Statistical Model for the Forecast of Electricity Power Generation in Ghana

Wiah, Eric Neebo; Buabeng, Albert; Agyarko, Kofi

doi:10.4236/ojs.2022.123024

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…Analysis of this projection becomes the basis for long-term planning [10]. On the other hand, there is also a projection model using the SARIMA model such as research related to predicting power generation in Ghana [11]. Furthermore, research related to projection analysis using the artificial neural network method has also been carried out which allows for a more efficient reduction of electricity costs in the short term [7].…”

Section: Introductionmentioning

confidence: 99%

Correlation and projection analysis between total population and total electrical energy consumption in Province of Bangka Belitung Island

Sulistiana

Dalimunthe

Saputra

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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This study aims to analyze the projected total population and its correlation to the level of electricity consumption in the Province of Bangka Belitung Islands. The correlation of total population to the level of electrical energy consumption and its projections are very important as basic information in designing sustainable regional development, especially in the energy and economic sectors. The time series data used in this study starts from 2007 and ends in 2021 with a projected value starting from 2022 to 2030. The projection method used is the exponential smoothing method with the results of model fitting that the Holt’s Exponential Smoothing model is the best model with the smallest Root Mean Square Error (RMSE), R Squared and Mean Absolute Percentage Error (MAPE) values among other models, namely 15381,641 for the RMSE value, 0.979 for R Squared value and 0.936 for the MAPE value. The projection results obtained that the Total Population and Total Electrical Energy Consumption variables in the Province of Bangka Belitung Islands have a significant increase namely 0.20% for total percentage increase and there was a strong correlation between the two variables with a significance value of less than 0.05 and a Pearson Correlation value of 0,792.

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Section: Introductionmentioning

confidence: 99%

Correlation and projection analysis between total population and total electrical energy consumption in Province of Bangka Belitung Island

Sulistiana

Dalimunthe

Saputra

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

View full text Add to dashboard Cite

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Forecasting the National Health Insurance Fund Membership Enrolment in Tanzania Using the SARIMA Model

Tembo,

Ilembo,

Lwaho

2024

SCIMUNDI

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This paper aimed at forecasting membership enrolment in the National Health Insurance Fund (NHIF) in Tanzania using quarterly time series data. This study used 88 time series data to fit the seasonal Autoregressive Integrated Moving Average model (SARIMA). ARIMA (3,1,1) (0,1,0)[4] model was built and used for forecasting. The results show that there will be an increasing membership enrolment overtime over the years and no signs of decreasing. Thus, the government, apart from continuing subsidizing the cost of accessing health insurance services, should also improve the National Health Insurance (NHI) coverage to accommodate the increased enrolment and discourage dropouts. In turn, this will help to achieve the Universal Health Coverage (UHC) ultimate aim of ensuring equitable access to essential and manageable healthcare services, regardless of individuals’ financial situations, their location, and personality.

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Statistical Model for the Forecast of Electricity Power Generation in Ghana

Cited by 2 publications

References 9 publications

Correlation and projection analysis between total population and total electrical energy consumption in Province of Bangka Belitung Island

Correlation and projection analysis between total population and total electrical energy consumption in Province of Bangka Belitung Island

Forecasting the National Health Insurance Fund Membership Enrolment in Tanzania Using the SARIMA Model

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