Long Term Electricity Load Forecast Based on Machine Learning for Cameroon’s Power System

  •  Terence K. Lukong    
  •  Derick N. Tanyu    
  •  Thomas T. Tatietse    
  •  Detlef Schulz    


A reliable power supply has long been identified as an important economic growth parameter. Electricity load forecasts predict the future behavior of the electricity load. Carrying out a forecast is important for real-time dispatching of power, grid maintenance scheduling, grid expansion planning, and generation planning depending on the forecasting horizon. Most of the methods used in long-term load forecasting are regressions and are limited to predicting peak loads of a yearly or monthly resolution with low accuracy. In this paper, we propose a method based on long short-term memory-recurrent neural networks (LSTM-RNN) cells with relations between identified influential econometric load-driving parameters which includes: the Gross Domestic Product (GDP), Population (H), and past Electric Load Data. To the best of our knowledge, the use of the GDP and H as two additional independent variables in load forecast modelling using machine learning techniques is a novelty in Cameroon. A comparison was performed between a linear regression (LR)-based long-term load forecast model (a model currently used by the Transmission System Operator of Cameroon) and LSTM-RNNs model constructed. The results generated were evaluated using a Mean Absolute Percentage Error (MAPE) within the same period of evaluation, and the overall value of the MAPE obtained for LSTM-RNNs model was 5.4962 whereas that for the LR model was 7.5422. Based on these results, the LSTM-RNN model is considered highly accurate and competent. The model was used to generate a forecast for the period of 2022–2026 with an hourly resolution. A MAPE of 5.4962 was obtained with a computational time of approximately ten minutes, making the model vital for offline use by utilities due to its capacity to quantitatively and accurately predict long-term load with an hourly resolution.

This work is licensed under a Creative Commons Attribution 4.0 License.
  • ISSN(Print): 1927-0569
  • ISSN(Online): 1927-0577
  • Started: 2011
  • Frequency: semiannual

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