A Big Data Approach for Demand Response Management in Smart Grid Using the Prophet Model

Abstract

Smart Grids (SG) generate extensive data sets regarding the system variables, viz., and demand and supply. These extremely large data sets are known as big data. Hence, preprocessing of this vast data and integration become critical steps in the load forecasting process. The precise prediction of the load is the primary concern while balancing the demand and supply in SG. Many techniques were devised for load forecasting using machine learning methods such as Deep-learning Models. However, in the case of large data sets, only a few models provide good performance, viz. Autoregressive Integrated Moving Average (ARIMA). However, this approach is complex, as it takes a minimum of 50 observations to make an evaluation. In this paper, the Prophet technique is used in the prediction of future demand response based on the past data, which is in the form of a time series. This technique is valid even if a few values in the time series are not available. Furthermore, the procedure is not affected by fluctuations, trends, and abnormal variations. The automatic model fitting approach is adopted for its effective performance. Further, ARIMA and Prophet model have been used to forecast and the approach is verified using various evaluation metrics. The demand response management was achieved and is being validated with two data sets. The results show the effectiveness of the Prophet model in the demand response management scheme involving large data sets.

Mushroom poisoning outbreaks in Guizhou Province, China: a prediction study using SARIMA and Prophet models

“…In most cases, the nonlinear structure should also be used for time series analysis because linear models do not produce adequate results. Nevertheless, Prophet models could compensate for this limitation 22 . Therefore, we constructed both the SARIMA and Prophet models, both of which targeted the time series data of mushroom poisoning events with both linear and nonlinear characteristics.…”