Prediction of Electricity Usage in The Food and Beverage Department Using Recurrent Neural Network
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Published:
Nov 29, 2021
Keywords:
Electricity Usage, Food and Beverage, Internet of Things, Prediction Modelling, RNN LSTM
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Computer Science
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Abstract
The Food and Beverage (F&B) department is one of the sources of income for the company. F&B uses a variety of equipment and machines with large enough power consumption to support operations. F&B can be a disadvantage because of the wasteful use of electrical energy. This research designs and builds an Internet of Things (IoT) prototype that can monitor electricity usage in electrical equipment using sensors then from the data sent by the sensor and additional data predictions are made. The electrical equipment studied included walk-in chillers, blower wheels, exhaust fans, freezers, dishwashers, water heaters and under chillers. To build IoT devices, Arduino nano, AC Current Module, SIM 800L and humidity and temperature sensors are used. Prediction model built using RNN LSTM. IoT devices have succeeded in sending data well after cloud architecture. With 8 neurons in LSTM with lookback has the best performance. The error values ??for the test data are 51,085 and 18,886 for RMSE and MAE.
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Lukman Aditya, Utomo, W. . ., Khumaidi, A., Hidayat, R. . . and Al jihad, H. . . (2021) “Prediction of Electricity Usage in The Food and Beverage Department Using Recurrent Neural Network”, Jurnal Mantik, 5(3), pp. 2020-2025. Available at: https://iocscience.org/ejournal/index.php/mantik/article/view/1823 (Accessed: 2May2026).
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[3] B. Li, J. Zhang, Y. He, and Y. Wang, “Short-Term Load-Forecasting Method Based on Wavelet Decomposition With Second-Order Gray Neural Network Model Combined With ADF Test,” IEEE Access, vol. 5, pp. 16324–16331, 2017, doi: 10.1109/ACCESS.2017.2738029.
[4] Y. Wang, Q. Chen, N. Zhang, and Y. Wang, “Conditional Residual Modeling for Probabilistic Load Forecasting,” IEEE Trans. Power Syst., vol. 33, no. 6, pp. 7327–7330, Nov. 2018, doi: 10.1109/TPWRS.2018.2868167.
[5] Y. Wang, Q. Chen, M. Sun, C. Kang, and Q. Xia, “An Ensemble Forecasting Method for the Aggregated Load With Subprofiles,” IEEE Trans. Smart Grid, vol. 9, no. 4, pp. 3906–3908, Jul. 2018, doi: 10.1109/TSG.2018.2807985.
[6] K. Chen, K. Chen, Q. Wang, Z. He, J. Hu, and J. He, “Short-Term Load Forecasting With Deep Residual Networks,” IEEE Trans. Smart Grid, vol. 10, no. 4, pp. 3943–3952, Jul. 2019, doi: 10.1109/TSG.2018.2844307.
[7] W. Kong, Z. Y. Dong, Y. Jia, D. J. Hill, Y. Xu, and Y. Zhang, “Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network,” IEEE Trans. Smart Grid, vol. 10, no. 1, pp. 841–851, Jan. 2019, doi: 10.1109/TSG.2017.2753802.
[8] K. Park, S. Yoon, and E. Hwang, “Hybrid Load Forecasting for Mixed-Use Complex Based on the Characteristic Load Decomposition by Pilot Signals,” IEEE Access, vol. 7, pp. 12297–12306, 2019, doi: 10.1109/ACCESS.2019.2892475.
[9] C. Feng, M. Sun, and J. Zhang, “Reinforced Deterministic and Probabilistic Load Forecasting via $Q$ -Learning Dynamic Model Selection,” IEEE Trans. Smart Grid, vol. 11, no. 2, pp. 1377–1386, Mar. 2020, doi: 10.1109/TSG.2019.2937338.
[10] Y. Hong, Y. Zhou, Q. Li, W. Xu, and X. Zheng, “A Deep Learning Method for Short-Term Residential Load Forecasting in Smart Grid,” IEEE Access, vol. 8, pp. 55785–55797, 2020, doi: 10.1109/ACCESS.2020.2981817.
[11] T. Ouyang, Y. He, H. Li, Z. Sun, and S. Baek, “Modeling and Forecasting Short-Term Power Load With Copula Model and Deep Belief Network,” IEEE Trans. Emerg. Top. Comput. Intell., vol. 3, no. 2, pp. 127–136, Apr. 2019, doi: 10.1109/TETCI.2018.2880511.
[12] M. Afrasiabi, M. Mohammadi, M. Rastegar, L. Stankovic, S. Afrasiabi, and M. Khazaei, “Deep-Based Conditional Probability Density Function Forecasting of Residential Loads,” IEEE Trans. Smart Grid, vol. 11, no. 4, pp. 3646–3657, Jul. 2020, doi: 10.1109/TSG.2020.2972513.
[13] L. Alfieri and P. De Falco, “Wavelet-Based Decompositions in Probabilistic Load Forecasting,” IEEE Trans. Smart Grid, vol. 11, no. 2, pp. 1367–1376, Mar. 2020, doi: 10.1109/TSG.2019.2937072.
[14] Z. Cao, C. Wan, Z. Zhang, F. Li, and Y. Song, “Hybrid Ensemble Deep Learning for Deterministic and Probabilistic Low-Voltage Load Forecasting,” IEEE Trans. Power Syst., vol. 35, no. 3, pp. 1881–1897, May 2020, doi: 10.1109/TPWRS.2019.2946701.
[15] A. Sherstinsky, “Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network,” Phys. D Nonlinear Phenom., vol. 404, p. 132306, Mar. 2020, doi: 10.1016/j.physd.2019.132306.
[16] N. Hikmah and A. Khumaidi, “Rancang Bangun Prototipe Pengusir Hama Burung Menggunakan Sensor Gerak RCWL Microwave Berbasis Internet of Things,” Simetris J. Tek. Mesin, Elektro dan Ilmu Komput., vol. 11, no. 2, pp. 560–567, Oct. 2021, doi: 10.24176/simet.v11i2.5071.
[17] A. Khumaidi, R. Raafi’udin, and I. P. Solihin, “Pengujian Algoritma Long Short Term Memory untuk Prediksi Kualitas Udara dan Suhu Kota Bandung,” Telematika, vol. 15, no. 1, pp. 13–18, 2020.
[18] A. Saxena, T. Sukumar, and T. Nadu, “Predicting bitcoin price using LSTM and Compare its predictability with Arima model,” Int. J. Pure Appl. Math., vol. 119, no. 17, pp. 2591–2600, 2020, doi: 10.13140/RG.2.2.15847.57766.
[19] Z. Zhao, W. Chen, X. Wu, P. C. Y. Chen, and J. Liu, “LSTM network: a deep learning approach for short?term traffic forecast,” IET Intell. Transp. Syst., vol. 11, no. 2, pp. 68–75, Mar. 2017, doi: 10.1049/iet-its.2016.0208.
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