Low carbon energy industry and network economy prediction based on sensors and real-time data processing

Abstract

The widespread use of sensors provides a large amount of real-time data for enterprises and decision-makers, providing more accurate information support for the prediction and decision-making of the network economy. With the help of Internet of Things technology, the data collected by sensors is transmitted in real time to data centers or cloud platforms. Real time data processing technology is used to clean, denoise, and analyze the data in real time, ensuring the accuracy and timeliness of the data. Perform pattern recognition and trend analysis on historical data, discover hidden patterns and correlations in the data, construct predictive and decision-making models to predict future economic trends and make reasonable decisions, continuously optimize and adjust the model to adapt to real-time data changes and dynamic changes in the economic environment, and improve the accuracy and efficiency of the model. The experimental results show that the network economy prediction and decision-making model based on sensor networks and Internet of Things technology can more accurately predict economic development trends, improve decision-making efficiency and accuracy. The large amount of data provided by sensor networks provides sufficient support for the construction and optimization of models.

Keywords

• Sensor
• Real-time data processing
• Low-carbon energy industry
• Network economic forecasting