Multivariate time series prediction by RNN architectures for energy consumption forecasting

Abstract

Households and buildings have been utilizing the traditional electric network structure for the last decade, relying on energy supplied by manufacturing centers based on fossil fuels. Large energy use places a burden on such centers. In this perspective, smart grids are a new technology and a new generation of traditional electric networks that provide increased efficiency, dependability, and energy management based on demand optimization. The importance of smart grids can also be seen in the possibility of integrating communication systems for energy demand forecasting, to provide an optimal management of the combination of renewable energies and production centers energy. The authors present a comparative analysis of several deep learning models, notably Recurrent Neural Network (RNN) architectures such as basic RNN, Long Short Term Memory (LSTM), and Gated Recurrent Unit (GRU), in this paper. These architectures are trained and tested on the Smart Grid Smart City (SGSC) project’s energy datasets (2010–2014) and assessed using a variety of indicators such as Root Mean Square Error (RMSE), Mean Absolute Error MAE, and R2 scores in order to analyze, compare and ultimately choose the most efficient model. As expected from the literature of RNN architectures, with the lowest value of RMSE error and the highest value of R2 Score among the three architectures, GRU outperformed both of basic RNN and LSTM, this result can be explained by several reasons the most important one is the ability of the GRU model to deal with the vanishing gradient problem and the impact of the number of parameters, used in building such a model, on the same problem.

Keywords

• Deep Learning
• RNN architectures
• Energy forecasting
• Smart grids
• Time series data