Which algorithm is commonly used for energy load forecasting in CPS architecture?

ARIMA, which stands for AutoRegressive Integrated Moving Average, is a statistical method that models time series data particularly well and is commonly used for forecasting purposes, including energy load forecasting in Cyber-Physical Systems (CPS) architecture.

One of the key strengths of the ARIMA model is its capability to handle the temporal dependencies inherent in time series data. Energy consumption patterns are often influenced by past consumption trends, seasonality, and cyclical behavior, which ARIMA models can effectively capture. This makes ARIMA suitable for predicting future energy loads, as it uses historical data to identify trends and make forecasts based on these patterns.

In the context of CPS, accurate energy load forecasting is crucial for optimizing energy management, enhancing efficiency, and ensuring reliability within the system. The flexibility of ARIMA to adapt to various time series characteristics enables it to perform effectively under diverse operational conditions typical of CPS architectures, thereby making it a preferred choice for energy load forecasting tasks.