Prediction of Energy Consumption of Turkey on Sectoral Bases by Arima Model

Öztürk, Suat; Öztürk, Feride

doi:10.18488/journal.82.2018.51.23.30

Cited by 6 publications

(1 citation statement)

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“…Jain and Al. [24] used ARIMA to predict electricity consumption. They concluded that the model performed well, achieving a MAPE of 6.63%.…”

Section: Introductionmentioning

confidence: 99%

Long Term Forecasting of Peak Demand and Annual Electricity Consumption of the West African Power Pool Interconnected Network by 2032

Prodjinotho,

Chetangny,

Agbomahena

et al. 2024

IJEPE

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The uneven distribution of primary sources of electric power generation in Economic Community of West African States (ECOWAS) compelled the heads of states to create the West African Power Pool (WAPP). The vision of this system is to set up a common electrical energy market to satisfy the balance between supply and demand at an affordable price using the interconnected network. Forecasting maximum power demand and energy consumption is essential for planning and the coordination of new power plant and transmission lines building. This work consists of predicting maximum power demand and total energy that must transit through the WAPP interconnected network by the year 2032. We compare the performances of three time series models namely the Long Short-Term Memory (LSTM), Auto-Regressive Integrated Moving Average (ARIMA) and Fb Facebook Prophet. Electric power and energy data used for training the systems comes from the WAPP authorties. The results show that, for monthly peaks, the Facebook (Fb) Prophet model is the best, with a MAPE (mean absolute error percentage) of 3.1% and a low RMSE (root mean square error) of 1.225 GW. For energy prediction, ARIMA performances are the best compared to others with (RMSE 1.20 TWh, MAPE 1.00%). Thus, the forecast for total annual energy consumption and annual peak demand will be, respectively, 96.85TWh and 13.6 GW in 2032.

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“…Jain and Al. [24] used ARIMA to predict electricity consumption. They concluded that the model performed well, achieving a MAPE of 6.63%.…”

Section: Introductionmentioning

confidence: 99%