Demand-Supply Forecasting based on Deep Learning for Electricity Balance in Cameroon
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Keywords:Forecasting, Long Short-term Memory, Electricity Production and Consumption
AbstractElectricity 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.
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How to Cite
Mbasso, W. F., Molu, R. J. J., Naoussi, S. R. D., & Kenfack, S. (2022). Demand-Supply Forecasting based on Deep Learning for Electricity Balance in Cameroon. International Journal of Energy Economics and Policy, 12(4), 99–103. https://doi.org/10.32479/ijeep.13092