|Autors: Stošovic M. A., Radivojevic N., Ivanova, M. S.|
Title: Electricity Consumption Prediction in an Electronic System Using Artificial Neural Networks
Keywords: artificial neural networks; gated recurrent unit; energy consumption; electronic system; long short-term memory; prediction; time series
Abstract: In this paper, a method for electricity consumption prediction based on artificial neural networks is proposed. The data obtained are measured for a period of over 2 years and then separated to four seasons, so different models are developed for each season. Five different network structures (ordinary RNN, LSTM, GRU, bidirectional LSTM, bidirectional GRU) for five different values of horizon, i.e., input data (one day, two days, four days, one week, two weeks) are examined. Performance indices, such as mean absolute percentage error (MAPE), root mean square error (RMSE), mean absolute error (MAE) and mean square error (MSE), are used in order to obtain qualitative and quantitative comparisons among the obtained models. The results show that the modifications of recurrent neural networks perform much better than ordinary recurrent neural networks. GRU and LSTMB structures with horizons of 168h and 336h are found to have the best performances.
1. Starace G, Tiwari A, Colangelo G, Massaro A. Advanced Data Systems for Energy Consumption Optimization and Air Quality Control in Smart Public Buildings Using a Versatile Open Source Approach. Electronics. 2022; 11(23):3904. https://doi.org/10.3390/electronics11233904 - 2022 - в издания, индексирани в Scopus или Web of Science
Вид: статия в списание, публикация в издание с импакт фактор, публикация в реферирано издание, индексирана в Scopus