Appling an Improved Method Based on ARIMA Model to Predict the Short‐Term Electricity Consumption Transmitted by the Internet of Things (IoT)

Abstract

The rapid development of the Internet of Things (IoT) has brought a data explosion and a new set of challenges. It has been an emergency to construct a more robust and precise model to predict the electricity consumption data collected from the Internet of Things (IoT). Accurately forecasting the electricity consumption is a crucial technology for the planning of the energy resource which could lead to remarkable conservation of the building electricity consumption. This paper is focused on the electricity consumption forecasting of an office building with a small-scale dataset, and 117 daily electricity consumption of the building are involved in the dataset, among which 89 values are selected as the training dataset and the remaining 28 values as the testing dataset. The hybrid model ARIMA (autoregression integrated moving average)-SVR (support vector regression) is proposed to predict the electricity consumption with different prediction horizons ranging from 1 day to 28 days. The model performances are assessed by three evaluation indicators, respectively, are the mean squared error (MSE), the root mean square error (RMSE), and the mean absolute percentage error (MAPE). The proposed model ARIMA-SVR is compared with the other four models, respectively, are the ARIMA, ARIMA-GBR (gradient boosting regression), LSTM (long short-term memory), and GRU (gated recurrent unit) models. The experiment result shows that the ARIMA-SVR model has lower prediction errors when the prediction horizon is within 20 days, and the ARIMA model is better when the prediction horizon is in the interval of 20 to 28 days. The provided method ARIMA-SVR has higher flexibility, and it is a great choice for electricity consumption prediction with more accurate results.

PFVAE: A Planar Flow-Based Variational Auto-Encoder Prediction Model for Time Series Data

“…In contrast, machine-learning methods with parameter self-learning and nonlinear adaptation are more suitable for time series data. The most widely used machine-learning methods include artificial neural network (ANN) [25], support vector regression (SVR) [26], integrated moving average autoregressive model (ARIMA) [27], etc. These models are easy to implement, but they cannot fit complex nonlinear relationships due to insufficient parameters [28,29].…”

A Survey of AI Techniques in IoT Applications with Use Case Investigations in the Smart Environmental Monitoring and Analytics in Real-Time IoT Platform

“…Several papers discussed use cases that provide the potential for using predictive analytics techniques to improve IoT application systems. They include applications in smart environments [18][19][20][21][22], smart manufacturing [23,24], smart home [25], smart building [26], smart healthcare [27,28], smart farming [29], and smart agriculture [30].…”

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