Intelligence based Accurate Medium and Long Term Load Forecasting System

In this study, we aim to provide an efficient load prediction system projected for different local feeders to predict the Medium- and Long-term Load Forecasting. This model improves future requirements for expansions, equipment retailing or staff recruiting to the electric utility company. We aimed to improve ahead forecasting by using hybrid approach and optimizing the parameters of our models. We used Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Multilayer perceptron (MLP) and hybrid methods. We used Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE) and squared error for comparison. To predict the 3 months ahead load forecasting, the lowermost prediction error was acquired using LSTM MAPE (2.70). For 6 months ahead forecasting prediction, MLP gives highest predictions with MAPE (2.36). Moreover, to predict the 9 months ahead load forecasting, the highest prediction has been attained using LSTM in terms of MAPE (2.37). Likewise, ahead 1 years MAPE (2.25) was yielded using LSTM and ahead six years MAPE (2.49) was provided using MLP. The proposed methods attain stable and better performance for prediction of load forecasting. The finding indicates that this model can be better instigated for future expansion requirements.