Electric Load Forecasting with Deep Machine Learning

Abstract

The most commonly used form of energy in houses, factories, buildings and agriculture is the electrical energy, however, in recent years, there has been an increase in electrical energy demand due to technology advancements and rise in population, therefore an appropriated forecasting system must be developed to predict these demands as accurately as possible. For this purpose, five models were selected, they are Bidirectional-Long Short Term Memory (Bi-LSTM), Feed Forward Neural Network (FFNN), Long Short Term Memory (LSTM), Nonlinear Auto Regressive network with eXogenous inputs (NARX) and Multiple Linear Regression (MLR). This paper will demonstrate the development of these selected models using MATLAB and an android mobile application, which is used to visualize and interact with the data. The performance of the selected models was evaluated by performing the Mean Absolute Percent Error (MAPE), the selected historical data used to perform the MAPE was obtained from Toronto, Canada and Tasmania, Australia, where the year 2006 until 2016 was used as training data and the year 2017 was used to test the MAPE of the historical data with the models’ data. It is observed that the NARX model had the least MAPE for both the regions resulting in 1.9% for Toronto, Canada and 2.9% for Tasmania, Australia. Google cloud is used as the IoT (Internet of Things) platform for NARX data model, the 2017 datasets is converted to JavaScript Object Notation (JSON) file using JavaScript programming language, for data visualization and analysis for the android mobile application.