Yıl: 2022 Cilt: 30 Sayı: 1 Sayfa Aralığı: 140 - 157 Metin Dili: İngilizce DOI: 10.3906/elk-2011-14 İndeks Tarihi: 30-06-2022

Application of long short-term memory (LSTM) neural network based on deep learning for electricity energy consumption forecasting

Öz:
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.
Anahtar Kelime:

Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık
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APA BILGILI M, ARSLAN N, SEKERTEKIN A, YASAR A (2022). Application of long short-term memory (LSTM) neural network based on deep learning for electricity energy consumption forecasting. , 140 - 157. 10.3906/elk-2011-14
Chicago BILGILI Mehmet,ARSLAN Niyazi,SEKERTEKIN Aliihsan,YASAR Abdulkadir Application of long short-term memory (LSTM) neural network based on deep learning for electricity energy consumption forecasting. (2022): 140 - 157. 10.3906/elk-2011-14
MLA BILGILI Mehmet,ARSLAN Niyazi,SEKERTEKIN Aliihsan,YASAR Abdulkadir Application of long short-term memory (LSTM) neural network based on deep learning for electricity energy consumption forecasting. , 2022, ss.140 - 157. 10.3906/elk-2011-14
AMA BILGILI M,ARSLAN N,SEKERTEKIN A,YASAR A Application of long short-term memory (LSTM) neural network based on deep learning for electricity energy consumption forecasting. . 2022; 140 - 157. 10.3906/elk-2011-14
Vancouver BILGILI M,ARSLAN N,SEKERTEKIN A,YASAR A Application of long short-term memory (LSTM) neural network based on deep learning for electricity energy consumption forecasting. . 2022; 140 - 157. 10.3906/elk-2011-14
IEEE BILGILI M,ARSLAN N,SEKERTEKIN A,YASAR A "Application of long short-term memory (LSTM) neural network based on deep learning for electricity energy consumption forecasting." , ss.140 - 157, 2022. 10.3906/elk-2011-14
ISNAD BILGILI, Mehmet vd. "Application of long short-term memory (LSTM) neural network based on deep learning for electricity energy consumption forecasting". (2022), 140-157. https://doi.org/10.3906/elk-2011-14
APA BILGILI M, ARSLAN N, SEKERTEKIN A, YASAR A (2022). Application of long short-term memory (LSTM) neural network based on deep learning for electricity energy consumption forecasting. Turkish Journal of Electrical Engineering and Computer Sciences, 30(1), 140 - 157. 10.3906/elk-2011-14
Chicago BILGILI Mehmet,ARSLAN Niyazi,SEKERTEKIN Aliihsan,YASAR Abdulkadir Application of long short-term memory (LSTM) neural network based on deep learning for electricity energy consumption forecasting. Turkish Journal of Electrical Engineering and Computer Sciences 30, no.1 (2022): 140 - 157. 10.3906/elk-2011-14
MLA BILGILI Mehmet,ARSLAN Niyazi,SEKERTEKIN Aliihsan,YASAR Abdulkadir Application of long short-term memory (LSTM) neural network based on deep learning for electricity energy consumption forecasting. Turkish Journal of Electrical Engineering and Computer Sciences, vol.30, no.1, 2022, ss.140 - 157. 10.3906/elk-2011-14
AMA BILGILI M,ARSLAN N,SEKERTEKIN A,YASAR A Application of long short-term memory (LSTM) neural network based on deep learning for electricity energy consumption forecasting. Turkish Journal of Electrical Engineering and Computer Sciences. 2022; 30(1): 140 - 157. 10.3906/elk-2011-14
Vancouver BILGILI M,ARSLAN N,SEKERTEKIN A,YASAR A Application of long short-term memory (LSTM) neural network based on deep learning for electricity energy consumption forecasting. Turkish Journal of Electrical Engineering and Computer Sciences. 2022; 30(1): 140 - 157. 10.3906/elk-2011-14
IEEE BILGILI M,ARSLAN N,SEKERTEKIN A,YASAR A "Application of long short-term memory (LSTM) neural network based on deep learning for electricity energy consumption forecasting." Turkish Journal of Electrical Engineering and Computer Sciences, 30, ss.140 - 157, 2022. 10.3906/elk-2011-14
ISNAD BILGILI, Mehmet vd. "Application of long short-term memory (LSTM) neural network based on deep learning for electricity energy consumption forecasting". Turkish Journal of Electrical Engineering and Computer Sciences 30/1 (2022), 140-157. https://doi.org/10.3906/elk-2011-14