Electricity consumption forecast for Tarkwa using autoregressive integrated moving average and adaptive neuro fuzzy inference system

Agyare, Robert Ofosu; Odoi, Benjamin; Asamoah, Mercy

doi:10.2298/sjee2101075o

Cited by 3 publications

(1 citation statement)

References 7 publications

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“…There are various performance indices that can be used to measure the forecasting performance of a model. In this study, we use performance indicators or error metrics such as ,mean absolute error(MAE), the root-mean squared error(RMSE), and mean absolute percentage error(MAPE) [23,24] given respectively by…”

Section: Model Evaluationmentioning

confidence: 99%

Predicting Petrol and Diesel Prices in Ghana, A Comparison of ARIMA and SARIMA Models

Agyare,

Odoi,

Wiah

2024

AJEBA

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Predicting prices is of great concern and important in the world of economics and finance. In this paper, a comparative analysis of gasoline and diesel in Ghana were analysed using Autoregressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving Average (SARIMA). Based on their forecasting accuracy, the best model was used for predicting future prices of gasoline and diesel from January 2024 to December 2024. A monthly data for the prices of gasoline and diesel spanning from January 2016 to December 2023 taken from the Bank of Ghana (BoG) and the National Petroleum Authority (NPA) was used for the analysis. ARIMA (0; 1; 2) and ARIMA (2; 1; 3) were identified as the best models for gasoline and diesel respectively, SARIMA(0; 1; 2) x (0; 1; 1)12 and SARIMA (1; 1; 1) x (0; 1; 1)12 were also identified after taking a seasonal difference of the series all based on AIC and BIC. The coefficient of the identified models were tested for its significance using the Z-test. The ARIMA and the SARIMA models were compared using RMSE, MAE, and MAPE. The SARIMA models generally performed better than the ARIMA models for both gasoline and diesel except RMSE for diesel where the ARIMA model was slightly better than the SARIMA models with values of 0:9677988 and 1:011531 respectively. The model evaluation proved that the SARIMA models for both gasoline and diesel were superior to the ARIMA and showed that, the SARIMA model is adequate and appropriate for forecasting of prices of gasoline and diesel prices in Ghana.

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Section: Model Evaluationmentioning

confidence: 99%

Predicting Petrol and Diesel Prices in Ghana, A Comparison of ARIMA and SARIMA Models

Agyare,

Odoi,

Wiah

2024

AJEBA

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MPPT algorithm based on metaheuristic techniques (PSO & GA) dedicated to improve wind energy water pumping system performance

Bouchakour,

Zarour,

Bessous

et al. 2024

Sci Rep

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This paper presents a comparative study between four techniques recently used to improve the wind energy conversion system (WECS) to water pumping systems. The WECS is a renewable energy source which has developed rapidly in recent years. The use of the WECS in the water pumping field is a free solution (economically) compared to the use of the electricity grid supply. The control of WECS, equipped with a permanent magnet synchronous generator, has the objective of carefully maximising power generation. A comparative study between the proposed Fuzzy Logic Control, optimised using a genetic algorithm and particle swarm optimisation algorithm, and the conventional Perturb and Observe MPPT method using Matlab/Simulink, is presented. The performance of the proposed system has been verified against the generated output voltage, current and power waveforms, intermediate circuit voltage waveform, and generator speed. The presented results demonstrate the effectiveness of the control strategy applied in this work.

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A Comparative Analysis of Support Vector Machine and Decision Tree Algorithm for Predicting Fault in Uninterruptible Power Supply Systems

Doe,

Annan,

Odoi

2024

IJITEE

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Power supply systems can have problems, and Ghana Gas Limited is not an exception. Ghana Gas Limited uses an intricate Uninterruptible Power Supply (UPS) system which is made up of several parts such as electromechanical components, PCB boards, and electrolytic capacitors. The majority of components have technical lifespans that are governed by usage, operational environment, and working conditions, such as electrical stress, working hours, and working cycles. Most of the time, these errors affect the integrity and power supply after manufacture. The issue is that it takes longer for the professionals who operate on this machine to recognize these flaws, which makes it difficult for them to predict errors quickly or anticipate the likelihood of faults happening in the system components at an early stage for effective corrective action to be performed. Support vector machines (SVM) and decision trees were used in this study to anticipate faults for technical data scheduling of uninterruptible power supply systems for Ghana Gas Limited in an efficient manner. Based on a comparative analysis using these two techniques, faults in Ghana Gas Limited's power supply system were predicted using a four-hour daily interval dataset on UPS recordings, including input voltage, battery voltage, battery current, and alarm, spanning from August 2017 to October 2023. The findings depicted that the support vector machine was more efficient in detecting the fault locations in the power supply system with an accuracy of 96.80%, recall of 99.80%, precision of 100 %, F1-score of 93.15%. The results from the error metrics also validate the measures in assessing the predictive ability of the model with MAE of 0.42%, MSE of 1.18%, RMSE of 4.45%, R2 of 99.97%, RMSLE of 0.036%, and MAPE of 0.21%.

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Electricity consumption forecast for Tarkwa using autoregressive integrated moving average and adaptive neuro fuzzy inference system

Cited by 3 publications

References 7 publications

Predicting Petrol and Diesel Prices in Ghana, A Comparison of ARIMA and SARIMA Models

Predicting Petrol and Diesel Prices in Ghana, A Comparison of ARIMA and SARIMA Models

MPPT algorithm based on metaheuristic techniques (PSO & GA) dedicated to improve wind energy water pumping system performance

A Comparative Analysis of Support Vector Machine and Decision Tree Algorithm for Predicting Fault in Uninterruptible Power Supply Systems

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