TY  - JOUR
TI  - Application of long short-term memory (LSTM) neural network based on deep
learning for electricity energy consumption forecasting
AB  - Electricity is the most substantial energy form that significantly affects the development of modern life,
work efficiency, quality of life, production, and competitiveness of the society in the ever-growing global world. In this
respect, forecasting accurate electricity energy consumption (EEC) is fairly essential for any country’s energy consumption
planning and management regarding its growth. In this study, four time-series methods; long short-term memory (LSTM)
neural network, adaptive neuro-fuzzy inference system (ANFIS) with subtractive clustering (SC), ANFIS with fuzzy cmeans (FCM), and ANFIS with grid partition (GP) were implemented for the short-term one-day ahead EEC prediction.
Root mean square error (RMSE), correlation coefficient (R), mean absolute error (MAE) and mean absolute percentage
error (MAPE) were considered as statistical accuracy criteria. Those forecasted results by the LSTM, ANFIS-FCM,
ANFIS-SC and ANFIS-GP models were evaluated by comparing with the actual data using statistical accuracy metrics.
According to the testing process, the best MAPE values were obtained to be 4.47%, 3.21%, 2.34%, and 1.91% for the
ANFIS-GP, ANFIS-SC, ANFIS-FCM, and LSTM, respectively. Furthermore, the best RMSE values were found as
25.94 GWh, 41.17 GWh, 29.50 GWh, and 80.14 GWh for the LSTM, ANFIS-SC, ANFIS-FCM, and ANFIS-GP models,
respectively. As a consequence, the LSTM model generally outperformed all ANFIS models. The results revealed that
forecasting of short-term daily EEC time series using the LSTM approach can provide high accuracy results.
AU  - ARSLAN, Niyazi
AU  - YASAR, Abdulkadir
AU  - BILGILI, Mehmet
AU  - SEKERTEKIN, Aliihsan
DO  - 10.3906/elk-2011-14
PY  - 2022
JO  - Turkish Journal of Electrical Engineering and Computer Sciences
VL  - 30
IS  - 1
SN  - 1300-0632
SP  - 140
EP  - 157
DB  - TRDizin
UR  - http://search/yayin/detay/527761
ER  -