Design of an IoT model for forecasting energy consumption of residential buildings based on improved long short-term memory (LSTM)

Abstract

Long short-term memory (LSTM) networks are critical in predicting periodic time series data on energy consumption, as many other forecasting methods do not take into account periodicity. Despite the effective forecasting capabilities of LSTM networks in predicting periodic energy consumption data, they are hindered by the dead region effect, which is caused by the sigmoid and hyperbolic tangent activation functions. These functions control the flow of information and determine which data is suitable for updating and learning within specific boundaries, but they also create unused regions that impact the accuracy and efficiency of the learning process in LSTM networks. To address this issue, this study introduces an Internet of Things (IoT) energy consumption forecasting model based on an improved long short-term memory (ILSTM) approach. This model aims to overcome the dead region problem and enhance the accuracy and learning process of traditional LSTM networks. The study collected actual energy consumption data from a residential building using a CT (SCT-013-030) sensor and ESP8266 NodeMCU real model with the Thingspek cloud platform for data processing. Additionally, a storage data recycling (SDR) technique is utilized to address data clustering shortages and fill missing information. The ILSTM forecasting model was assessed using various evaluation metrics including mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE). Additionally, comparisons were made between the throughput, latency, and bill information of the proposed ILSTM forecasting model and the ARIMA, DBN Regression, and conventional LSTM (CLSTM) forecasting models. The evaluation demonstrated that the ILSTM network outperformed the CLSTM network, showing improvements of 61.697% in MAE, 59.248% in MSE, and 50.537% in RMSE. Furthermore, the ILSTM network exhibited lower throughput values for varying energy consumption data compared to the CLSTM, and demonstrated reduced latency compared to ARIMA, DBN Regression, and CLSTM by 40.1, 21.1, and 13.5 cycles, respectively. Lastly, the results revealed that the ILSTM network provided more accurate energy consumption forecasting and bill estimation than the CLSTM.