CEMS Demand Grouping for Correlated Facility Load Prediction
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Solution Overview
Problem
Existing systems face increased processing burdens when calculating the total amount of electric power demanded across multiple consumer facilities, as they do not adequately account for correlations between facilities, leading to inefficiencies in demand prediction.
Innovation Solution
A management server organizes consumer facilities into groups based on correlated electric power demands, using first and second communication systems to transmit power data at different intervals, allowing for efficient calculation of total demand and reducing processing loads.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the server receives demanded electric power from all consumer facilities individually, then the accuracy of total demand calculation is improved, but the processing burden increases
Solution Approach 1:
The patent segments consumer facilities into multiple groups based on their demand characteristics. Instead of processing all facilities individually, the server processes groups collectively. Each group's total demand is calculated by summing the demands of its constituent facilities, enabling parallel processing and reducing the overall computational burden while maintaining accurate total demand calculation.
Solution Approach 2:
The patent merges facilities with similar demand patterns into the same group. By combining facilities that exhibit correlated demand behavior, the system reduces the number of individual processing operations required. The server can calculate group-level totals more efficiently than processing each facility separately, thus reducing processing burden while preserving measurement accuracy.
2Reliability
If the server processes demand data from multiple consumer facilities, then the comprehensiveness of demand prediction is improved, but the processing time increases
Solution Approach 1:
The patent divides the set of consumer facilities into multiple groups, allowing the server to process demand data in parallel segments rather than sequentially processing all facilities. This segmentation enables the server to maintain comprehensive demand prediction by including all facilities while reducing total processing time through concurrent group-level calculations.
Solution Approach 2:
The patent performs preliminary grouping of facilities based on their demand characteristics before the actual demand calculation process. By pre-organizing facilities into groups with similar patterns, the system prepares the data structure in advance, enabling faster aggregation and reducing the time required for comprehensive demand prediction when actual demand data needs to be processed.
Data Source
AI summary
A CEMS server manages an amount of demanded electric power of consumer facilities organized into groups. The groups include at least two consumer facilities of which amounts of demanded electric power are correlated with each other. One of the at least two consumer facilities includes an HEMS server that transmits an amount of demanded electric power of the corresponding consumer facility to the CEMS server every first period. The CEMS server stores relationship information indicating a relationship between the amounts of demanded electric power of the at least two consumer facilities. The CEMS server calculates a total amount of demanded electric power of the groups based on the amount of demanded electric power transmitted from the HEMS server and the relationship information and outputs a DR request to the consumer facilities such that the total amount of demanded electric power matches a planned amount of electric power.