Performance Analysis of Machine Learning and ANN for Load Forecasting in Bangladesh

Electrical load forecasting is essential for energy suppliers and policymakers to operate and develop power plants and provide stable and dependable energy infrastructure in Bangladesh. Forecasting loads come in three different forms: short-, mid-, and long-term. Of these, forecasting short-term load keeps track of activities on a daily, weekly, and hourly basis, which is becoming more popular since it reduces time and costs. While designing and implementing the electric power system, mid-term forecasting of the load is a critical stage in organizing, maintaining, and preparing for interruptions. On the basis of the Pearson correlation matrix, Machine learning models like support vector regression(SVR) and linear regression(LR), and deep learning, i.e., multilayer perceptron (MLP) approaches presented in this paper. The recommended approaches are evaluated and compared depending on accuracy, mean absolute error (MAE), mean squared percentage error (MAPE), and root mean squared error(RMSE), using a dataset from Power Grid Company of Bangladesh Ltd. (PGCB). Forecasting results show that MLP for both cases exhibits the best accuracy of 98.947% and 99.217% with the least RMSE, MAPE, and MAE error rates in comparison with other models.