Mengyuan Li, Wei Zhang, Hechuan Zhang
Smart power metering; Energy saving control; Big data and cloud computing; Load scheduling
This paper presents a user-oriented optimization research on intel- ligent power metering and energy-saving control systems, aiming to address challenges in precise data collection, real-time analysis, and environmental adaptability. A framework for intelligent power me- tering systems based on user behavior data analysis is designed, integrating smart meters and real-time monitoring technologies to achieve efficient power data collection and dynamic management. Big data and cloud computing technologies are introduced to enable distributed storage and real-time analysis of massive power data, enhancing system scalability and processing capabilities. Combined with artificial intelligence, a high-precision power demand forecasting model and a user-side dynamic load scheduling mechanism are de- veloped to optimize power resource allocation and adjust user power consumption behavior. Data analysis from the intelligent power me- tering system reveals that 20.8% of users consume 63.2% of total electricity during specific periods, highlighting significant impacts of user behavior on load. Additionally, 48% of users concentrate elec- tricity consumption during weekday daytime, while 73.5% show low nighttime usage, and 9.6% exhibit obvious consumption fluctuations affecting grid load balance. The proposed system demonstrates im- proved power efficiency and grid load balancing, offering practical and scalable solutions for smart grid applications.
Important Links:
Go Back
