IoT Power Supply Regulation Using Consumption Forecasting
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The challenge of insufficient power supply in smart cities due to the mismatch between supply and demand, exacerbated by the green transformation and electrification, necessitates efficient and scientific power regulation strategies.
Innovation Solution
A method and system utilizing an Internet of Things platform to predict per capita living electricity consumption based on weather, time event, and economic development features, determining power supply strategies, and distributing electricity subsidies to promote energy savings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional power supply regulation methods are used, then implementation is simple, but power supply gaps and pressure cannot be effectively reduced
Solution Approach 1:
The system performs preliminary prediction of per capita living electricity consumption using weather features, time event features, and economic development features before actual power supply regulation. This advance prediction enables proactive power supply strategy formulation, allowing the system to prepare appropriate regulation measures before power supply gaps occur, thereby improving reliability without proportionally increasing complexity.
Solution Approach 2:
The system establishes a feedback loop where predicted consumption data informs power supply strategy adjustments. By continuously monitoring predicted per capita consumption and adjusting strategies accordingly, the system dynamically optimizes power supply regulation effectiveness, reducing power supply gaps through iterative improvement rather than static complex configurations.
2Measurement precision
If precise power supply prediction is implemented, then power supply strategies become accurate, but data processing complexity increases
Solution Approach 1:
The system segments the complex prediction task into distinct feature categories: weather features, time event features, and economic development features. Each feature type is processed and analyzed separately, then integrated to form the overall consumption prediction. This segmentation reduces data processing complexity by organizing information into manageable modules while maintaining high prediction accuracy.
Solution Approach 2:
The system transforms multiple types of input features (weather, time events, economic data) into standardized parameters suitable for consumption prediction. By converting diverse data sources into uniform parameter formats, the system simplifies the processing pipeline while preserving the precision needed for accurate per capita living electricity consumption prediction.
Data Source
AI summary
A method, an internet of things system and a storage medium for government power supply regulation in a smart city are provided. The method may be implemented by a government power supply regulation and management platform and include: obtaining weather features of a target area in a future time period, time event features of the target area in the future time period, and basic economic development features of the target area in a current time period; predicting, based on the features, per capita living electricity consumption of the target area in the future time period through an electricity consumption prediction model; and determining, based on the per capita living electricity consumption of the target area in the future time period, a power supply strategy of the target area in the future time period.