Study of Mid Term Load Forecasting Using Artificial Neural Networks (ANN) Algorithm in Sihanoukville

Mid-term load forecasting (MTLF) is crucial for power transmission system planning, safe operation, and
maintenance. It’s very significant in a developing country like Cambodia. In the past decade, many kinds of research have been developed for MLTF. However, the specific research of MLTF for Sihanoukville’s transmission system is still insufficient to meet the optimal planning for the rapid growth of the demand. This is the reason that require more research work to be contributed to this field. This paper presents an attempt at a medium-term forecast for the trans-mission system load of the Electricité Du Cambodge (EDC) company in Sihanoukville with the monthly index of historical data from January to June in 2023, Using a Artificial Neural Networks (ANN) The other variables such as dates, months of the years (January to June in 2023), relative humidity, wet-bulb temperature, and total non-working days of eachmonth also were taken into account to train with the ANN model. The Mean Absolute Error (MAE) and the Mean Absolute Percentage Error (MAPE) is used as the indicator to evaluate the performance of the simulated data from models to the actual data. The obtained results have shown that the MAPE of ANN and actual load were 10.4512 MW percent and The Mean Absolute Error (MAE) performed 4.7111 percent.