Prediction of Electricity Usage in The Food and Beverage Department Using Recurrent Neural Network - Lukman Aditya; Wargijono Utomo; Ali Khumaidi; Rahmat Hidayat; Hudzaifah Al jihad (Institute of Computer Science (IOCS), 2021)
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.