Saturated load forecasting based on clustering and logistic iterative regression

Multifeature-Based Variational Mode Decomposition–Temporal Convolutional Network–Long Short-Term Memory for Short-Term Forecasting of the Load of Port Power Systems

“…VMD uses iterative optimization to achieve frequency separation and concentration for each mode, effectively decomposing the signal and analyzing its different frequency characteristics. This decomposition [37] is accomplished by solving a constrained variational problem, as shown in Formula (10).…”

“…Jeong et al [9] demonstrated good accuracy in multivariate time series forecasting while utilizing the Vector Autoregressive (VAR) model to predict building electrical loads. By taking data analysis and selecting logistic regression as the basic model, Feng et al [10] proposed and developed a load forecasting method based on the combination of clustering and iterative logistic regression. Wu et al [11] proposed an improved regression model based on mini-batch stochastic gradient descent to address the issues of slow prediction speed and low prediction accuracy in regression analysis models.…”

Review of multiple load forecasting method for integrated energy system

“…The general steps are shown in Figure 5. In recent years, the traditional statistical analysis-based forecasting method has a more mature theoretical system, mainly using regression analysis (Wu et al, 2022;Feng et al, 2022;Nano et al, 2019) and time series (Ervural et al, 2016;Yu et al, 2019;Wu et al, 2020;Guefano et al, 2020). Their models are simple to calculate and easy to implement, but in the face of complex nonlinear load data, the forecasting effect is unstable and the forecasting accuracy cannot meet the research demand.…”

“…Experimental results show that the improved algorithm has significantly improved the forecasting speed than the traditional algorithm. In order to better load forecasting with the help of massive data (Feng et al, 2022), proposed a load forecasting method based on a combination of clustering and iterative logistic regression by taking data analysis as the entry point and choosing logistic regression method as the basic model (Nano et al, 2019). used "calendar" as an important influencing factor as an entry point and used multiple linear regression for load forecasting on different dates to test the feasibility and applicability of load forecasting on Indian calendar with two data sets.…”