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A forecasting model approach of sustainable electricity management by developing adaptive neuro-fuzzy inference system

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Abstract

With an exponential industrial growth, an accurate demand forecasting of energy is of prime importance for strategic decision-making and new power policies regarding generation and distribution in the power sector. This is a great impediment in economic development as well as shattering people’s daily life. Hence, forecasting of energy demand in emerging markets is one of the most important policy tool used by decision-makers all over the world. This study focused on the forecasting approach of electricity consumption in Pakistan by developing a model that is called ANFIS (Adaptive neuro-fuzzy inference system). A framework was developed comprising economic and demographic variables as input. Previous historical data of GDP, population, industry efficiency, and weather (annual average temperature) was collected as input to the model and electricity consumption as output of the model. By developing ANFIS model, forecasting was done up to 2045. The increasing trends with respect to predictors showed significant association with electricity consumption. The overall least error proved this model best for forecasting and planning electricity demand to achieve sustainability in the power sector.

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Abbreviations

ANFIS:

Adaptive neuro-fuzzy inference system

ANN:

Artificial neural network

ARCH:

Autoregressive conditional heteroscedasticity

ARDL:

Auto regressive distributed lag

ARIMA:

Autoregressive integrated moving average

FALCON :

Fuzzy adaptive learning control system

FBPN:

Fuzzy back-propagation network

FHRCNNs:

Fuzzy hyper-rectangular composite neural networks

FUNN:

Fuzzy neural network

GARCH:

Generalized autoregressive conditional heteroscedasticity

GDP:

Gross domestic product

GWh:

Gigawatt hour

HDIP:

Human development index Pakistan

KESC:

Karachi electric supply corporation

LEAP :

Long-range energy alternative planning system

MAPE:

Mean absolute percentage error

MSPE:

Mean squared percentage error

MSE:

Mean squared error

RE:

Relative error

SARIMA:

Seasonal autoregressive integrated moving average

WAPDA:

Water and power development authority

WDI:

World bank indicators

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Authors and Affiliations

  1. Department of Management Sciences, COMSATS University Islamabad, Lahore Campus, Defense Road, Off Raiwind Road, Lahore, Pakistan

    Aamir Nawaz Khan & Muhammad Asif Nadeem

  2. Department of Chemistry, University of Sargodha Sub Campus Bhakkar, Punjab, Pakistan

    Muhammad Shahid Hussain

  3. Department of Chemical Engineering, COMSATS University Islamabad, Lahore Campus, Defense Road, Off Raiwind Road, Lahore, Pakistan

    Muhammad Aslam & Aqeel Ahmed Bazmi

  4. Process and Energy Systems Engineering Center-PRESTIGE, Department of Chemical Engineering, COMSATS University Islamabad (CUI), Lahore Campus, Defense Road, Off Raiwind Road, Lahore, Pakistan

    Aqeel Ahmed Bazmi

Authors
  1. Aamir Nawaz Khan
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  2. Muhammad Asif Nadeem
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  3. Muhammad Shahid Hussain
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  4. Muhammad Aslam
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  5. Aqeel Ahmed Bazmi
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Correspondence to Aqeel Ahmed Bazmi.

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Khan, A.N., Nadeem, M.A., Hussain, M.S. et al. A forecasting model approach of sustainable electricity management by developing adaptive neuro-fuzzy inference system. Environ Sci Pollut Res 27, 17607–17618 (2020). https://doi.org/10.1007/s11356-019-06626-5

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  • DOI: https://doi.org/10.1007/s11356-019-06626-5

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