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

Mustafa Wassef Hasan
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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.
基于改进型长短期记忆(LSTM)的住宅能耗预测物联网模型设计
长短期记忆(LSTM)网络是预测能源消耗周期时间序列数据的关键,因为许多其他预测方法没有考虑到周期性。尽管LSTM网络在预测周期性能源消耗数据方面具有有效的预测能力,但由于s型和双曲正切激活函数引起的死区效应阻碍了LSTM网络的预测。这些功能控制信息流,并确定哪些数据适合在特定的边界内更新和学习,但它们也会创建未使用的区域,从而影响LSTM网络学习过程的准确性和效率。为了解决这一问题,本研究引入了一种基于改进长短期记忆(ILSTM)方法的物联网(IoT)能耗预测模型。该模型旨在克服传统LSTM网络的死区问题,提高网络的学习精度和学习速度。该研究使用CT (SCT-013-030)传感器和ESP8266 NodeMCU真实模型,并使用Thingspek云平台进行数据处理,收集了住宅楼的实际能耗数据。此外,利用存储数据回收(SDR)技术来解决数据聚类不足和填补缺失信息。采用平均绝对误差(MAE)、均方误差(MSE)和均方根误差(RMSE)等多种评价指标对ILSTM预测模型进行评价。此外,还将提出的ILSTM预测模型的吞吐量、延迟和账单信息与ARIMA、DBN回归和传统LSTM (CLSTM)预测模型进行了比较。评估结果表明,ILSTM网络优于CLSTM网络,MAE提高了61.697%,MSE提高了59.248%,RMSE提高了50.537%。此外,与CLSTM相比,ILSTM网络对不同能耗数据的吞吐量值较低,并且与ARIMA、DBN Regression和CLSTM相比,延迟分别减少了40.1、21.1和13.5个周期。最后,结果表明,与CLSTM相比,ILSTM网络提供了更准确的能耗预测和账单估计。
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