive models. Also, for electricity consumption, results showed that the Harvey model predicted best than the Markov and Autoregressive models for electricity consumption. Thus, the Markov and Harvey model used to forecast electricity generation and consumption in Nigeria for the next 20 years (2018 to 2037) did not only reveal that electricity generation and consumption will continue to increase from 3,692.11 mln kW/h to 18,250.67 mln kW/h and from 2,961.10 mln kW/h to 127,071.30 mln kW/h respectively but also indicates high accuracy and the reference value of these models.

show abstract

Reactive Power Compensation Improvement Technique for Injection Substations in Nigeria

Cited by 5 publications

References 9 publications

High Precision Modeling of Power Demand for Electricity Distribution Companies with frequent load shedding

High Precision Modeling of Power Demand for Electricity Distribution Companies with frequent load shedding

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

A Comparative Study of Stochastic Models for Forecasting Electricity Generation and Consumption in Nigeria

Contact Info

Product

Resources

About