Optimizing Parameters of Artificial Intelligence Deep Convolutional Neural Networks (CNN) to improve Prediction Performance of Load Forecasting System

Abstract

Load Forecasting is an approach that is implemented to foresee the future load demand projected on some physical parameters such as loading on lines, temperature, losses, pressure, and weather conditions etc. This study is specifically aimed to optimize the parameters of deep convolutional neural networks (CNN) to improve the short-term load forecasting (STLF) and Medium-term load forecasting (MTLF) i.e. one day, one week, one month and three months. The models were tested based on the real-world case by conducting detailed experiments to validate their stability and practicality. The performance was measured in terms of squared error, Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE). We optimized the parameters using three different cases. In first case, we used single layer with Rectified Linear Unit (ReLU) activation function. In the second case, we used double layer with ReLU - ReLU activation function. In the third case, we used double layer with ReLU - Sigmoid activation function. The number of neurons in each case were 2, 4, 6, 8, 10 and 12. To predict the one day ahead load forecasting, the lowest prediction error was yielded using double layer with ReLU - Sigmoid activation function. To predict ahead one-week load forecasting demands, the lowest error was obtained using single layer ReLU activation function. Likewise, to predict the one month ahead forecasting using double layer with ReLU - Sigmoid activation function. Moreover, to predict ahead three months forecasting using double layer ReLU - Sigmoid activation function produced lowest prediction error. The results reveal that by optimizing the parameters further improved the ahead prediction performance. The results also show that predicting nonstationary and nonlinear dynamics of ahead forecasting require more complex activation function and number of neurons. The results can be very useful in real-time implementation of this model to meet load demands and for further planning.