Data Forecasting of Air-Conditioning Load in Large Shopping Malls Based on Multiple Nonlinear Regression

Abstract

This article applies multiple nonlinear regression methods to establish a forecasting model for the load characteristics of air conditioning in shopping malls at different times. Based on Python data, determine the functional relationship of refrigerant parameters concerning pressure and temperature. The article uses kernel smoothing estimation technology to calculate the room temperature probability density distribution of users participating in DLC to characterize the user's comfort. The article's research results show that the average error between the regression analysis results of refrigerant parameters and the reference value is within 1%. This model is suitable for medium and long-term load forecasting. It has high prediction accuracy for the sudden change trend with a turning point.

Impact of Information Security on National Digital Investment

“…The national security implications of foreign investments have become a significant concern, especially in the aftermath of events such as 9/11, underscoring the intricate relationship between economic security, terrorism, and national security (Hasnat, 2015). Moreover, the lack of audit information can influence securities investment decisions, emphasizing the need for robust analysis methods to understand the impact of missing audit information on investment decisions (Wang, 2023). The digitalization of agriculture and business processes in human resources management also plays a pivotal role in shaping national digital investment, highlighting the need for public investments and policies to facilitate data sharing, usability, and trust in digital products and services ("The digitalisation of agriculture", 2022; Odegov et al, 2019).…”