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Demand-Supply Forecasting based on Deep Learning for Electricity Balance in Cameroon

Author

Listed:
  • Wulfran Fendzi Mbasso

    (Laboratory of Technology and Applied Sciences, University Institute of Technology of Douala, University of Douala, Douala, Cameroon.)

  • Reagan Jean Jacques Molu

    (Laboratory of Technology and Applied Sciences, University Institute of Technology of Douala, University of Douala, Douala, Cameroon.)

  • Serge Raoul Dzonde Naoussi

    (Laboratory of Technology and Applied Sciences, University Institute of Technology of Douala, University of Douala, Douala, Cameroon.)

  • Saatong Kenfack

    (Laboratory of Technology and Applied Sciences, University Institute of Technology of Douala, University of Douala, Douala, Cameroon.)

Abstract
Electricity is becoming an important commodity in Cameroon. Within the years, its consumption and production have led to many studies. Hence, having an idea on its progression is one of research concerns. Thus, this paper aims to develop a model for forecasting electricity production and consumption in Cameroon based on Long Short-Term Memory (LSTM). Indeed, the LSTM approach, showing a good ability to grab the long-term dependencies between time steps of electricity production and consumption, allows a good prediction in 2030 of 7178GWh for consumption with 0.067 RMSE and 0.2965% MAPE and 8686GWh for production with 0.1631 RMSE and 0.4291%MAPE. Hence, the proposed model is more reliable, what makes possible to monitor the growth in electricity supply and demand, falling to the study of balance in Cameroon.

Suggested Citation

  • Wulfran Fendzi Mbasso & Reagan Jean Jacques Molu & Serge Raoul Dzonde Naoussi & Saatong Kenfack, 2022. "Demand-Supply Forecasting based on Deep Learning for Electricity Balance in Cameroon," International Journal of Energy Economics and Policy, Econjournals, vol. 12(4), pages 99-103, July.
    • Handle: RePEc:eco:journ2:2022-04-13
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    References listed on IDEAS

    as
    1. Deb, Chirag & Zhang, Fan & Yang, Junjing & Lee, Siew Eang & Shah, Kwok Wei, 2017. "A review on time series forecasting techniques for building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 902-924.
    2. Kaboli, S. Hr. Aghay & Fallahpour, A. & Selvaraj, J. & Rahim, N.A., 2017. "Long-term electrical energy consumption formulating and forecasting via optimized gene expression programming," Energy, Elsevier, vol. 126(C), pages 144-164.
    3. Szoplik, Jolanta, 2015. "Forecasting of natural gas consumption with artificial neural networks," Energy, Elsevier, vol. 85(C), pages 208-220.
    4. Ghulam Hafeez & Khurram Saleem Alimgeer & Zahid Wadud & Zeeshan Shafiq & Mohammad Usman Ali Khan & Imran Khan & Farrukh Aslam Khan & Abdelouahid Derhab, 2020. "A Novel Accurate and Fast Converging Deep Learning-Based Model for Electrical Energy Consumption Forecasting in a Smart Grid," Energies, MDPI, vol. 13(9), pages 1-25, May.
    5. Jihoon Moon & Yongsung Kim & Minjae Son & Eenjun Hwang, 2018. "Hybrid Short-Term Load Forecasting Scheme Using Random Forest and Multilayer Perceptron," Energies, MDPI, vol. 11(12), pages 1-20, November.
    6. Felix Ghislain Yem Souhe & Camille Franklin Mbey & Alexandre Teplaira Boum & Pierre Ele, 2021. "Forecasting of Electrical Energy Consumption of Households in a Smart Grid," International Journal of Energy Economics and Policy, Econjournals, vol. 11(6), pages 221-233.
    7. Pruethsan Sutthichaimethee & Harlida Abdul Wahab, 2021. "A Forecasting Model in Managing Future Scenarios to Achieve the Sustainable Development Goals of Thailand s Environmental Law: Enriching the Path Analysis-VARIMA-OVi Model," International Journal of Energy Economics and Policy, Econjournals, vol. 11(4), pages 398-411.
    8. Maher AbuBaker, 2021. "Household Electricity Load Forecasting Toward Demand Response Program Using Data Mining Techniques in A Traditional Power Grid," International Journal of Energy Economics and Policy, Econjournals, vol. 11(4), pages 132-148.
    9. Yuan, Chaoqing & Liu, Sifeng & Fang, Zhigeng, 2016. "Comparison of China's primary energy consumption forecasting by using ARIMA (the autoregressive integrated moving average) model and GM(1,1) model," Energy, Elsevier, vol. 100(C), pages 384-390.
    10. Muhammad Mutasim Billah Tufail & Mohd Nasrun Mohd Nawi & Akhtiar Ali & Faizal Baharum & Mohamad Zamhari Tahir & Anas Abdelsatar Mohammad Salameh, 2021. "Forecasting Impact of Demand Side Management on Malaysia s Power Generation using System Dynamic Approach," International Journal of Energy Economics and Policy, Econjournals, vol. 11(4), pages 412-418.
    11. Xu, Weijun & Gu, Ren & Liu, Youzhu & Dai, Yongwu, 2015. "Forecasting energy consumption using a new GM–ARMA model based on HP filter: The case of Guangdong Province of China," Economic Modelling, Elsevier, vol. 45(C), pages 127-135.
    12. Chaido Dritsaki & Dimitrios Niklis & Pavlos Stamatiou, 2021. "Oil Consumption Forecasting using ARIMA Models: An Empirical Study for Greece," International Journal of Energy Economics and Policy, Econjournals, vol. 11(4), pages 214-224.
    13. Sawle, Yashwant & Gupta, S.C. & Bohre, Aashish Kumar, 2018. "Socio-techno-economic design of hybrid renewable energy system using optimization techniques," Renewable Energy, Elsevier, vol. 119(C), pages 459-472.
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    More about this item

    Keywords

    Forecasting; Long Short-term Memory; Electricity Production and Consumption;
    All these keywords.

    JEL classification:

    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy

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