A Data-Driven Model to Forecast Multi-Step Ahead Time Series of Turkish Daily Electricity Load

Abstract

It is critical to maintain a balance between the supply and the demand for electricity because of its non-storable feature. For power-producing facilities and traders, an electrical load is a piece of fundamental and vital information to have, particularly in terms of production planning, daily operations, and unit obligations, among other things. This study offers a deep learning methodology to model and forecast multistep daily Turkish electricity loads using the data between 5 January 2015, and 26 December 2021. One major reason for the growing popularity of deep learning is the creation of new and creative deep neural network topologies and significant computational advancements. Long Short-Term Memory (LSTM), Gated Recurrent Network, and Convolutional Neural Network are trained and compared to forecast 1 day to 7 days ahead of daily electricity load. Three different performance metrics including coefficient of determination (R2), root mean squared error, and mean absolute error were used to evaluate the performance of the proposed algorithms. The forecasting results on the test set showed that the best performance is achieved by LSTM. The algorithm has an R2 of 0.94 for 1 day ahead forecast, and the metric decreases to 0.73 in 7 days ahead forecast.