TY - GEN
T1 - Forecasting Fuel Supply Inventory in Remote Areas using Deep Learning Approaches
AU - Raharjo, Agus Budi
AU - Purwitasari, Diana
AU - Dzhalila, Dzhillan
AU - Rachmadi, Hilmi Zharfan
AU - Diani, Nabila A.Idah
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Electricity is an essential commodity, yet power disruptions remain a critical challenge in remote regions. This research addresses the need for preemptive management of fuel supply for power plants in remote areas to prevent blackouts. The study evaluates the efficacy of three deep learning approaches: Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and a combined CNN-LSTM approach. The results indicate that the CNN-LSTM method outperforms the others, demonstrating the lowest Root Mean Square Error (RMSE) and the highest R2 value. This finding suggests that the CNN-LSTM model is the most effective for forecasting and mitigating power outages in these regions. Implementing this method could enhance the reliability of electricity supply, thereby ensuring continuous and stable power services for communities in underserved areas.
AB - Electricity is an essential commodity, yet power disruptions remain a critical challenge in remote regions. This research addresses the need for preemptive management of fuel supply for power plants in remote areas to prevent blackouts. The study evaluates the efficacy of three deep learning approaches: Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and a combined CNN-LSTM approach. The results indicate that the CNN-LSTM method outperforms the others, demonstrating the lowest Root Mean Square Error (RMSE) and the highest R2 value. This finding suggests that the CNN-LSTM model is the most effective for forecasting and mitigating power outages in these regions. Implementing this method could enhance the reliability of electricity supply, thereby ensuring continuous and stable power services for communities in underserved areas.
KW - CNN-LSTM
KW - fuel forecasting
KW - remote areas
UR - http://www.scopus.com/inward/record.url?scp=85189940585&partnerID=8YFLogxK
U2 - 10.1109/IWAIIP58158.2023.10462733
DO - 10.1109/IWAIIP58158.2023.10462733
M3 - Conference contribution
AN - SCOPUS:85189940585
T3 - IWAIIP 2023 - Conference Proceeding: International Workshop on Artificial Intelligence and Image Processing
SP - 254
EP - 259
BT - IWAIIP 2023 - Conference Proceeding
A2 - Jusman, Yessi
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 International Workshop on Artificial Intelligence and Image Processing, IWAIIP 2023
Y2 - 1 December 2023 through 2 December 2023
ER -